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Organisational climate and its influence upon performance:A study of Australian hotels in South East Queensland
Michael Cameron Gordon DavidsonB.A., M.Ed.Admin., Cert. Ed., Diploma Hotel and Catering
Management
Faculty of Commerce and ManagementSchool of Marketing and Management
Griffith University
Submitted in fulfillment of the requirements of the degree ofDoctor of Philosophy
August 2000
I
Abstract
This study gathered data from 14 four to five-star hotels in South-East
Queensland, Australia, in an attempt to examine the nature and degree of influence
organisational climate has upon the performance of hotels. Employee perception of
customer satisfaction was studied both as an index of performance and as an intervening
variable between organisational climate and financial performance as indexed by
revenue per available room (REVPAR). The data provided a description of a young,
relatively gender balanced, well educated and trained work force which received
relatively low levels of financial remuneration and displayed very high levels of
turnover.
A new instrument was used to measure the dimensions of organisational climate
across the hotels. This instrument represented a modification of that presented by Ryder
and Southey (1990), which itself was a modification of the 145 item psychological
climate questionnaire of Jones and James (1979). The instrument represented a subset of
70 items of the Ryder and Southey instrument. Responses to all items within the
instrument were on a 7 point anchored scale. Principal components analysis (PCA)
produced results consistent with earlier versions of the instrument, which had been
reported elsewhere. This analysis described organisational climate within the sample to
be composed of 7 underlying dimensions; Leader facilitation and support, Professional
and organisational esprit, Conflict and ambiguity, Regulations, organisation and
pressure, Job variety, challenge and autonomy, Workgroup co-operation, friendliness
and warmth, and Job standards. These dimensions were judged to be consistent with
those reported earlier by Jones and James, and by Ryder and Southey. Poor support was
found for the first structural model that proposed that employee demographic variables
would affect organisational climate and that organisational climate would affect
customer satisfaction (although the latter link was quite strong). The most important
II
finding of the study was the support for a second structural model when it was found
that variation in the 7 dimensions of organisational climate accounted for 30% of the
variation in Employee Perception of Customer Satisfaction. Furthermore, that Employee
Perception of Customer Satisfaction accounted for 23% of the variation in REVPAR
between the hotels. Possible extensions of this study using direct measures of customer
satisfaction and expanding it to include hotels of different star ratings are discussed.
III
Table of Contents
ABSTRACT.................................................................................................................................................I
TABLE OF CONTENTS.........................................................................................................................III
LIST OF TABLES ...................................................................................................................................VI
LIST OF FIGURES ..............................................................................................................................VIII
STATEMENT OF ACKNOWLEDGEMENT.......................................................................................IX
1.0 INTRODUCTION ......................................................................................................................... 1
1.1 BACKGROUND TO THE RESEARCH................................................................................................... 11.2 RESEARCH PROBLEM ...................................................................................................................... 31.3 JUSTIFICATION OF THE RESEARCH................................................................................................... 41.4 METHOD ......................................................................................................................................... 81.5 ORGANISATIONAL AND OPERATIONAL STRUCTURE OF A HOTEL ................................................... 101.6 DEFINITIONS................................................................................................................................. 131.7 OUTLINE OF THE THESIS................................................................................................................ 14
2.0 LITERATURE REVIEW OF ORGANISATIONAL CLIMATE........................................... 17
2.1 AN INTRODUCTION TO ORGANISATIONAL CLIMATE LITERATURE .................................................. 172.2 EARLY FORMULATIONS OF THE CLIMATE CONSTRUCT .................................................................. 192.4 THE DISTINCTION BETWEEN CULTURE AND CLIMATE.................................................................... 232.5 DEVELOPMENT OF CLIMATE INSTRUMENTS .................................................................................. 252.6 DIMENSIONS OF ORGANISATIONAL CLIMATE ................................................................................ 272.7 A CRITIQUE OF CLIMATE THEORY ................................................................................................. 322.8 MEASUREMENT ISSUES OF THE MULTILEVEL CLIMATE CONSTRUCT.............................................. 332.9 ORGANISATIONAL CLIMATE AS A VARIABLE IN THEORY AND RESEARCH...................................... 392.10 ORGANISATIONAL CLIMATE AND MODELS OF ORGANISATIONAL FUNCTIONING............................ 432.11 CLIMATE, SERVICE QUALITY AND ORGANISATIONAL PERFORMANCE............................................ 482.12 UTILISATION OF THE CLIMATE CONSTRUCT WITHIN A SERVICE QUALITY PERSPECTIVE................. 512.13 CUSTOMER AND EMPLOYEE PERCEPTIONS OF CUSTOMER SATISFACTION ...................................... 602.14 CLIMATE AND INNOVATION. ......................................................................................................... 612.15 ORGANISATIONAL CLIMATE AND IMPLICATIONS FOR THE HOTEL INDUSTRY................................. 62
3.0 THEORETICAL MODELS AND HYPOTHESES.................................................................. 68
3.1 THE RESEARCH QUESTION............................................................................................................. 683.2 THE DIMENSIONS OF ORGANISATIONAL CLIMATE WITHIN THE HOTELS ......................................... 683.3 THE RELATIONSHIPS BETWEEN EMPLOYEE DEMOGRAPHIC VARIABLES, ORGANISATIONAL CLIMATE,
AND EMPLOYEE PERCEPTIONS OF CUSTOMER SATISFACTION......................................................... 723.4 THE RELATIONSHIPS BETWEEN THE DIMENSIONS OF ORGANISATIONAL CLIMATE, EMPLOYEE
PERCEPTIONS OF CUSTOMER SATISFACTION, AND PERFORMANCE OF HOTELS. .............................. 74
4.0 METHOD..................................................................................................................................... 76
4.1 INTRODUCTION ............................................................................................................................. 764.2 JUSTIFICATION FOR THE PARADIGM AND METHOD ........................................................................ 774.3 IDENTIFICATION AND RATIONALE FOR THE SAMPLE...................................................................... 794.4 GAINING CO-OPERATION OF THE HOTELS...................................................................................... 824.5 FORMULATION OF THE SURVEY INSTRUMENTS ............................................................................. 86
4.5.1 The organisational climate questionnaire.......................................................................... 864.5.2 Hotel profile and hotel manager's questionnaire ............................................................... 93
4.6 PERCEPTIONS OF CUSTOMER SATISFACTION MEASURE.................................................................. 954.7 ORGANISATIONAL PERFORMANCE�REVENUE PER AVAILABLE ROOM (REVPAR)........................ 96
4.7.1 Occupancy percentage ....................................................................................................... 974.7.2 AVERAGE DAILY ROOM RATE ....................................................................................................... 97
4.7.3 Revenue per Available Room (REVPAR) ........................................................................... 994.8 PILOT AND PRE-TESTING PROCEDURE ........................................................................................... 99
IV
4.9 ADMINISTRATION OF CLIMATE SURVEY...................................................................................... 1024.10 DATA COLLECTION AND SORTING PROCEDURES ......................................................................... 104
5.0 HOTEL GENERAL OPERATING STATISTICS AND STAFF DEMOGRAPHIC DATA..................................................................................................................................................... 106
5.1 INTRODUCTION ........................................................................................................................... 1065.2 ANALYTICAL PROCEDURES......................................................................................................... 1065.3 HOTEL LEVEL DATA.................................................................................................................... 1085.4 STAFF LEVEL DATA..................................................................................................................... 1185.5 SUMMARY AND DISCUSSION....................................................................................................... 142
6.0 THE DIMENSIONS OF ORGANISATIONAL CLIMATE IN 14 AUSTRALIAN HOTELS..................................................................................................................................................... 146
6.1 INTRODUCTION ........................................................................................................................... 1466.2 RELIABILITY ANALYSIS............................................................................................................... 146
6.2.1 Approaches to the estimation of reliability of a test instrument ....................................... 1466.2.2 Reliability analysis of responses to the 70 item version of the psychological climatequestionnaire.................................................................................................................................... 147
6.3 STATISTICAL TECHNIQUES TO IDENTIFY UNDERLYING DIMENSIONS IN A DATA MATRIX OFPARTICIPANT RESPONSES ............................................................................................................ 153
6.3.1 Factor analysis ................................................................................................................. 1536.3.2 Principal components analysis......................................................................................... 153
6.4 PRINCIPAL COMPONENTS ANALYSES OF ORGANISATIONAL CLIMATE DATA ................................ 1546.5 VARIABLES ENTERED INTO THE PCA......................................................................................... 1576.6 PROPORTION OF VARIANCE EXPLAINED BY PRINCIPAL COMPONENTS.......................................... 1576.7 ROTATED PRINCIPAL COMPONENT LOADINGS ............................................................................. 1586.8 INTERPRETATION OF MEANING OF THE PRINCIPAL COMPONENTS ................................................ 1636.9 VARIATION IN CLIMATE DIMENSIONS BETWEEN HOTELS............................................................. 166
6.9.1 Generating climate dimension sores ................................................................................ 1666.9.2 Comparison of Climate Dimensions between the 14 Hotels in the Study......................... 168
6.10 SUMMARY AND DISCUSSION....................................................................................................... 170
7.0 ANALYSES OF THE RELATIONSHIPS BETWEEN; EMPLOYEE DEMOGRAPHICVARIABLES, ORGANISATIONAL CLIMATE, CUSTOMER SATISFACTION, ANDREVPAR..................................................................................................................................... 177
7.1 OVERVIEW.................................................................................................................................. 1777.2 STATISTICAL ANALYSES AND MODELLING TECHNIQUES USED IN THIS CHAPTER......................... 178
7.2.1 Multiple linear regression ................................................................................................ 1787.2.2 Structural equation modelling.......................................................................................... 179
7.3 STRUCTURAL MODEL A: RELATIONSHIP BETWEEN DEMOGRAPHIC VARIABLES, ORGANISATIONALCLIMATE AND CUSTOMER SATISFACTION................................................................................... 181
7.3.1. Multiple linear regression analysis examining the relationship between employeedemographic variables and organisational climate proposed by structural model A...... 182
7.3.2 An examination of the relationship between organisational climate and employeeperception of customer satisfaction as proposed by Structural Model A. ........................ 184
7.3.3 Structural equation modeling ........................................................................................... 1867.3.4 Summary of analysis of Structural Model A..................................................................... 188
7.4 STRUCTURAL MODEL B: THE RELATIONSHIP BETWEEN ORGANISATIONAL CLIMATE, CUSTOMERSATISFACTION, AND REVPAR. .................................................................................................. 188
7.4.1 Multiple linear regression analysis examining the relationship between organisationalclimate dimensions and employee perception of customer satisfaction proposed byStructural Model B. .......................................................................................................... 190
7.4.2 An examination of the relationship between REVPAR and employee perception ofcustomer satisfaction as proposed by Structural Model B. .......................................... 192
7.4.3 Structural equation modeling ........................................................................................... 1937.4.4 Summary of analysis of Structural Model B..................................................................... 194
7.5 SUMMARY AND DISCUSSION ....................................................................................................... 194
8.0 GENERAL DISCUSSION AND CONCLUSIONS................................................................. 198
8.1 OVERVIEW OF STUDY.................................................................................................................. 1988.2 HOTEL OPERATING STATISTICS ................................................................................................... 1988.3 STAFF DEMOGRAPHIC DATA........................................................................................................ 1998.4 VARIATION IN STAFF DEMOGRAPHIC VARIABLES BETWEEN HOTELS ........................................... 201
V
8.5 THE MEASUREMENT OF ORGANISATIONAL CLIMATE WITHIN THE HOTELS OF THE SAMPLE......... 2028.6 TESTING STRUCTURAL MODEL A ............................................................................................... 2058.7 TESTING STRUCTURAL MODEL B ............................................................................................... 2078.8 IMPLICATIONS OF THE RESULT THAT STRUCTURAL EQUATION MODEL B IS SUPPORTED ............ 2088.9 THE VALIDITY OF MEASURES USED IN THIS STUDY...................................................................... 210
8.9.1 The index of financial performance REVPAR .................................................................. 2108.9.2 Organisational climate..................................................................................................... 2108.9.3 Customer satisfaction ....................................................................................................... 213
8.10 THE ISSUE OF MULTILEVEL VARIABLES AND THE INTERPRETATION OF RELATIONSHIPS .............. 2148.11 GENERALISING RESULTS............................................................................................................. 2178.12 FUTURE RESEARCH ..................................................................................................................... 2188.13 SUMMARY AND CONCLUSION...................................................................................................... 218
TABLE OF APPENDICES ................................................................................................................... 220
APPENDIX A ......................................................................................................................................... 221
ORGANISATIONAL CLIMATE QUESTIONNAIRE, EMPLOYEE DEMOGRAPHICS, ANDEMPLOYEE PERCEPTION OF OPERATIONS AND CUSTOMER SATISFACTION . 221
APPENDIX B ......................................................................................................................................... 231
HOTEL PROFILE INSTRUMENT..................................................................................................... 231
APPENDIX C ......................................................................................................................................... 233
HOTEL MANAGERS' DEMOGRAPHICS, OPERATION PERFORMANCE AND PERCEPTIONOF CUSTOMER SATISFACTION......................................................................................... 234
APPENDIX D ......................................................................................................................................... 240
STAFF DEMOGRAPHIC DATA AND CONTINGENCY TABLE ANALYSES ........................... 240
APPENDIX E ......................................................................................................................................... 253
RELIABILITY ANALYSIS AND ........................................................................................................ 253
PRINCIPAL COMPONENTS ANALYSIS OF EMPLOYEE ORGANISATIONAL CLIMATEDATA.......................................................................................................................................... 253
APPENDIX F.......................................................................................................................................... 282
MODEL TESTING................................................................................................................................ 282
PART 1 STRUCTURAL EQUATION MODEL A............................................................................ 283
PART 2 STRUCTURAL EQUATION MODEL B ............................................................................ 291
REFERENCES....................................................................................................................................... 300
VI
List of Tables
Table 2.1 Contrasting: Organisational culture and organisational climate .............24
Table 2.2 Comparison of climate dimensions across studies ..................................30
Table 2.3 Climate for service (in banks) .................................................................55
Table 2.4 Comparison of HRM climate dimensions ...............................................57
Table 4.1 70 items of the modified version of the PCQ used in this study and 35 the
�a priori� scales used by Jones and James (1979). ..................................89
Table 5.3.1 Hotel operational statistics ....................................................................109
Table 5.3.2 Rooms business mix ..............................................................................112
Table 5.3.3 Hotel revenue mix percentages and key employment percentages .......114
Table 5.3.4 Comments by hotels on what affected trading conditions.....................117
Table 5.4.1 Response rate for the survey of the 14 hotels........................................119
Table 5.4.2 Gender � employees and managers .......................................................120
Table 5.4.3 Gender of employees for each of the 14 hotels .....................................122
Table 5.4.4 Age profile of employees and managers ...............................................124
Table 5.4.5 Age profile of employees for each of the 14 hotels...............................125
Table 5.4.6 Educational level of employees and managers......................................126
Table 5.4.7 Educational level of employees for each of the 14 hotels .....................127
Table 5.4.8 Organisational tenure for employees and managers..............................128
Table 5.4.9 Organisational tenure for employees for each of the 14 hotels .............130
Table 5.4.10 Job tenure for employees and managers................................................131
Table 5.4.11 Job tenure for employees for each of the 14 hotels ...............................132
Table 5.4.12 Gross salary for employees and managers ............................................133
Table 5.4.13 Gross salary for employees of the 14 hotels..........................................134
Table 5.4.14 Mode of employment for employees.....................................................135
Table 5.4.15 Mode of employment for employees for the 14 hotels..........................136
Table 5.4.16 Hours worked by employees .................................................................137
Table 5.4.17 Hours worked by employees for the 14 hotels ......................................138
Table 5.4.18 Time since last training session for employees and managers ..............139
Table 5.4.19 Time since last training session for employees for the 14 hotels ..........141
Table 5.4.20 Employees and managers were asked do you need training?................142
Table 6.1 Statistics for each of the 70 items of the modified version of the
Psychological Climate Questionnaire entered into the reliability
analysis. .................................................................................................149
VII
Table 6.2 Percentage of variance explained by Principal Components with
Eigenvalues greater than 1.....................................................................158
Table 6.3 Primary Rotated Component loadings for items of the modified version
of the PCQ. Also included for comparison purposes are the factors upon
which those items loaded in the earlier studies of Jones and James (1979)
and Ryder and Southey (1990). .............................................................160
Table 6.4 Relationship between principal components (Factors) found in this study,
and those found by Jones and James (1979) and Ryder and Southey
(1990). The proportion of items falling on the corresponding factor in
each of the earlier studies is also indicated. ..........................................164
Table 6.5 Mean scores on climate dimensions and for the composite measure of
organisational climate across the 14 hotels in the study........................168
Table 6.6 Summary of results of 7 oneway ANOVAs. Each ANOVA compared
the 14 means of each of the hotels on one of the 7 dimensions of
organisational climate............................................................................169
Table 7.1 Regression coefficients and associated probabilities for multiple linear
regression using demographic variables to predict composite measure of
Organisational Climate. .........................................................................184
Table 7.2 Pearson r correlation coefficients examining the relationship between the
Composite Measure of Organisational Climate and Employee
Perceptions of Customer Satisfaction for each of the Hotels participating
in the study. ...........................................................................................185
Table 7.3 Mean Composite Measure of Organisational Climate, Mean Employee
Perception of Customer Satisfaction and REVPAR for each of the 14
Hotels.....................................................................................................186
Table 7.4 Goodness of fit and parsimony of fit indices for structural equation
analysis. .................................................................................................187
Table 7.5 Correlations between Employee Perception of Customer Satisfaction and
each of the 7 dimensions of Organisational Climate.............................190
Table 7.6 Regression coefficients and associated probabilities for Multiple Linear
Regression using Organisational Climate Dimensions to predict
Employee Perception of Customer Satisfaction. ...................................191
Table 7.7 Goodness of fit and parsimony of fit indices for structural equation
analysis. .................................................................................................193
VIII
List of Figures
Figure 1.1 General Hotel Organisational Chart ........................................................11
Figure 2.1 Climate Formation (from Ashforth, 1985) ..............................................41
Figure 2.2 Moran and Volkwein (1992) depiction of culture and climate ...............42
Figure 2.3 Jones and James 1976 Model Of Organisational Functioning ................45
Figure 2.4 A Model of Climate, Culture and Productivity (Adapted from Kopelman,
Brief And Guzzo 1990) ...........................................................................47
Figure 2.5 General Factor of Psychological Climate (Reproduced from James and
James (1989)) ..........................................................................................58
Figure 3.1 Organisational Climate Model A: The dimensions of organisational
climate from the study of Jones and James (1979)..................................70
Figure 3.2 Organisational Climate Model B: The dimensions of organisational
climate from the study of Ryder and Southey (1990). ............................71
Figure 3.3 Structural Model A ..................................................................................73
Figure 3.4 Structural Model B ..................................................................................75
Figure 6.1 Organisational Climate Model A: The dimensions of Organisational
Climate from the study of Jones and James (1979)...............................155
Figure 6.2 Organisational Climate Model B: The dimensions of Organisational
Climate from the study of Ryder and Southey (1990). .........................156
Figure 6.3 Organisational Climate Model C: The dimensions of Organisational
Climate of the 14 Hotels participating in the current study...................174
Figure 7.1 Structural Model A ................................................................................182
Figure 7.2 Structural Model B ................................................................................189
Figure 8.1 Structural Model A ................................................................................205
Figure 8.2 Structural Model B. ...............................................................................207
IX
Statement of Acknowledgement
In the preparation of this thesis I would like to acknowledge the assistance and
support of a number of people: Firstly, my supervisors, Dr Nils Timo for his constant
encouragement and support, Professor Peter Brosnan who has given detailed feedback
and advice, Professor Paul Ryder who helped set up the study and survey instrument,
and Dr David Kennedy for his advice.
Apart from my supervisors the author is also indebted to Dr Mark Manning for
his detailed advice and guidance on the statistical procedures and modelling used.
I would also like to thank Debbie Amsler for the work in data input, formatting
and presentation, Lorraine Hauser in assisting in the data collection phases and my
colleagues in the School of Tourism and Hotel Management for their support.
Finally, I must thank my wife, Rosalind, and my family for their encouragement
and support over all the stages of the study.
M.C.G. Davidson
August 2000
X
This work has not previously been submitted for a degree or diploma in any university.
To the best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made in the thesis
itself.
M.C.G. Davidson
August 2000
1
1.0 Introduction
1.1 Background to the research
The hospitality industry is rich in many traditions that the word hospitality itself
conjures up. The Macquarie Dictionary defines hospitality as:
the reception and entertainment of guests or strangers with liberality and
kindness.
The history of the hospitality industry can be traced back to the beginning of the
major civilisations and the need for people to travel, trade and communicate. Religion
played an early part as religious orders saw it as one of their responsibilities to provide
rest, food and shelter for travellers. Hospitality gradually moved from the responsibility
to host travellers for altruistic reasons to a commercial basis with �Inns� being set up at
major cross-roads and places of commerce and governance. The development process of
the travellers� inns took place in Europe, Asia Minor and Asia, and has continued over
the centuries. The advent of mass affordable travel in the last half of the 20th century has
meant that tourism and hospitality has become the major industry of the world (WTO,
1996).
Despite this huge growth it remains one of the least researched of the major
industries in the world today. This lack of basic research has been recognised not only
by the World Tourism Organisation (WTO) but also many individual governments
worldwide. In Australia the federal government has in 1997 provided funds for a Co-
operative Research Centre in Sustainable Tourism that is headquartered at Griffith
University�s Gold Coast campus. It should be noted that the word tourism encompasses
2
the hospitality industry that is seen as a major sector within the tourism industry as a
whole.
Integral to the concept of hospitality is the notion of service. For service in a
hospitality setting we must have a service delivery process, and that is provided by
employed staff. A predominant factor in the hospitality industry�s economic importance
is the number of people employed to provide the service. Current estimates by the
Bureau of Tourism Research (BTR) and the World Travel and Tourism Council
(WTTC), reported by the Tourism Forecasting Council (TFC, June 1997) put tourism�s
worth at 7 to 11.5 % of Australian jobs, and between 6 and 10.5 % of gross domestic
product. These estimates vary because of the methodology and approach taken as to
what precisely constitutes tourism and its attendant processes and segments such as
hospitality.
Hospitality as a service industry is provided within various physical structures
(hotels, motels, resorts, clubs, restaurants, etc.), and has a plethora of management
structures and ownership arrangements ranging from independent owner operators to
chain operator�s, e.g., Hilton. The hospitality industry, whilst not seen as a great user of
technology, nonetheless, is reliant upon fairly sophisticated computer equipment for
reservations, accounting and monitoring of energy consumption. Operational
management systems, marketing and finance vary in their sophistication depending
upon, principally, the size of the company. Most importantly it is the staff and
customers which have the biggest impact upon how the process of hospitality service is
carried out. The use of tacit skills, those that interpret the contextual framework and
acknowledge the shared perception of customer and staff member, are crucial to the
enhancement of the service experience (Lammont and Lucas, 1999). Of crucial
importance to the success of an enterprise is the employee perceptions of their
3
organisation as expressed through the concept of �organisational climate�, and the
employees� relationship with customers (Schneider, 1994; Francese, 1993; James &
James, 1989; Jones and James, 1976; Kopelman, Brief & Guzzo, 1990; Sinclair, 1996;
Price & Chen, 1993; Shea, 1996, and others).
Organisational climate, as represented by the aggregation of the perceptions of
individual employees within the organisation, has been the focus of considerable
empirical research that can be traced back to the work of Lewin, Lippitt and White
(1939). The large body of climate research - much of this has been included in the
literature review in chapter 2 - has been subjected to very considerable theoretical
debate. This debate concentrates on the methodological issue of how the construct of
climate can be translated into an indicator of organisational effectiveness. Schneider and
Bowen (1985) and Cole, Bacayan and White (1993) have provided evidence that a good
organisational climate does have a positive effect upon service outcomes and hence
improves organisational success.
1.2 Research problem
Many organisational climate studies have been conducted across a range of
industries. Yet no specific academic study of organisational climate in the hospitality
industry has been undertaken to ascertain what effect this construct has on performance.
Therefore, the research question to be addressed by the current research is:
What is the nature and degree of influence that organisational climate has
upon the performance level of organisations within the Australian hotel industry?
4
1.3 Justification of the research
The largest single item of operating expenditure for international four or five star
hotels is the cost of labour. By definition, such hotels (four or five stars) rely upon their
reputation for service and customer satisfaction to be profitable. The major resource
component in service delivery is the hotel employee, the deliverer of the service. It is,
therefore, crucial for operational managers to seek an understanding of the hotel
employees� perceptions of their jobs and satisfaction derived. The emphasis upon
employee motivation and satisfaction is within the broad management theoretical
framework of human relations theory. These theories were first espoused by such
theorists as Mayo and colleagues from the Chicago School in the United States and Trist
from the Tavistock Institute in the United Kingdom, who concentrated on the human
aspects of the work and production process (Mullins, 1996).
Information gathered through research on an organisation's employees can be
used as a basis for assessing how operational and strategic goals are to be achieved. This
information should also be utilised in the design of appropriate procedures and systems
that are needed to ensure an individual organisation, such as a hotel, is able to deliver
the service excellence expected by its customers.
Organisational climate surveys are an excellent tool to supply information about
employee perceptions and have been successfully used in a range of organisational
settings (many of which are discussed at some length in chapter 2). Although the hotel
industry represents the largest employment sector in the world's largest industry -
tourism (Olsen, 1996) - when organisational climate research is examined within this
industry, only one major international hotel chain regularly uses this research
methodology and the information gained as a management tool for improving its
5
management and operational systems. It is noteworthy that this company, Marriott
Hotels, was the only hotel company to be named in the Fortune Magazine�s top 100
American companies (Branch, 1999). Many individual enterprises within the tourism
industry carry out individual employee surveys but they are seen as one off - identify a
problem, fix it and carry on! Many of the companies have achieved success and
international reputations but what might have been, if they were more cognisant of their
employee perceptions?
The scope and importance of the tourism industry in terms of physical structures
such as hotels in Australia, particularly in Queensland, has seen dramatic growth over
the last 15 years. In the survey area of Southeast Queensland there were 5 international
four and five star hotels in the early to mid-1980�s, whereas today there are in excess of
40 (Queensland Tourist and Travel Corporation, 1997). This level of dramatic growth
has been seen in many other tourism destinations �that have been discovered� e.g., the
growth of the Spanish tourism industry in the 1950�s and 1960�s. Butler (1980) has
described this growth phenomenon in his �resort destination life cycle� that analyses the
development, consolidation, maturity and decline or re-invention of any tourism
destination.
Australia has had the additional impediment in its growth cycle of being
geographically isolated from even its nearest neighbour (Papua New Guinea) with the
majority of the Australian east-coast tourism destinations being at least 7 hours flying
time. Much of Australia�s initial tourism growth in the early 1980�s was based upon the
Asian economic expansion and in particular the Japanese �economic miracle� where an
increasingly affluent middle class saw Australia as an ideal alternative to the U.S. and
Europe. The flying time was less than many U.S. and European destinations and with a
6
unique flora and fauna, wonderful natural attractions, and suitable destinations with
appropriate accommodation, Australia became an international tourist destination.
The expansion of the Australian hotel industry saw a substantial increase in the
supply of hotel accommodation accounting for over $1 billion in capital investment
during 1997 - 1998 (Industry Commission, 1996). By the end of 1998, Australian hotels
provided 191,147 beds with an annual turnover of in excess of A$2 billion (Australian
Bureau of Statistics, 1998).
Whilst international visitors proved to be the catalyst for major hotel growth, it
must be remembered that most of the destinations were already in existence serving the
domestic tourism market providing a sound base for the growth. The inflow of Japanese
visitors and the huge strength of the Japanese economy, plus in Australia and certainly
in Queensland a very pro development stance taken by the government, was the catalyst
for large amounts of Japanese investment in hotels and tourism ventures. The actual
number of Japanese visitors to Australia grew from 100,000 in 1985 to 800,000 in 1995
(Tourism Forecasting Council - TFC, 1997).
Japan remains a major source of international visitors despite the recent Asian
economic crisis, and this is especially so for Queensland. The QTTC (1997) reported
that in the year ended August 1997, Queensland received 453,500 Japanese visitors,
31% of Australian international visitors. All other Asian countries accounted for another
28%.
The geographical area that has been surveyed for this study includes the Gold
Coast, Brisbane, the Sunshine Coast and Wide Bay. This area accounted for 6,017,000
(77.7%) of the total Queensland visitors and 25,239,000 (62.3%) of total Queensland
visitor nights in 1997 (QTTC, June 1998)
7
This level of significance of the hotel industry was not achieved by capital
investment alone. To run the many new establishments that were planned and built
during the 1980�s and early 1990�s required an enormous expansion in the training and
education systems to provide people with the skills to operate these new properties. The
Technical and Further Education (TAFE) system was expanded and, the then, Colleges
of Advanced Education offered higher education courses in the area of tourism and
hospitality. The skill shortage also meant that a considerable number of people were
recruited from overseas. This included many tourism and hospitality educators who
were required to staff the expanded training and education system (Davidson, 1991).
The overview presented above of Australian and Queensland tourism, very
briefly describes some of the activity that accompanied the expansion of the tourism and
hospitality industry in the 1980�s. The numbers of visitors reinforces the economic
importance that the industry has now assumed. In Butler�s life cycle model, a major
tourism area such as the Gold Coast can be said to have reached its maturity stage. If
stagnation and decline are not to be experienced, new markets, attractions, and levels of
professionalism will be needed for the re-invention process. It is in the area of
professionalism and operational performance that the study of employee attitudes and
perceptions can play a major role. Organisational climate is a management tool that, if
used appropriately, can identify how performance can be enhanced, and this is the
crucial issue for the Australian and Queensland hotel industry into the 21st century.
South East Queensland was selected for this study because it represented a
complete cross section of locations and styles of hotels, e.g., city-centre,
golf/sport/leisure resort, seaside resort, boutique resort, eco-resort and casino
complexes. The area is a major tourism destination for both domestic and international
8
visitors and is second only to Sydney in actual international visitor nights (QTTC, June
1998).
1.4 Method
Chapters 2 and 3 will provide a full explanation of the literature, research
method and statistical techniques used. The aim here is to provide only a brief overview
of the main research methods and research strategies.
The research has been carried out in the southern coastal fringe of Queensland
bordered by Wide Bay in the north and the Gold Coast in the south. All of the hotels
studied are of an international four to five star standard and cover a cross-section of
operations that includes business, resort, leisure, group and conference market. The data
was collected by a combination of visits, personal interviews, telephone interviews, 3
composite survey questionnaires and reference to secondary data sources for
confirmation of certain factual and performance indicators. Data collection took place in
the period from August 1997 to February 1998.
The data collection for the main research thrust of examining organisational
climate necessitates a fairly complex survey instrument that is capable of capturing
employee perceptions. Many theorists have worked in this area and a number of
underlying dimensions had been proposed. Among the dimensions proposed for
organisational climate are leadership facilitation and support; job variety, challenge
and autonomy; conflict and pressure; organisational planning to achieve workgroup
effectiveness; workgroup reputation; co-operation, friendliness and warmth; and
interdepartmental co-operation. Prominent in the process of identification of the
various organisational climate dimensions during the 1960�s and 1970�s were Kahn,
Wolfe, Quinn, Snoek and Rosenthal (1964); Taguiri (1966); Litwin and Stringer (1966);
9
Schneider and Bartlett (1968); Campbell, Dunnette, Lawler and Weick (1970); Pritchard
and Karasick (1973); James and Jones (1974); and Jones and James (1979).
For this study, the Jones and James (1979) organisational climate questionnaire,
developed originally for the use in the US Navy and subsequently called psychological
climate, was used as the base. This questionnaire has been used by a number of
researchers in different settings and has been proposed to be a reliable and valid
measure through a factor analysis over a range of settings. However, for use in the
hospitality setting there was a need to modify both the language and length. Details of
the modification of the instrument are provided in chapter 3.
Fourteen hotel properties in the study area took part in the project. In addition to
the organisational climate data of the modified Jones and James questionnaire, data
collected from the hotel staff sought their employment perceptions, demographic profile
and employment details. Staff were asked to complete an organisational performance
questionnaire that addressed their view on how the hotel was performing in a number of
key service areas.
Managers at each property were asked to complete an organisational
performance questionnaire. This survey instrument included financial performance
broken down into departmental areas, demographic information, employment detail and
a customer satisfaction rating. The last aspect of the data collection was confidential
property information for both financial and operational performance indicators which
either the general manager or the financial controller was asked to extract from the
audited accounts.
Each property was visited at least twice to explain the details of the research and
the method by which the questionnaires should be distributed, collected and returned. It
10
also provided an opportunity to deal with concerns raised by employees, departmental
managers and senior management.
Descriptive and inferential statistics were computed using the SPSS computer
package. Tabulations of frequencies were calculated to compare employee and staff
demographic data between hotels. An exploratory Factor Analysis (Principal
Components) was conducted on the responses to the modified organisational climate
questionnaire used in this study. This analysis enabled the comparisons of underlying
dimensions of the sample with organisational climate dimensions described elsewhere
using different versions of the instrument in Australia and overseas. Correlation and
Multiple Linear Regression were used to examine the relationship between a number of
predictor variables and hotel performance (as indexed by revenue per available room �
REVPAR). To test a number of models proposing explicit causal relationships between
variables, the AMOS structural equation modelling program was used.
1.5 Organisational and operational structure of a hotel
It is appropriate at this point to offer a brief overview of the typical
organisational and operational structure of a hotel in order to provide a context and
reference point for readers of this thesis. Much of the subsequent discussion and the
analysis can only be interpreted if some understanding of the industrial setting is known.
Whilst many would argue that there are as many organisational and operational
systems as there are hotels, nonetheless, there are certain generic similarities which can
be applied to all hotels. A simplified typical hotel organisational chart is provided using
the hierarchical model as an example. (See Figure 1.1)
11
These departments and areas of responsibility are typical of a large international
four or five star hotel of 200 rooms plus. Of course, the number of departments and the
naming can and does vary enormously according to the hotel, its location and style of
operations. The organisational chart does provide an indication of the scope of
General Manager
Directors of: Marketing - Food & Beverage � Rooms Division �
Finance - Engineering � Human Resources
Departments and Areas of Responsibility:
Dir. MarketingSalesMarketingP.R.PublicityStatisticsConference
Dir. F & BRestaurantsKitchenBarsRooms ServiceBanqueting
Dir. RoomsHousekeepingFront OfficeReservationsTelephonistsConciergeLaundryLinenCleaning
Dir. FinanceCashierControlStoresAccountsInventoryPayrollSecurity
Dir. Eng.MaintenanceLandscapeGardensDecorationSystems SupportOther (Sports)
Dir. HRMPersonnelRecruitmentTrainingIndustrial Relations
Figure 1.1 General Hotel Organisational Chart
operations that are carried out routinely by any large hotel. It can be readily seen that a
hotel is indeed a large and complex operation and as such is often seen as a microcosm
of a small community.
12
From an operational standpoint, the technological and human systems are
diverse and complex. Many large hotel companies seek to codify the operational
procedures. However, it is patently obvious that when there are 200 plus hotel
bedrooms, numerous food and beverage outlets, recreational and conference facilities;
all the departments listed; the staff to run the operation; and the customers that this is
indeed a complex operation.
Whilst standard operational procedures are used in many operations it is
impossible to fully codify what happens in a hotel. So how does a hotel deliver and
maintain a quality operation? It can only be achieved by training and giving employees
the power to make decisions on service actions, within laid down parameters, that
impact upon the guest. This view in supported by Peters (1997) who so aptly points out,
it is the empowerment of the staff that ensures a hotel can deliver the type of service
customers are looking for and increasingly demanding. The only way to deliver high
quality service is to ensure that staff have the appropriate training and are able to make
the required operational decisions.
If the Australian hotel industry can improve its reputation for service and
professionalism and thus be more attractive to both international and domestic visitors,
it will also have the opportunity to improve its profitability. However, this improvement
in reputation and professionalism of the industry as a whole must also be accompanied
by a commensurate increase in the employment image for the industry�s staff. Unless
this improvement takes place and the career path planning is significantly improved, it
will still not be seen as a worthwhile career choice for school leavers and individuals
being displaced in other more traditional manufacturing and agricultural industries. If
this is achieved the hotel industry will have �come of age� in Australia but that state will
13
not be attained without a far better understanding of the industry�s employees, which is
where organisational climate research can play a significant role.
1.6 Definitions
The definitions adopted by researchers are often not uniform. Therefore, this
section will outline the definitions used throughout the thesis.
Organisational Climate and Psychological Climate: a full definitional
discussion is provided in chapter 2. Organisational climate will be used for both
constructs. In essence, organisational climate is an individual attitude toward the
organisation and can be subject to change when circumstances change.
Organisational Culture is the framework that is engendered by the
organisational systems and beliefs. It is relatively slow to form but has a high degree of
permanency. In today�s management consultancy parlance, the notion of �Change
Management� is often thought of as cultural change. In many cases, in fact, it should be
organisational climate change. Again, a full discussion is provided in chapter 2.
Construct can be defined in varying degrees of specificity from narrow
concepts to more abstract and complex concepts. No matter what the level of its
specificity a construct cannot be directly measured but it needs to be approximated by a
range of indicators (Hair, Anderson, Tatham and Black, 1995).
Tourism Industry has almost as many definitions as it has parts. Broadly
speaking it encompasses any activity that businesses and/or governments engage in that
provide travel, accommodation, sustenance, recreational and leisure activities outside of
a person's home. Its economic flow-on effect is considerable in not only those directly
14
employed but in the many service based industries that also benefit from the tourism
dollar.
Hospitality Industry is a defined sector of the tourism industry that principally
concentrates upon services such as accommodation, food and beverage in all their
forms.
Organisational performance: for the purposes of this thesis, 2 principal
indicators of organisational performance will be used. Those indicators are customer
satisfaction as measured by the perceptions of the hotel employees, and revenue
generated per available bedroom for each of the hotels (REVPAR).
1.7 Outline of the thesis
This introduction chapter provides a general overview of the research question
and the aims of the study. It briefly deals with justification and methodology employed
as well as giving various definitions that have been used.
Chapter 2 is a detailed literature review that addresses the origins and research
traditions of organisational climate. It traces the various debates that have surrounded
organisational climate and how these link with the construct of culture. The effect that
climate has within an organisation and what the processes are that contribute to its
formation are examined. Various contentious issues amongst leading theorists, such as
the aggregation of climate dimensions and what climate actually measures, are analysed
and discussed in some depth.
The focus of this chapter examines climate and service quality, climate and
innovation and the implications of organisational climate for the performance of the
15
hotel industry. The question �Can climate be used a predictor of organisational
performance?� is also raised.
Chapter 3 firstly proposes causal models to test the relevance of organisational
climate as a predictor of customer satisfaction as measured by employee perceptions.
One model, labelled Structural Model A, uses demographic variables as predictors of
organisational climate. Another model, Structural Model B, uses the dimensions of
organisational climate to ascertain the veracity of an aggregated measure of
organisational climate as a predictor of organisational performance. A number of
hypotheses are posed based upon the theoretical models presented.
Chapter 4 then provides a fully detailed account of the research methodology,
the reasons that the various research strategies were selected, and the rationale for the
sample selection process. It also deals with the issue of why the particular survey
instrument was selected as being appropriate for the current study. Considerable detail
will be provided of the reasons for selection and the modifications from the original
instrument initially designed for US Navy personnel to one that is suitable for the hotel
industry.
Detail is provided of the pilot study that was undertaken to assist in the design of
the instruments used to obtain performance and financial data as well as demographic
and employment details. This chapter will also describe the administration and
collection process that was used for the survey of the hotels.
Chapter 5 will report the Hotel Level Data of the 14 hotels concentrating upon
the demographic data, general operational data, general operational statistics and the
ranking each hotel�s performance. The key performance indicator for the hotels is
interpreted in the context of the other operational and market data. The second part of
16
the chapter will examine the Staff Level Data for employees and managers. This will
concentrate upon the various demographic data including gender, age, educational level,
organisational tenure, job tenure, gross salary, mode of employment, hours worked,
training frequency, and training needs.
Chapter 6 provides an analysis of the dimensions of organisational climate using
the multivariate technique of Principal Components Analysis (PCA). It examines the
organisational climate diversion across the 14 hotels using a one way analysis of
variance (ANOVA).
Chapter 7 reports upon the relationships between the dimension of
organisational climate, customer satisfaction and the key performance indicator of
REVPAR. The data again fall into the 2 classifications of Hotel Level and Individual
Level. The analysis presented in this chapter is guided by the structural models and uses
the multivariate technique of structural equation modelling.
Chapter 8 will discuss the conclusions that can be drawn and addresses the issue
of the hypotheses and whether they have been supported or rejected by the data
collected. Finally, the results will be put into the context of the hotel industry for South
East Queensland and Australia addressing the questions of what implications the
research has for the industry and future research in this area.
17
2.0 Literature Review of Organisational Climate
2.1 An introduction to organisational climate literature
In this study organisational climate is defined as the following.
Organizational climate is a relatively enduring characteristic of an
organization which distinguishes it from other organizations: (a) and
embodies members collective perceptions about their organization with
respect to such dimensions as autonomy, trust, cohesiveness, support,
recognition, innovation, and fairness: (b) is produced by member
interaction; (c) serves as a basis for interpreting the situation; (d) reflects
the prevalent norms, values and attitudes of the organisations culture; and
(e) acts as a source of influence for shaping behavior. (Moran and
Volkwein, 1992, p. 2)
Although this is the definition used to guide this research, many researchers
have presented different definitions of organisational climate, and there has been some
confusion as to the manner in which organisational climate is distinct from the notion of
organisational culture. This chapter will, in part, provide a review of the evolution of
this definition of organisational climate and provide an explanation of its relationship to
the concept of organisational culture.
Not only is it important to clarify the construct of organisational climate, but it is
also important to understand its usefulness for the service industries as a possible tool in
seeking to improve the effectiveness and quality of their service provision. The
importance of climate for the hospitality industry has been highlighted by a number of
theorists including, Francese (1993) who examined the effect of climate in service
responsiveness; Meudell and Gadd (1994) who studied climate and culture in short life
organisations; and Vallen (1993) who was concerned about organisational climate and
service staff burnout. However, the investigation of these themes further becomes very
18
difficult when consensus on the definition of climate has proved elusive, and there are
many conceptual issues that need to be addressed.
Organisational climate has much to offer in terms of its ability to explain the
behaviour of people in the workplace. Ashforth (1985, p. 838) put forward the view that
�climate has the potential to facilitate a truly integrative science of organisational
behaviour�. Schneider later discussed climate in terms of:
the atmosphere that employees perceive is created in their organisations by
practices, procedures and rewards � Employees observe what happens to them
(and around them) and then draw conclusions about the organisation's priorities.
They then set their own priorities accordingly. (Schneider, 1994, p. 18)
Schneider, Brief and Guzzo (1996, p. 9) argue that �sustainable organisational
change is most assured when both the climate - what the organisations� members
experience - and the culture - what the organisations� members believe the organisation
values - change�. Other empirical studies have claimed that climate has a considerable
impact upon organisational effectiveness (Campion, Medsker & Higgs, 1993; Drexler,
1977; Franklin, 1975; Fredrickson, Jensen & Beaton, 1972; James & Jones, 1989;
Likert, 1961, 1967; Furnham & Drakeley, 1993; Lawler, Hall & Oldham, 1974; Kanter,
1983; Mudrack, 1989; Schneider, Brief & Guzzo, 1996; Schneider, Gunnarson & Niles
�Jolly, 1994, and others).
The role of climate is crucial in any organisational improvement process that
requires the implementation of a major organisational change, or innovation. Much of
the following review will be definitional. This is necessary for two reasons:
1) in the context of performance and quality management, the term climate
has been used loosely to the extent that the terms culture and climate have been used
interchangeably; and
19
2) the literature on climate itself contains multiple definitions, factors,
dimensions, research methods and aetiologies.
The review will examine the major theories and models that have formed the
basis of climate research.
2.2 Early formulations of the climate construct
The concept of climate can be traced back to the work of Lewin, Lippitt and
White (1939) and a work entitled �Patterns of aggressive behaviour in experimentally
created social climates� (Denison, 1996; Schneider, 1990). The Lewin et. al. (1939)
study investigated the relationship between leadership style and climate, a factor that
has remained central to the concept. Joyce and Slocum (1982) trace the concept back to
the studies of Koffka (1935) on �behaviour environment�; Lewin�s (1936) study on �life
space�; and Murray�s (1938) work on organisational climate.
Lewin�s concept of life space, has been explained by Krech and Crutchfield as:
the individual�s total conception of the worlds in which he exists ... It includes
his knowledge, beliefs and memories and his view of the past and future as well
as of the present; and it may include domains of life reached after mortal �death�
- heaven and hell paradise and purgatory. It is not, of course, the same as the
actual physical and social environments described by the outside observer. It is
what exists subjectively for the person. His life space may correspond in some
way with the actual external environment but it also deviates from them in
radical degree, and varies markedly from life spaces of other people. (Krech &
Crutchfield, 1961, p. 210)
In the understanding of the differences between culture and climate, Lewin�s
(1951) approach to climate was conceptualised by the relationship between individuals,
their social environment and how that is set in a framework. Lewin expressed this in
20
terms of the simple equation:
B = f (P.E.)
in which B= Behaviour, E = Environment, and P = the person
It is clear from Lewin�s equation that the concept of climate takes a
psychological approach, focussing upon the individual and seeking to understand the
cognitive processes and behaviour. Lewin�s conceptualisation of the theory provides the
underpinnings of many studies and approaches to climate research.
2.3 Three approaches to the climate construct
Following the seminal work of Lewin et. al. (1939), obtaining consensus as to
the definition of climate has been difficult as the climate construct is complex and many
different researchers have used the same terminology to mean different things to the
extent that providing a definitive description of climate has been likened to �nailing jello
to the wall� (Schneider, 1990, p. 1). Others have argued that if the use of the same term
to mean different things continues, climate research will �grind to a stop in an
assemblage of walled in hermits each mumbling to himself words in a private language
that only he can understand� (Boulding, cited in Glick, 1988, p. 133).
James and Jones (1974) conducted a major review of the theory and research on
organisational climate and identified climate in three separate ways that were not
mutually exclusive, (a) multiple measurement - organisational attribute approach, (b)
perceptual measurement � organisational attribute approach, and (c) the perceptual
measurement � individual attribute approach. In the multiple measurement
organisational approach they cite Forehand and Gilmer (1964) as defining
organisational climate as a
set of characteristics that describe an organization and that (a) distinguish the
organization from other organizations (b) are relatively enduring over time, and
21
(c) influence the behavior of people in the organization. (Forehand & Gilmer,
1964 p. 3621 cited in James & Jones , 1974)
The perceptual measurement organisational attribute approach seeks to define
climate in terms of individual perceptions of the organisation and it is these perceptions
that influence behaviour. James and Jones (1974) reported that the Campbell, Dunnette,
Lawler & Weick (1970) study which itself had synthesised Kahn, Wolfe, Quinn, Snoek
& Rosenthal (1964), Litwin & Stringer (1968) and Schneider & Bartlett (1968) had
proposed four organisational climate dimensions,
! Individual autonomy � based on the factors of individual responsibility, agent
independence, rules orientation and opportunities for exercising individual initiative.
! The degree of structure imposed upon the position � based on the factors of
structure, managerial structure and the closeness of supervision.
! Reward orientation � based upon the factors of reward, general satisfaction,
promotional-achievement orientation, and being profit minded and sales oriented.
! Consideration, warmth and support � based upon the factors of managerial support,
nurturing of subordinates, and warmth and support.
It must be remembered that such dimensions of climate are not always clearly
distinguishable from other variables that might fit into categories such as organisational
structure, process, system values and norms. The reliance upon perceptual measurement
may mean that organisational climate also includes some situational characteristics as
well as individual perceptual differences and attitudes. Whilst James and Jones (1974)
note considerable criticism of this approach, they reaffirm that there is both rational and
empirical evidence to support that which is being measured by the perceptual questions
are variables related to different levels of explanation.
22
In reviewing psychological climate as a set of perceptually based, psychological
attributes (rather than the conceptualised independent or structural variable) Jones and
James (1979) noted that the process reflected the developments that had occurred in the
conceptualisation of climate and the nature of its major influences. They propose that
psychological climate,
(a) refers to the individual�s cognitively based description of the situation; (b)
involves a psychological processing of specific perceptions into more abstract
depictions of the psychologically meaningful influences in the situation; (c)
tends to be closely related to situational characteristics that have relatively direct
and immediate ties to the individual experience; and (d) is multi-dimensional,
with a central core of dimensions that apply across a variety of situations
(though additional dimensions might be needed to better describe particular
situations. (Jones and James, 1979, p. 205)
However, Schneider and Hall (1972) describe climate as a global perception
held by individuals about their own organisational environment. Schneider and Snyder
(1975) further clarified the approach by defining climate as a summary perception
which individuals form of (or about) an organisation. For them it is a global impression
of the organisation. The global nature of organisational climate does not suggest that the
concept is uni-dimensional. Many different types of events, practices and procedures
may contribute to the global or summary perception individuals have of their
organisation.
Within the current study, organisational climate is conceptualised as a construct
created by the activities of the organisation. It is not the activities themselves, which is a
distinction that is not always clear in some of the earlier works. Schneider (1975)
refined the definition of climate to include meaningful apprehensions of order for the
perceiver that are based on the equivalent of psychological cues. Whilst this definition
has some common elements with James & Jones� (1974) and Jones & James� (1979)
23
constructs, its focus shows a movement from definitional issues toward a concern for
people and their view of climate and what impact it has for the organisation. It is a way
of apprehending order and a way of judging the appropriateness of behaviour.
2.4 The distinction between culture and climate
Trice and Beyer (1993) define culture in terms of what it is not. It is not climate,
which is measured with researcher-based data, whereas culture is measured by intense
data collection of an emic (contrastive) nature. Reflecting the concerns of both
Schneider (1990) and Glick (1988), Trice and Beyer state:
So many different variables have been subsumed under the climate concept by
various researchers that it overlaps with most constructs in organisational
behaviour as well as with structure, technology, formalisation and effectiveness
� The appeal of the climate construct was that it seemed to give the researchers
a way to combine a broad array of variables already studied into a single
omnibus concept that would simplify the process of characterising and
comparing the psychological environments. (1993, pp. 19-20)
The definition of culture put forward by Trice and Beyer (1993) noted that it has
many unique indicators like myths, symbols, rites and stories. Denison (1996) took what
he considered to be a more controversial view in arguing that it is not clear that culture
and climate are examining distinct organisational phenomena. However, the literature
refers to culture as being deeply rooted in the structure of an organisation and based
upon values, beliefs and assumptions held by the members. Climate, however, tends to
present social environments in relatively static terms measured by a broad set of
dimensions and can be considered as temporary and subject to a range of controls. Table
2.1 gives an outline of differences between the literatures using an epistemological
24
approach, the point of view taken, methodology used, temporal orientation, level of
analysis and the discipline area.
Table 2.1 Contrasting: Organisational culture and organisational climate
Contrasting: Organisational Culture and Organisational Climate
Research Perspective Cultural Literature Climate Literature
Epistemological Contextualised andidiographic
Comparative andnomothetic
ViewPoint Emic (native view) Etic (researcher's view)
Methodological Qualitative observation Quantitative data
Temporal Orientation Historical evolution Ahistorical snapshot
Level of Analysis Underlying values andassumptions
Surface levelmanifestations
Discipline Sociology Psychology
Source: Denison (1996, p. 625)
Culture studies were searching for that which is unique in each setting and used
qualitative methods whereas climate studies in contrast, used quantitative methods and
looked for factors that were generalisable across different settings. Many of the
difficulties that seem to have plagued researchers in the climate area can be traced to
this desire to find generalisable factors that are applicable to all environments, to the
extent that a multiplicity of dimensions, climate instruments and underlying theoretical
assumptions have been produced by various researchers. Denison summed up this
paradox thus,
Culture researchers were more concerned with the evolution of social systems
over time ... whereas climate researchers were generally less concerned with
evolution but more concerned with the impact that organisational systems have
on groups and individuals � Culture researchers argued for the importance of
25
deep underlying assumptions ... Climate researchers in contrast, typically placed
greater emphasis on organisational members perceptions of observable practices
and procedures that are closer to the surface of organisational life ... and
categorisation of these practices and perceptions into analytic dimensions
defined by the researchers. (Denison, 1996, pp. 621- 622).
2.5 Development of climate instruments
James and Jones (1976) developed the items for their questionnaire after an
extensive review of the literature. From the literature they identified 35 concepts related
to organisational climate. Eleven concepts related to job and role characteristics, eight
related to leadership characteristics, four to work-group characteristics and 12
comprised sub-system and organisational level characteristics. Many of these had been
shown to be internally consistent, psychologically meaningful measures of the work
environment. For each of these concepts, between two and seven items were generated.
This procedure produced a 145 item questionnaire. Responses to each individual item
consisted of a stem with a variable scaled response of either three or five. Thirty-five a
priori composite variables were produced by summing across the relevant item
responses.
This was done to support their choice of climate composites, as they called
them, and the individual question items or scales that comprised each composite. In
1989 James and James reported that the items and scales that comprised the dimensions
of climate that had shown factorial invariance were developed using interviews,
observations and literature reviews. They outlined a number of measures for the job or
role, leader orientation, workgroup environment and variables that relate to the overall
organisational climate.
Schneider argues that neither, interviews or questionnaires are necessarily
preferable to each other in collecting data, but are useful for different purposes. The
qualitative information yielded from interviews is particularly useful for providing
26
managers with �the precise practices and procedures that inhibit service delivery [for
instance] rather than merely identifying the fact that there are some inhibitory practices
and procedures� (1990, p. 404). This stands in the way of change agents dealing with the
manifestations of particular climates in particular settings. Low levels of supervisory
support, for instance, don�t reveal precisely what needs to be changed. Schneider
proposes that intermediate positions may be useful;
one alternative would be to have a survey that contained items assessing generic
themes that could be used across settings but for each setting the generic items
could be supplemented by tailor made items. The latter items would require
some in depth exploration of issues in a specific organization to identify the
ways in which generic concepts become manifest there. (Schneider, 1990 p. 404)
This discussion reaffirms that there is still much work to be done in the area of
developing appropriate climate instruments. Current instruments include Patterson,
Payne & West (1996) Business Organisation Climate Index that consists of 28 item
scales however only eight were used because of the length. Kozlowski & Doherty�s
(1989) instrument uses 55 measures consisting of 11 sub-scales that overlaps with Jones
& James (1979). Joyce & Slocum (1982) used the same measure as Pritchard &
Karasick (1973) with 10 dimensions that were factor analysed and reduced to six.
Drexler�s (1977) survey of operations that was based upon Taylor & Bowers (1972), a
composite of several other instruments. Likert�s (1967) profile of organisational
characteristics and Pritchard & Karsick (1973) instrument were both based upon
Campbell et al., (1970) using eleven of their original 22 measures. James and Jones
(1976) developed their psychological climate questionnaire (PCQ) which used 35 a
priori scales derived from the literature, to that point. This questionnaire was
administered to a large US Navy sample as discussed above and the results were then
factor analysed. The components that resulted were then compared to other samples to
derive the generalised dimensions.
27
Ryder and Southey (1990) used the James and Jones (1979) questionnaire as the
basis for their instrument which they applied to employees within a large public
building construction and maintenance authority in Australia. Modifications to the
original instrument were threefold, consisting of modifications to the wording, scaling,
and presentation format. Items were reworded to remove culturally specific
terminology, to enable the use of non-sexist language, and to make the items applicable,
non-military employees. Ryder and Southey judged the scaling of the original
instrument to be unsatisfactory. The original instrument employed between three and
five scaled responses that listed either descriptive attributes on a continuous scale, or
were presented in a Likert format. Ryder and Southey employed across all 144 items of
their questionnaire a consistent seven point anchored scale format. Again between two
and seven items were used to produce each of 35 composite climate variables. They
reported that the instrument, so presented, required less time to complete than did the
original Jones and James version.
2.6 Dimensions of organisational climate
The definitions and theoretical positions on climate have varied considerably
between the individual theorists. This has also been the case for the dimensions of
climate and its measurement. Denison (1996) argues that developing a universal set of
dimensions was often the central issue of the climate researchers so that comparative
studies could be made possible in different organisational settings. He compared this
approach to that of the culture research that used a post-modern perspective which
examined the qualitative aspects of individual social contexts where each culture that
was examined was seen as unique and was not expected to have generalisable qualities
which had become central to the climate research.
It is possible that the dependence on the use of climate surveys as the research
method of choice led those working in the climate area to seek generalisable qualities
across settings. Jones and James (1979) argued that one of the assumptions of the
28
climate literature is that a relatively limited number of dimensions could characterise a
wide cross-section of social settings.
Jones and James (1979) initially administered their 145 item instrument to a
large sample of 4315 US Navy personnel. An exploratory Principal Components
Analysis (PCA) produced a six factor (eigenvalues greater than unity) solution. Jones
and James labelled their factors as follows:
�Conflict and ambiguity�, which �reflected perceived conflict in organisational
goals and objectives, combined with ambiguity of organisational structure and
roles, a lack of interdepartmental cooperation, and poor communication from
management. Also included were poor planning, inefficient job design, a lack of
awareness of employee needs and problems, and a lack of fairness and
objectivity in the reward process.�
�Job challenge, importance and variety�, which �reflected a job perceived as
challenging, important to the Navy, whic involved a variety of duties, including
dealing with other people. The job was seen as providing autonomy and
feedback, and demanding high standards of quality and performance.�
�Leader facilitation and support�, which �reflected perceived leader behaviors
such as the extent to which the leader was seen as helping to accomplish work
goals by means of scheduling activities, planning, etc., as well as the extent to
which he was perceived as facilitating interpersonal relationships and providing
personal support.�
�Workgroup cooperation, friendliness, and warmth�, which �generally described
relationships among group members and their pride in the workgroup.�
�Professional and organisational esprit�, which �reflected perceived external
image and desirable growth potential offered by the job and by the Navy. Also
29
included were perceptions of an open atmosphere to express one�s feelings and
thoughts, confidence in the leader, and consistently applied organisational
policies, combined with nonconflicting roles expectations and reduced job
pressure.�
�Job standards�, which �reflected the degree to which the job was seen as having
rigid standards of quality and accuracy, combined with inadequate time,
manpower, training, and resources to complete the task.�
Jones and James applied their instrument to two other samples of health
managers and firemen. PCA analysis in both of these cases extracted 6 factors with
eigenvalues greater than unity. Analysis of the items on each factor, however, revealed
only 5 factors to be common across the three samples (Conflict and ambiguity, Job
challenge, importance and variety, Leader facilitation and support, Workgroup
cooperation, friendliness, and warmth, and Professional and organisational esprit).
Jones and James reviewed the comparability of the results found in their US
Navy sample and the findings of other similar studies. A number of the dimensions that
had been used in other studies could be related to their own findings as shown in Table
2.2.
The identification of dimensions was also the subject of a study by Campbell,
Dunnette, Lawler, and Weick (1970) when they reviewed the work of Litwin and
Stringer (1966), Schneider and Bartlett (1968), Taguiri (1966), and Kahn, Wolfe,
Quinn, Snoek, and Rosenthal (1964). Campbell et al. found four factors common to
each of these studies: (a) individual autonomy, (b) degree of structure imposed on the
position, (c) reward orientation, and (d) consideration, warmth and support. Whilst there
is no definitive agreement on climate dimensions there does appear to be some
30
Table 2.2 Comparison of Climate Dimensions across Studies
JONES AND JAMES (1979)WORKGROUP CO-OPERATIONFRIENDLINESS AND WARMTH
Meyer (1968)Thornton (1969)Friedlander & Margulis (1969)Pritchard & Karasick (1973)Lawler, Hall & Oldham. (1974)
Team SpiritDistant vs Close Working RelationshipIntimacySocial relationsFriendly-Unfriendly
JONES AND JAMES (1979) CONFLICT AND AMBIGUITY
Litwin & Stringer (1968)Schneider & Barlett (1968)Pritchard & Karasick (1973)Meyer (1968)Payne, Pheysey & Pugh (1971)
Waters, Roach & Batlis (1974)Thornton (1969)Campbell, Dunnette, Lawler & Weick(1970)Litwin & Stringer (1968)Pritchard & Karasick (1973)Schneider & Barlett (1968)
ConflictConflictConflictOrganisational ClarityNormative ControlEffective Organisational StructureEfficiency and Clarity of Purpose
StructureStructureStructureStructure
JONES AND JAMES (1979) LEADERSHIP, FACILITATION and SUPPORT
Schneider and Bartlett (1968)Campbell et al., (1970)
Waters, Roach & Batlis (1974)Friedlander & Margulis (1969)
Managerial SupportConsideration, Warmth and SupportClose, Impersonal supervision, and Employeecentered OrientationAloofness, Production Emphasis, Trust andConsideration (4 separate factors)
ADAPTED FROM JONES AND JAMES (1979)
commonality of organisational dimensions that can be measured by a number of
theorists and the debate continues over the narrowness of range used to describe
different work environments (Pritchard and Karasick, 1973; James and James, 1989;
James, James and Ashe, 1990; Schneider, 1975).
31
Ryder and Southey (1990) applied an exploratory PCA to the data they gathered
from their Australian sample using their modified version of the Jones and James (1979)
instrument. This procedure resulted in a 10 factor solution (using the criterion of the
corresponding eigenvalue being greater than unity). The authors report that of those 10
factors, only 6 were interpretable. The dimensions they so identified were:
�Leader Facilitation and Support�, with the leader providing support and
facilitating the accomplishment of work goal�s, facilitating interpersonal relationships,
being aware of employee needs and providing job feedback. It also encompasses
openness of expression and allows for upward interaction.
�Job Variety, Challenge and Esprit�, deals with not only job variety, challenge
and autonomy but professional, work group and organisational esprit de corps. It also
encompasses opportunities for growth and advancement, role ambiguity and efficiency
of job design.
�Conflict and Pressure�, deals with conflict in a role and between organisational
goals and objectives, job pressure, planning and co-ordination, and opportunities to deal
with others.
�Organisational Planning Openness�, describes planning and effectiveness, and
ambiguity of organisational structure. It also deals with job standards and importance,
the consistent application of organisational policies, and confidence and trust down.
�Workgroup Reputation, Co-operation, Friendliness and Warmth�, encompasses
precisely the concepts named in its title.
�Perceived Equity�, looks at interdepartmental co-operation, organisational
communication down, and the fairness and objectiveness of the reward process.
Ryder and Southey noted that the major dimensions of psychological climate are
32
stable and would provide a framework for future research. In their study they modified
the Jones and James (1979) questionnaire and reported improved measures of
reliabilities.
2.7 A critique of climate theory
Schneider (1975) criticised the whole idea of an omnibus theory of climate and
in particular the indiscriminate use of the term organisational climate. He proposed that
the term organisational climate be used only to refer to an area of research rather than to
a construct with a limited number of dimensions. From a review of early climate studies
Schneider concluded that some dependent variable had implicitly driven the research on
the climate construct. Many of the studies have looked at climate as a particular facet of
organisational life, rather than a general omnibus measure. These studies included
theorists such as, Lewin et al., (1939) who examined leadership style and social climate.
Fleishman (1953) whose investigation looked at the climate for leadership whereas
Argyris (1958) was concerned about the right type of climate, and McGregor (1960)
looked at climate from the leadership perspective. Litwin and Stringer (1968) were
studying a climate for motivation, Schneider and Bartlett (1968) were exploring the
climate for new employees and Taylor and Bowers (1973) dealt with creativity.
Schneider (1973) was concerned about psychological success whilst Renwick (1975)
looked at conflict resolution. Additionally, several subsequent studies could also have
fallen into this categorisation: Delbecq & Mills (1985) addressed innovation and
Schneider (1990) studied service.
Schneider (1975) argues that these theorists could have investigated the same set
of organisations because each of those climates could have existed side by side. It was a
theme repeated in later work;
Organisations may have many climates, including a climate for creativity, for
leadership, for safety, for achievement, and or for service. Any one research
33
effort probably can not focus on all of these but the effort should be clear about
its focus. (Schneider, Parkington and Buxton, 1980, p. 255)
Schneider believed that the salience of a particular dimension could only be
found in the context of a particular criterion of interest (1975). Schneider and Reichers
(1983) further reinforced this view by strongly advocating that examining organisational
climate without attaching a referent is meaningless.
Jones and James (1979) responded to Schneider�s criticism, by arguing that the
call for criterion based climate studies did not rule out the possibility that a relatively
small set of dimensions may still describe a wide range of environments. They
postulated that a particular dimension may be related to the same criteria under
consideration but be negatively related to another criterion and not related at all to
others. James and James (1989) argued for the concept of a generalisable psychological
climate (PCg), first developed by Lazarus (1982; 1984), as a general higher order factor
integrating the meanings behind the psychological climate of an organisation.
Stated simply people respond to work environments in terms of how they
perceive these environments, and a key substantive concern in perception is the
degree to which individuals perceive themselves as being personally benefited as
opposed to being personally harmed (hindered) by their environment. (James
and James, 1989, p. 748)
They found strong support for this notion in their research and demonstrated the
theorised relationship between the dimensions of climate as a generic concept and the
underlying factors or PCg that make up the individual dimensions.
2.8 Measurement issues of the multilevel climate construct
Psychologists explain the behaviour of people through the use of both
nomothetic (group) and idiographic (individual) means (Mullins, 1996). A fundamental
34
question inherent with organisational climate research is �What is the appropriate level
of analysis; the organisation, the department or subunit, the workgroup or the
individual?�. Many researchers have conceptualised climate as an individual and
psychological variable, however, the difficulty has been justifying the extrapolation of
results from one level of analysis, i.e. (the individual), to the broader context of the
workgroup, the department or to the total organisation (Guion, 1973). The cross level
interference problems together with the unit of analysis issue have been addressed by a
number of researchers (Glick, 1980, 1985; Glick and Roberts, 1984; Mossholder and
Bedeian, 1983). When Cameron (1983) discussed organisational effectiveness he also
confirmed that a major problem for these types of studies is the primary level of
analysis.
Guzzo (1982), Cummings (1983), Noord (1983) and Keely (1980) all use single
indicators that are extrapolated to assess the whole organisation. The extrapolation of
results from the individual level to the group level allows climate researchers to analyse
and draw conclusions about the running of the total organisation and for groups of
people within the organisation in terms of whatever effectiveness parameter is being
investigated. Generally researchers have sought to do this by calculating the average
(usually a mean) of results for a particular climate survey and then sought to discover
the extent to which the results mapped into the structure and effectiveness of the
organisation. There has been considerable discussion in the literature concerning the
extent to which this practice is justified and in what context (Patterson, Payne & West,
1996; Glick, 1988, James, Joyce & Slocum, 1988; Denison, 1996).
In Argyris (1958), Forehand and Gilmer (1964) and Litwin and Stringer (1968)
the unit of theory was focussed upon the organisation as the natural unit for climate
research. Another group of these earlier theorists concentrated upon group or sub-unit,
notably Hellriegal and Slocum (1974), Powell and Butterfield (1978) and Howe (1977).
More recently, James and Jones (1974) used the term psychological climate to embrace
both individual and, when aggregated, organisational level units of analysis, although
35
later they (e.g., James et al., 1988) tended to use the term organisational climate to refer
to these aggregated individual psychological climate scores.
Glick stated that organisational climate �is an organizational attribute that may
be estimated with a central tendency, but the central tendency is not the organization
itself� (1988, p. 135). Whilst there are inherent difficulties with the aggregating of data
sets and disagreements as to the dimensionality of organisational climate, i.e.,
dimensions of psychological climate may not be appropriate to organisational climate,
however, it can still be estimated by aggregating individual psychological climate
scores. Glick proposed that organisational climate as defined by James et. al. (1988) be
renamed �aggregate psychological climate�. His overall conception of the construct
regards climate as a broad class of organisational variables that are used to describe the
context for individual members within an organisation�s formal and informal policy and
procedures. The dimensions are as yet still not fully resolved and whilst climate is an
emergent organisational level process it cannot entirely be decomposed to individual
level cognition. Glick�s (1988) summation draws upon and supports the work of other
theorists such as Schneider & Reichers (1983), Powell & Butterfield (1978), Campbell,
Dunette, Lawler & Weick (1970) and Glick (1985).
The multiple level of units of theory is important because they may differ in
their empirical approach. Whereas the term organisational climate connotes an
organisational unit level of analysis, it does not refer to the individual, department or
workgroup. The debate on the unit of theory as being the organisation is strengthened
by the common practice of many researchers of using aggregation of psychological
climate (Gavin and Howe, 1975; James, 1982; Jones and James, 1979; Schneider,
1975). In discussing the units of theory, Glick (1985) makes the point that psychological
climate is very much linked to the organisational climate and that care needs to be taken
and that separate cross-level analyses should be used.
James and Jones (1976) discussed the difficulties inherent in using individuals�
36
perceptions of organisational situations as the basis for higher level analysis in some
depth. The concern that emerged from their work was that perceptually based data
carried the risk of reflecting individual characteristics rather than differences in the
situations being studied. When, for instance, an organisation hired certain kinds of
persons into a particular group, the results of the study could be skewed. The process of
aggregation, they argued, rested on a number of implicit assumptions:
The argument for aggregating perceptually based climate scores (i.e.,
psychological climate scores) appears to rest heavily on three basic assumptions:
first, that psychological climate scores describe perceived situations; second, that
individuals exposed to the same set of situational conditions will describe these
conditions in similar ways; and third, that aggregation will emphasize perceptual
similarities and minimize individual differences. (Jones and James, 1979, p. 206)
Mossholder and Bedeian (1982) defended the use of aggregated psychological
climate in assessing how individuals perceive an organisation. They postulated that
while it appears to require an organisational unit of analysis, the actual units of analyses
are both organisational because psychological climate represents individuals, in general,
and the results may also be aggregated.
Schneider and Reichers (1983) discussed how climates form and why
aggregation is a legitimate technique. They considered three approaches to the
formation of climate: the structural perspective; selection, attraction and attribution; and
social interactionism. The structural perspective sees as arising from the structural
characteristics of the organisation. The selection, attraction and attrition approach at
which individuals (based on the work of Bowers, 1973) create homogeneous
organisational membership and where there are similar climate perceptions among
individuals. Thirdly, social interactionism approach is where individuals check,
suspend, regroup and transform their own perceptions in the light of their interactions.
This approach seeks to explain differences in climate across workgroups in the same
37
organisation that are not explained by the other approaches.
In the debate between Glick (1985) and James, Joyce and Slocum (1988) there is
fundamental disagreement about the conceptualisation and measuring of organisational
climate using psychological climate. James et al., argue that psychological climate, with
its parsimonious set of dimensions and the scores obtained, does represent shared
meaning and perceptual agreement which can be aggregated to give an overall indicator
of organisational climate. They further point out that the basic unit of theory for
organisational climate (aggregated psychological climate) must be the individual
because �it is individuals, and not organizations, that cognize� (1988, p. 130). The
aggregation of climate is appropriate because of the shared assignment of meaning that
allows a higher order of analysis for groups, sub-systems and organisations. It provides
a mechanism for relating the construct of psychological climate at individual level of
analysis to another form of the construct at the group, subsystem or organisational level
yet the basic unit is psychological analysis. This is a crucial point for organisational
research as it allows researchers the possibility of using aggregated psychological
climate to describe organisations in psychological terms (James, 1982; Joyce & Slocum,
1979, 1984).
In the sampling process, within any organisation in order to use aggregated
psychological climate to predict organisational climate there is a need to ensure that all
members of the organisation, or a random stratified sub-sample of individuals covering
all positions, are represented. Without such sampling procedures in place, James et al.,
(1984) conclude that the use of aggregation is unjustified.
Patterson, Payne and West (1996) discuss the problems that Schneider and
Reichers (1983) faced where they could not account for differences that were found to
exist across workgroups within the same organisation. This follows similar results
found by James and Jones (1979) in their US Navy study and the variation Pritchard and
Karasick (1973) found across regions. Schneider and Reichers (1983) addressed this
38
problem using social interactionism, drawing on the work of Mead and Bulmer (1969)
in the area of symbolic interactionism, and suggested that climate perceptions were a
function of social interactions. As discussed above these social interactions can be
examined by looking at how people interpret meaning in the social context.
meaning (which includes perceptions, descriptions and evaluations) does not
reside in any particular thing in itself, nor does it reside in the individual
perceiver. Rather the meanings of things arise from the interactions among
people. The actions of others act to define an event or procedure for the focal
person. This is not meant to suggest that people simply apply the meanings
given to them by others. Rather, individuals check, suspend, regroup and
transform their own perceptions of events in light of the interactions they have
with others in the setting. (Schneider and Reichers, 1983, p. 30)
Ashforth (1985) discusses the interactionist perspective and highlights the
susceptibility of newcomers to influence outcomes in their desire to fit into a new
setting. Social comparison theory explains that individuals compare their beliefs to
others whom they perceive to be similar to them (for example, people in the same job).
Normative social influence and the stake that group members have in maintaining the
frame of reference of the prescribed behaviours, beliefs, and attitudes affect the
development of organisational cultures. Patterson, Payne and West (1996) argue that
these approaches should be seen as complementary rather than competing and that each
may be useful for examining the various stages of development of a climate. Patterson
et. al. (1996) rely upon the depiction by Ashforth of the aetiology (cause) for climates in
the explication of the results, which were inconclusive from the social interactionist
model perspective.
Another conceptual difficulty identified by Cameron (1983), is the reverse to
that described above, where an organisation�s effectiveness is measured by single
indicators of performance such as return on investment, overall performance rating and
turnover. When organisational climate is represented by the aggregation of scores from
39
individuals within the organisation, a score would (obviously) exist for each individual
and may be included in multivariate statistical analyses relating climate to other
characteristics such as employee demographic variables. Within such analyses, should
the researcher wish to also examine the relationship between these variables and a
single indicator of performance, then the researcher is necessarily limited to either
dealing with aggregate scores across individuals, or must assign, for each individual, a
score representing that single performance indicator. Both approaches have advantages
and disadvantages.
2.9 Organisational climate as a variable in theory and research
According to Schneider (1975), the basis of the climate function can be traced to
two different schools of psychology: Gestalt and Functionalism. The Gestalt school
argues that the perceiver has no choice but is actually driven to find order in the world.
Nature has order, and the perceiver has to find that order through the process of closure.
The closure principle suggests that given some limited amount of information to
which people ascribe order, the totality they may create represents more than the
simple sum of the limited information perceived ... Given a set of cues about the
world with some perceived relationship, i.e. there is sufficient information for
order to be perceived, a whole or total concept is formed. (Schneider, 1975, p.
448).
Mullins (1996) discusses Gestalt theory in terms of its instant and spontaneous
assumptions that we cannot stop ourselves making about our environment. Gestalt
theory also stresses the drive to behave on the basis of this apprehended order and in a
manner that suits the environment in which the perceivers finds themselves (Schneider,
1975; Kozlowski & Doherty, 1989). The earliest reported incident of the phenomenon
was detailed in the work of Lewin et. al. (1939). In their experimentally created social
climates they found that the behaviour of the boys in the study varied according to the
social climate created by their leaders; authoritarian, democratic or laissez faire.
40
Functionalism provides a framework in which individuals can seek order in their
environment. This allows them to function adaptively: they have a fundamental need to
seek information about the status of their behaviour in terms of the environment within
which they operate, �they seek information so that they can adapt to, or be in
homeostatic balance, with their environment� (Schneider, 1975, p. 450). Theorists such
as Frederickson, Jenson and Beaton (1972), Fleishman (1953), Litwin and Stringer
(1968) and Argyris (1957) support this view of Functionalism. And Schneider (1990)
refined his view of climate to include �a sense of imperative� for individuals.
Ashforth (1985) argues that a strong culture informs the climate of the
organisation in two ways: directly by telling the individual what is important in the
environment, and indirectly through its influence on the environment. Whereas climate
influences factors in the workgroup, the process of newcomer socialisation, symbolic
management and to a lesser extent the physical setting. The point for Ashforth is that
culture underpins these factors so that the assumptions and values of the organisation
(the culture) are behind the perceptions and inferences of the organisation (the climate)
and the behaviour of the members of the organisation. Ashforth�s conceptualisation of
the formation of climates and how it is based upon and is affected by an organisation�s
culture is displayed in Figure 2.1
41
CLIMATE FORMATION
ADAPTED FROM ASHFORTH (1985)
Figure 2.1 Climate Formation (from Ashforth, 1985)
When Moran and Volkwein (1992) examined the relationship between culture
and climate they saw an organisation�s climate as a specific portion of the overall
construct. They viewed climate as embedded into the overall construct of culture, which
STRONG CULTUREAssumptions and values
underpinPerceptions and inferences
Informs climate in two ways
Directly by telling theindividual what isimportant
Indirectly by its impact onthe environment
INFLUENCED BY
WORKGROUPFestinger (1954): Social comparison theory
Hamner and Organ (1978): norms and expectations,frame of reference, prescribed behaviours, sanctions
AFFECTNewcomer socialisation: desire forintegration, desire to reduce anxiety
SYMBOLIC MANAGEMENT
PHYSICAL SETTING
CLIMATE ENACTEDas a joint property of both the individual
and the organisation both macro and micro
42
was seen as larger and more abstract. As far as individual behaviour in the formation of
climate is concerned, both Moran and Volkwein (1992) and Ashforth (1985) saw the
contextualising of the psychological principles contained in the Gestalt and
Functionalist approaches to behaviour. Figure 2.2 depicts how Moran and Volkwein
conceptualised the relationship between climate and culture. They viewed culture as
being the invisible construct which guides and inform individual behaviour, in effect
setting an agenda from which climate can develop and where in their view it can have
some enduring quality.
BRIDGING CULTURE AND CLIMATE
ADAPTED FROM MORAN AND VOLKWEIN (1992)
Figure 2.2 Moran and Volkwein (1992) depiction of culture and climate
CULTURE: INVISIBLE
(Exists quite apart fromindividual variation)
Interacting Individuals(informed and constrained
by common culture)
Contingencies in the internaland external environment
CLIMATE: VISIBLE
Collective and Individual properties
OPERATES AT THE LEVEL OF ATTITUDES ANDVALUES. FORMS MORE QUICKLY, CHANGES MORE
RAPIDLY: (changes in key staff, budgetary cuts)
RELATIVELY ENDURING
CULTURE STOPS CLIMATE BEING ENTIRELYTRANSITORY OPERATES AT THE PRECONSCOUS,
SUBCONSCIOUS LEVEL
�Culture�.is the source of purposeful action and continuity fromwhich the more routine adaptive behaviour exhibited in the
organisation's climate derive their impetus.�
43
An alternative perspective on the nature of the climate construct in theory and
research is presented by Schneider (1975) describing climate to have been
conceptualised across studies in one of three ways - as a dependent, independent, and
intervening variable, which he considered were merely different vantage points. Those
theorists that took climate as a dependent variable, George & Bishop (1971), Payne,
Pheysey & Pugh (1971), Dietertly & Schneider (1974) and Lawler, Hall & Oldham
(1974) used the construct to analyse varying situations and procedures in a macro sense.
Secondly, the group that used climate as an independent variable, Lewin, Lippitt &
White (1939), Argyris (1957), Andrews (1967), Frederickson et. al. (1972) and
Pritchard & Karasick (1973) were concerned with interpreting of practices which
produce varying organisational climates. Thirdly, the final group used the construct as
an intervening variable, where climate was a pre-determined way of specifying types of
procedures that will lead members to view climate in a particular way (McGregor, 1960;
Likert, 1967; Hall & Schneider, 1973).
The view emerging from some theorists is that climate should be viewed as an
intervening variable that is psychological by nature and represents an individual�s social
interaction which is underpinned by the culture of the organisation (Ashforth, 1985;
Moran & Volkwein, 1992). Moran and Volkwein (1992) have examined the constructs
of climate and culture, tracing the theoretical antecedents, arguments and positions in an
attempt to demonstrate differences and also provide a link between the two constructs.
They have drawn upon work by Forehand and Gilmer (1964) and Pritchard and
Karasick (1973) in forming their definition of organisational climate presented in
section 2.1.
2.10 Organisational climate and models of organisational functioning
Although the debate over what organisational climate does and does not describe
has been ongoing from the time Lewin, Lippitt and White (1939) first utilised the
construct, an adequate and comprehensive theory of climate has been elusive.
44
James and Jones have provided a conceptualisation of �Organisational
Functioning� (Figure 2.3) that displays the role of organisational climate in relation to
the resultant job behaviours and ultimately the end result criteria in an integrated model
(1976, p. 96). Organisational climate is depicted as a situational variable along with
more objective factors such as organisational structure, systems and norms and
processes. These themselves are further broken down into a number of sub-systems. It is
the action of these situational variables that in turn produce the perceived psychological
climate and the perceived physical environment. There are a number of other causal
influences but the prime relationship of the perceived climate and physical environment
is with a range of individual characteristics such as attitudes, motivation, job
satisfaction, expectancy instrumentality and reward reference. Other individual
characteristics become moderating variables but the relationship with job behaviours
and performance and end result outcomes for the organisation is clearly shown.
45
Figure 2.3 Jones and James 1976 Model of Organisational Functioning
Source: Jones and James 1976 INDIVIDUALBEHAVIOURS AND
CRITERIA
EXTERNAL ENVIRONMENT
ORGANISATION
SUBSYSTEM AND GROUP
EXTERNALPHYSICAL
ENVIRONMENT
SOCIOCULTURALENVIRONMENT
ORGANISATIONALPHYSICAL ENVIRONMENT
ORGANISATIONALCONTEXT
ORGANISATIONALSTRUCTURE
ORGANISATIONALPROCESS
SUBSYSTEM ANDGROUP PHYSICAL
ENVIRONMENT
SUBSYSTEMAND GROUP CONTEXT
SUBSYSTEM ANDGROUP STRUCTURE
SUBSYSTEM ANDGROUP PROCESS
ORGANISATIONAL CLIMATE
SITUATION
PSYCHOLOGICALCLIMATE (PC)
PERCEIVED PHYSICAL
ENVIRONMENT(HABITABILITY)
ORGANISATIONALLYRELATED ATTITUDESAND MOTIVATION
JOB SATISFACTION
EXPECTANCYINSTRUMENTALITY
REWARD REFERENCE
INDIVIDUAL RESOURCESINTELLIGENCE ABILITIESPERSONALITYRACESOCIOECONOMICSTATUS
JOBBEHAVIOURS
ANDPERFORMANCE
ENDRESULT
CRITERIA
INTERVENING VARIABLESINDIVIDUAL
CHARACTERISTICS
ORGANISATIONALSYSTEMS AND NORMS
SUBSYSTEM ANDGROUP SYSTEMS AND
NORMS
46
Kopelman, Brief, and Guzzo (1990) also provide a linear model of
organisational functioning (Figure 2.4) that demonstrates the role of the culture and
climate as they are ultimately linked to organisational productivity. Kopelman et. al.�s
model starts with societal and organisational culture as setting the parameters of the
human resource practices. It is the HRM practices that in turn engender the
organisational climate, which produce the cognitive and affective states of individuals
(work motivation and job satisfaction). The aggregation of individual perceptions
determines the salient features of organisational behaviour and in sum make up the
organisational productivity. Although the criterion of interest in this case is
productivity, the model has utility for explanatory purposes with climate being depicted
as an intervening variable. This model uses the role of HRM practices of the
organisation as a situational variable that will ultimately affect the productivity of the
organisation. Kopelman et. al.�s (1990) description of organisational climate reflects
both individual and organisational characteristics. Similarly, salient organisational
behaviours such as attachment, performance and citizenship are seen as intervening
between the climate of the organisation and the ultimate outcomes. Attachment will
affect such factors as absenteeism and turnover, leading to an increase in training
separation and replacement costs. In a service industry the quality of the service
provision is also likely be affected.
Performance relates to the manner in which the formal requirements of the job
are attended to, and it is here that the citizenship or pre-social organisational behaviours
have an important role. These refer to �constructive or co-operative gestures that are not
mandatory� without which attachment performance and ultimately productivity will
slowly deteriorate (Brief and Motowidlo, cited in Kopelman et. al., 1990, p. 301).
Schneider, Gunnarson and Niles-Jolly (1994) claim that organisational citizenship
behaviour is essential in creating a climate that allows for organisational success.
Perceptions of fairness and trust, norms of helpfulness and co-operation and fair reward
systems based on a broad range of contributions are seen as essential in creating a good
climate.
A MODEL OF CLIMATE, CULTURE AND PRODUCTIVITY
ADAPTED FROM KOPELMAN, BRIEF AND GUZZO 1990
Figure 2.4 A Model of Climate, Culture and Productivity (Adapted From Kopelman, Brief And Guzzo 1990)
Human ResourceManagement Practices
HiringPlacingRewardingMonitoringDevelopingPromoting
SOCIETAL CULTURE
OrganisationalCulture
Organisational Climate
Goal EmphasisMeans EmphasisReward OrientationTask SupportSocioemotional Support
Cognitive and AffectiveStates
WorkMotivationJobSatisfaction
Salient OrganisationalBehaviours
AttachmentPerformanceCitizenship
OrganisationalProductivityPhysicalOutputTotal labour costs
48
Modelling the way climate affects the outcomes of the organisation through the
behaviour of the employees has its antecedents in the work of Likert (1961) who
discussed climate in terms of an intervening variable. The role of climate in the
provision of high quality service draws on the models provided by Likert (1961), James
and Jones (1976), Kopelman et al., (1990) and others. Likert�s model used causal
variables which included only those that were under direct management control;
intervening variables that reflected the organisational climate such as performance
goals, loyalties, attitudes, perceptions and motivation; and end result variables that are
dependent variables that include productivity measures, costs, service and quality.
2.11 Climate, service quality and organisational performance
From the earliest studies the climate of an organisation has been shown to exert
a powerful influence on the attitudes and behaviour of the people in the organisation.
Many aspects and factors have been shown to have a relationship with organisational
climate such as, work methods (Frederiksen, 1968, cited in James and Jones, 1974);
satisfaction (Pritchard and Karasick, 1973); alienation (Witt, 1993); trust (Strutton,
Toma and Pelton, 1993); productivity (Frederiksen, 1968, cited in James and Jones,
1974); turnover intentions (Parkington and Schneider, 1979); agency success (Schneider
cited in Pritchard and Karasick, 1973); organisational income (Scheflen cited in
Pritchard and Karasick, 1973); service quality (Schneider and Bowen, 1985); innovation
(Scott and Bruce, 1994) and many other factors. It is therefore reasonable to conclude
that organisational climate is of major importance in the understanding of how
organisations work and the success they achieve.
When discussing the role of climate and its links to the provision of high quality
service it is first necessary to understand the operational environment of the hospitality
industry. The provision of high quality service has become essential to survival, and
many hospitality organisations are attempting to implement various quality management
schemes (Harrington & Akehurst, 1996). Higgins and Vincze (1993) argue that firms
49
wishing to be successful in the 1990�s must have a quality management programme in
place and that quality has become a strategic imperative.
According to Partlow, the search for a sustainable competitive advantage in the
hotel industry �has become focussed to a large degree on product and service quality�
(1994, p. 16). The performance and credibility of hospitality organisations like the Ritz
Carlton, Hyatt, Sheraton, Pan Pacific Hotels, Southern Pacific Hotel Corporation and
others which have already successfully implemented a quality management systems is a
compelling justification for all other hospitality organisations also adopting similar
strategies. Hoque (2000) suggests that it is the linking of good HRM practices to the
business strategy that enhances performance.
Yet there is no guarantee that the introduction of quality programmes will lead
to success. Harari (1993) points to a success rate of only 20-30 %. Similarly, Eskildon
(1994) reported that 63 % of those surveyed with TQM programmes had failed to
reduce internal defects by 10 % or more. Only one-fifth of British firms believed that
their quality programmes had made a significant impact, and only one-third of US
manufacturing and service firms believed their TQM efforts had made them more
competitive. Morton (1994) put it even more succinctly when discussing why British
firms could not match their Japanese counterparts, quoting a Japanese executive when
asked, �why are you so open about your processes, the techniques and commercial
decisions?� The response was both realistic and depressing, �because we know you
won�t do it anyway. You will not change� (1994, p. XV).
Napier (1997) found that many North American organisations which start a
formal quality initiative lose their way or give up within two years, in the process
wasting a lot of time, effort and money. Most of the TQM programmes also fail to
address the issues of psychological/behavioural aspects that are essential prerequisites
for such change. Napier argues that they are focussed on the pure mechanics of
implementation so that without the �...supporting behaviours quality systems either get
50
bastardised to the stage that they are rendered useless or are simply doomed to failure�
(1997 p. 7). Eskildon (1994) argues that companies such as Harley-Davidson, Hewlett-
Packard, Xerox and Compaq have all achieved success by managing their TQM
programs by creating clear goals, whilst many other companies which implement TQM
concentrate on creating a culture, without creating clear goals for improving customer-
value outcomes.
Goals have an important function in understanding the link between the daily
activities of the organisation and the deeper psychological issues. It is here that climate
has an important explanatory role to play. Goals and how an organisation goes about its
business are key components by which the members of an organisation infer the climate
of an organisation (Schneider, Brief and Guzzo, 1996). The influence of an
organisations climate on employee behaviour extends beyond the implementation of
proposed change, and has been demonstrated by numerous studies on all aspects of
employee behaviour (Drory, 1993; Witt, 1993; Strutton, Toma & Pelton, 1993). An
organisation needs to be aware of three separate kinds of climate in order to ensure the
success of service focussed quality improvement efforts: (1) a climate for service, (2) a
climate for innovation, and (3) a climate for human resources or employee welfare
(Schneider, Gunnarson and Niles-Jolly, 1994).
The climate of the workplace is particularly relevant to all service industries
where, like hospitality, the vast majority of its output is characterised by intangibility,
heterogeneity, and simultaneous production and consumption. This is perhaps best
described by Carlzon�s �moment of truth� (1987) in the service encounter recognising
that it is impossible to directly control the outcome of the service process. The
management of service industries is different and according to Schneider, Gunnarson
and Niles-Jolly (1994, p. 23) �...in the absence of direct control of the service encounter,
it is the climate and culture that determines high quality service�. In many ways climate
becomes a substitute for leadership, and understanding how it works is vital to those in
51
the hospitality industry (Kerr and Jermier, 1978).
2.12 Utilisation of the climate construct within a service quality perspective
The integral role of people in the development of a TQM plan (Price and Chen,
1993) is crucial to its implementation and success. Crom and France (1996) detail the
consequences of �a climate of fear� in relation to employee risk taking and how a variety
of techniques including teamwork and the redesign of job processes can address this
situation. Ryan (1995) discusses the need for the development of a climate for
innovation in the context of a continuous improvement effort. Silcox, Cacioppe and
Soutar (1996) concentrated on the way that various sub-cultures may be managed
during any intervention. They recognise that cultural change for a large group has
considerable difficulties, whereas the efficacy of small group cultural change is an
alternative and more productive approach stating,
The commitment and self esteem of workers, the culture and climate of the
organisation, together with the quality of the organisational communication and
leadership have a direct effect on the quality of products and services and the
overall productivity of the organisation and need to be examined and understood
by managers considering a quality intervention. (Silcox, Cacioppe and Soutar,
1996, p. 26)
Tice (1993) supports this view claiming that �all too often the human, or
behavioural side of TQM is either ignored altogether or given cursory attention� (1993,
p. 22). Libotte discusses the difficulty of �embedding the will for continuous
improvement� (1995, p. 48) and promotes the prescription of measurable results as well
as new roles, and responsibilities in place of the use of new written procedures only.
Easton (1992) comments upon the continued emphasis on financial and cost factors in
the decision making of American industry to the neglect of other indicators concerning
individuals. Heymann (1992) extends the discussion of the need to establish a quality
culture to include the day-to-day behaviour that is evident in the organisation, and
52
Saraph and Sebastian (1993) discuss the need for quality goal setting. Partlow (1993)
gives an extensive account of the practices and procedures that are seen to be central to
the quality improvement process at the Ritz-Carlton group. These processes cover
climate-related issues and give a clear indication of the importance of the role
organisational climate plays in the quality management initiative.
Vallen (1993) provided clear evidence of the link between organisational climate
and the burnout of service staff. The study used Likert�s �Profile of Organisational
Characteristics (POC)�, an 18 item questionnaire divided into six categories, leadership;
communication; interaction and influence; decision making, goal setting and control.
This is based upon Likert�s (1961) four Systems of Management, ranging from System
1 (exploitative-authoritative), System 2 (benevolent � authoritative), System 3
(consultative) through to System 4 (participative). Apart from the strong correlation
between burnout and a poor organisational climate, Vallen also noted that the
hospitality firms surveyed in terms of their climate rarely used a consultative style. His
research showed that service jobs with a high degree of customer interaction have a
higher level of burnout. This burnout has been defined by Riggar as being displayed by
the characteristics of �turnover, absenteeism, lowered productivity, psychological
problems, etc.� (Riggar, 1985 in Vallen, 1993, p. 55). Vallen�s study concluded that
high emotional exhaustion and depersonalisation scores (high burnout) correlate with
low POC scores in highly autocratic organisations. He recommended hospitality
managers seeking to reduce their staff turnover should look to their organisational
climate as it undoubtedly affects the ability of a hospitality organisation to deliver
service quality. This is supported by the findings of Kordupleski, Rust and Zahorik
(1993) who found that of the overall quality processes used in service industries was
responsible for 70 % of the variation in an organisation�s output. The processes such as
quality programmes can only be successful when there is genuine staff and management
commitment. Meudell and Gadd (1994) also reported similar results, finding that 6% of
individuals in an organisation felt the process of managing people was good and only
4% thought that management had displayed a positive attitude towards them.
53
The issue of management in organisations taking insufficient account of the
individual behavioural perceptions and actions is a constant theme within the service
quality literature. Building a responsive service climate was the focus of a study by
Francese (1993), �previous research has shown that both customers and managers agree
that a responsive service climate is the key to service quality and customer satisfaction�
(p. 55). Drawing on the work of Schneider and Bowen (1985) and Shoorman and
Schneider (1988), Francese proposed a model for service organisations using a support
dimension, a managerial dimension and an adaptive dimension correlating to a
relationship of service responsiveness and service quality. She found a clear link
between teamwork, entrepreneurial management behaviour and adaptive marketing
policies and activities. As such, her results reveal the link between the areas service
quality and responsiveness and organisational climate.
The above studies support theorists such as Schneider (1973) who explored the
relationship between climate and service related issues. Initially, Schneider examined
the relationship between service climate (and other more tangible factors) and customer
intentions to switch their accounts to another bank. He found that none of the objective
indexes of customer participation (size of bank balance, length of time as a bank
customer) was related to switching intentions but found that switching behaviour was
strongly related to climate perceptions. This study revealed that the measure of the
atmosphere in the bank �warm and friendly� was most strongly correlated with
switching intentions. As such this supports the underlying assumption �that the climate
bank employees create for customers is an extension of the climate bank management
creates for employees� (Schneider, 1973 p. 255). Customer retention has been clearly
linked to the climate created for the employees of the organisation.
Ross (1995) in a study of the hotel industry in North Queensland found that
there were major divergences between management and employee service quality
ideals. Following Parkington and Schneider�s (1979) reasoning, this would appear to
have negative consequences for the quality of service offerings of the hotels concerned.
54
These issues are highly relevant for service managers in their the day to day operations.
Parkington and Schneider (1979) argue that it is possible to get operational staff
enthusiastic using the usual management tools.
Through alterations of policies and procedures and goals it may be possible for
management to effect changes in the degree to which there is emphasis on an
enthusiastic service orientation more similar to that of boundary personnel. This
should reduce the levels of role stress and the levels of negative employee
outcomes. (1979, p. 279).
It is imperative that the quality of the service offerings should improve in service
organisations. Organisational dynamics have a direct impact on the people the
organisation serves, as well as on employee performance and attitudes. Schneider et. al.
(1980) were explicit as to the implication this had for service management, arguing that
consumers are better served if the policies, practices and procedures of an organisation
meet the needs of, and satisfy, employees which results in a directly positive outcome in
terms of service quality for the consumer. The creation of a climate for service is an
example of organisational effectiveness of an organisation being responsive to its
environment, in this case its customers. The relationship between a climate for service
and the service quality perceptions of the customers is clearly supported by
the ways in which branch employees describe some facets of the service
orientation of their branch and the support received from some systems outside
the branch are related to what customers say about the quality of the service they
receive in the branch. (Schneider et. al. 1980, p. 262)
In a study of the banking industry Schneider and Bowen (1985) isolated 10
dimensions of a climate for service, many of which showed a significant correlation
with customer perceptions of service quality in the organisation. They are part of the
overall climate framework and link with the climate dimensions identified by James and
Jones (1979) and others. The service dimensions are shown in Table 2.3 together with
the employee perceptions of each dimension.
55
Table 2.3 Climate for service (in banks)
Service Climate Dimensions
Bureaucratic orientation to service
Enthusiastic orientation to service
Managerial behaviour
Service rewards
Customer retention
Personnel support
Operations support
Marketing support
Equipment/supply and support
Employee Perceptions
Following all rules & proceduresDoing the job in a routine fashion
Keeping a sense of familyDesigning new ways to serve thecustomer
Planning and goal setting for servicedelivery
Incentives and other rewards for serviceexcellence
Active attempts to retain customersNot giving special treatment to majorcustomers
Staffing and training permit good service
Easy access to customer recordsError free records
Understanding of customersCare in introducing new products andservices
Equipment is available and up operatingNecessary supplies available
SOURCE: ADAPTED FROM SCHNEIDER (1990)
Whilst this study was completed in the banking industry it none-the-less has
application across all service organisations and is of particular relevance for the
hospitality industry because the nature of the service interaction in banks with its
immediacy is replicated in hospitality.
Schneider and Bowen (1985) also derived five human resource dimensions of
the relationship between climate and service quality with several items loading onto
each dimension. The five dimensions were, work facilitation (10 items), supervision (14
items), organisational career facilitation (6 items), organisational status (4 items) and
new employee socialisation (6 items). They found a consistent correlation between these
56
human resource dimensions and customer perceptions of employee morale, branch
administration and most significantly overall quality. It is clear that the implications of
these findings are;
Customer perceptions, attitudes and intentions seem to be affected by what the
employees experience, both in their specific role as service employees and in
their more general role as organizational employees. That is, organizational
practices (both service related and human resource related) are apparently the
source of cues visible to customers and are used by them to evaluate service
quality � In other words, because services themselves yield little tangible
evidence as a useful basis for evaluation, it is how they are delivered, and the
context in which they are delivered that is important. (Schneider & Bowen,
1985, p. 430 - 431)
The climate of the organisation is an important factor in the creation of quality
services as defined by the customer. Within the wider quality movement there is a call
for the incorporation of the concept of employee satisfaction as well as the more widely
used customer satisfaction into the overall focus of the business. This is because the
evidence shows that without an environment which supports the employee it will be
difficult to enlist the employee�s support for the objectives of management (Cole,
Bacayan & White, 1993).
In comparing research, it becomes increasingly clear that when the climate for
human resources dimensions are compared to the more generic climate dimensions that
there is a significant overlap. Table 2.4 shows the degree of congruity between the
human resource dimensions of Schneider and Bowen (1985; 1993) and the more generic
one enunciated by Jones and James (1979) and James and James (1989).
57
Table 2.4 Comparison of HRM climate dimensions
SCHNEIDER AND BOWEN(1985; 1993)
JONES AND JAMES (1979)JAMES AND JAMES (1989)
WORK FACILITATION�Conditions on my job do not permit people
to reach their work goals.�
WORK FACILITATIONSupervisor helps achieve goal attainment
through such activities as scheduling,coordinating, planning and providing
resources
SUPERVISIONSupervisors I work with use the rewards
they have (praise, performance appraisals)to let people know when they have done a
fine job.
GOAL EMPHASISSupervisor stimulates personal involvement
in meeting group goals.JOB FEEDBACK
The extent to which an individual is aware ofhow well he is performing on his job.
ORGANISATIONAL CAREERFACILITATION
The organisation provides information andcounselling about my career.
OPPORTUNITIES FOR GROWTH ANDADVANCEMENT
The degree to which an individual feels thatthe organisation provides a vehicle for
development of desired personal skills, goalsand rewards.
ORGANISATIONAL STATUSPeople outside (the organisation) think the
people who work here are high calibrepeople.
PROFESSIONAL ESPRIT DE CORPSThe degree to which an individual believeshis profession has a good image to outsidersand provides opportunities for growth and
advancement.
NEW EMPLOYEE SOCIALISATIONPeople coming on the job get specialtraining that helps them get started.
No directly comparable measure (but jobpressure mentions training. See alsoFriedlander and Greenberg (1971))
James and James (1989) research into psychological climate concentrated on the
extent to which a particular environment (as described by its climate) was of benefit or
otherwise to the individuals exposed to it. Psychological climate was discussed in terms
of being the underlying factor of the usual generic dimensions of climate that they had
found to be invariant over a number of environments. Figure 2.5 displays four
dimensions of psychological climate as identified by James and James (1989) together
with the factors that load upon each dimension. There is considerable evidence, as
58
Figure 2.5 General Factor of Psychological Climate (Reproduced from James and James (1989))
General factor ofPsychological Climate
Leader support andfacilitation
Role stress and lack ofharmony
Job challenge andautonomy
Workgroup Cooperation,warmth and friendliness
Hierarchicalinfluence
Psychologicalinfluence
Leader trust andsupport
Leaderinteraction
Leader goalemphasis andfacilitation
Role abiguity
Role conflict
Role overload
Subunit conflict
Organisationidentification
Managementconcern andawareness
Job autonomy
Job importance
Job challengeand variety
Workgroupco-operation
Responsibilityand effectiveness
Workgroupwarmth tofriendliness
REPRODUCED FROM JAMES AND JAMES (1989)
59
outlined above, to conclude the research on climate which Schneider and Bowen called
a climate for human relations, and the generic climate dimensions of the many other
theorists, including James and James, have examined are in fact the same construct but
with slightly differing nomenclature. It also follows that the appropriate climate of the
organisation is a prerequisite in the facilitation of service quality. The measurement of
climate in an organisation may provide insights as to the issues that need to be
addressed in order for the organisation to achieve its quality service goals.
The Schneider and Bowen (1993) study replicated previous research using the
same climate dimensions for human resource management and the results were largely
confirmed.
This research points out that managers, in their pursuit of service quality, need to
create two related, but different, climates: a climate for service and a climate for
employee well-being. Our research indicates that a climate for employee well
being serves as a foundation for a climate for service. Employees need to feel
that their own needs have been met within the organization before they can
become enthusiastic about meeting the needs of customers. (Schneider and
Bowen, 1993, p. 43)
Schneider and Bowen argue that companies like Four Seasons Hotels and others
that have implemented employee-centred human resource management practices have a
strategic advantage over other less successful competitors. This reinforces the validity
and profound implications for those companies that are seeking to use organisational
climate as a measure of the effectiveness of the organisation in providing quality
service.
Schneider and Bowen (1993), however, make the point that a generic set of
human resource management practices will not necessarily lead to customer perceptions
of service quality. These practices should be designed to suit the particular
60
organisational setting and the consequent customer definition of service quality. In some
settings, procedure driven human resource management practices may be appropriate
and the empowerment of employees may be deemed less important in achieving the
required goals in customer service.
It is also important to note that the measure of service quality that Schneider and
Bowen (1985; 1993) used was developed for the measurement of service in banks.
Whilst Schneider (1990) points to the fact that Zeithaml, Parasuraman and Berry (1990)
have developed a generic service measurement tool (SERVQUAL), no correlation of
the organisational climate and the measurement of its service quality was undertaken.
However, Dean (1997) demonstrated a methodology for making such a comparison in a
study of the applicability of the SERVQUAL model to the health care industry.
2.13 Customer and employee perceptions of customer satisfaction
It has been reported above that a number of studies have claimed there exists a
positive relationship between organisational climate and customer satisfaction. A
related, but different, question is the extent to which employees� perceptions of
customer satisfaction and customers own reports of satisfaction match.
This is an important issue, particularly for service industries such as the hotel
industry, as customer feedback may be difficult to gather, and particularly difficult to
gather in an unbiased form. Should a good correspondence exist between employee
perceptions of customer satisfaction and reports of satisfaction directly provided by
customers, then in many situations employee perception of customer satisfaction may be
used as a more easily measured index of feedback to management. A small number of
studies has addressed this issue.
Schneider et al., (1980) in a study gathering data from both customers and
employees of 23 bank branches found a strong correspondence between branch
customer attitudes about service quality and branch employee perceptions of the quality
61
of the service customers received (r = .67). Schneider and Bowen (1985) replicated the
earlier study gathering data from 142 employees and 968 customers of 28 bank
branches. This study also found a strong relationship between employee perception of
customer satisfaction and that reported directly by customers (r = .63).
The results of these two studies indicate that direct reports of customer
satisfaction are closely mirrored by employee perceptions of customer satisfaction.
Consequently, it may be expected that in many instances employee perceptions of
customer satisfaction will provide management with useful feedback. This would be
particularly so in service environments where production and consumption are
instantaneous and direct assessment of the customer perceptions, at the time, would
negatively impact upon the product�s quality.
2.14 Climate and innovation.
Organisational analysts are increasingly reporting that the failure of innovations
such as TQM, that are more often than not a failure in the implementation stage. It is
here that climate has an important role to play, in both informing management of the
likelihood of success and subsequently during the implementation stage (Klein & Sorra,
1996; Eskildon, 1994). Organisational members� readiness to implement an innovation
strategy is a function of, the climate for innovation; the fit between the values of the end
users; and how well the planned innovation matches those values. According to Klein
and Sorra (1996) �an organisation�s climate for the implementation of a given
innovation refers to targeted employees� summary perceptions of the extent to which
their use of a specific innovation is rewarded, supported, and expected within their
organization� (p. 1060). Additionally, if there is a poor fit with the values of the targeted
group of employees then the implementation will be less successful. Both climate and
culture are important in the successful implementation of innovations such as quality
improvement programmes (Klein and Sorra, 1996).
Scott and Bruce (1994) in discussing the same issues are less concerned with the
62
effect of individual values on innovation. They argue that people in the workplace
respond to the signals and expectancies they receive, regulating their own behaviour in
order to �realize positive self evaluative consequences such as self satisfaction and self
pride� (1994, p. 582). For them the effect of climate on innovation is unmediated by
individual values and only two (performance reward dependency and flexibility) of 10
generic climate dimensions studied were consistently related to innovative performance.
Harrington and Akehurst (1996) found there appeared to be a high level of
awareness of quality related issues in the cross section of the British hospitality
industry. Few managers at an operational level had systems, such as human resource
practices, in place to implement service quality initiatives.
Schneider, Brief and Guzzo (1996) examined the linkage between climate and
culture and the concept of total organisational change that has been achieved by such
firms as Ritz Carlton, AT&T and others. They suggest that these firms are more
successful because they are more effective in managing three aspects of climate (nature
of interpersonal relationships, the nature of the hierarchy and the focus on support and
rewards) simultaneously. The need is to establish a climate that fosters innovation,
customer service, and citizenship behaviour concurrently which in turn allows both high
quality service and enhances organisational performance and success (Schneider,
Gunnarson & Niles-Jolly, 1994).
2.15 Organisational climate and implications for the hotel industry
The hospitality industry in general and hotel companies in particular are
becoming more aware of the need to understand their employee perceptions and the
climate generated by their organisation because of its links to the perceptions and levels
of customer service. While there are obvious advantages in understanding the forces
that are involved in the creation of the organisational climate, it is the linking of that
understanding to day to day activities that holds major significance for management.
63
The effect of managerial actions and leadership factors on the climate of the
organisation has been known since the studies of Litwin and Stringer (1968), McGregor
(1987) Kozlowski and Dougherty (1989) and others. Brown and Leigh (1996) argued
for supportive management where subordinates may try and fail without any fear of
reprisals. This is where employees are allowed to experiment with new methods
bringing creativity to workplace problems. The level of control and freedom and sense
of security that this supportive style of management engenders is more likely to produce
a high level of job commitment and motivation (Argyris, 1964; Kahn, 1990). The
Brown and Leigh (1996) study clearly demonstrates the positive relationship between
supportive management together with clear work goals as being crucial in producing
greater job effort, commitment, and performance. They conclude that the study,
demonstrated an important series of linkages to work relating psychological
climate and job involvement to work performance and indicated that
organizational environment is perceived as psychologically safe and meaningful
is related directly to job involvement and indirectly to effort and work
performance. (Brown and Leigh, 1996, p. 365)
It is established that the psychological climate of individual employees of an
organisation can have pronounced positive or negative effects on the organisation and
its performance. Therefore, it becomes essential that management on a continual basis
should monitor employee attitudes such as the climate of the organisation. The
importance of employee attitude has been recognised by British Petroleum Exploration
who regularly surveys employees. It is especially useful when any organisational
changes are being contemplated (Standing, Martin and Moravec, 1991). Blenkhorn &
Gaber (1995) and Shea (1996) point out that the basis of every customer driven
organisation is the staff who must be respected and valued by their organisation. Shea
(1996) observed that climate surveys are being used by service organisations like Avis,
Amex, St George Bank and the Ritz Carlton Hotel Group to monitor how their
employees perceive the organisation.
64
Organisations pursuing a high quality service strategy appear to be more
cognisant of the role that the climate of the organisation has in the provision of that
service. Organisations like the Ritz Carlton ensure there are no mixed messages in the
routines they follow and try to ensure that every employee feels valued by the
organisation (Schneider, Brief and Guzzo, 1996). Their practices and procedures are
enunciated in a document called the Ritz Carlton �Gold Standards� which includes the
�Credo�, the �Motto�, �Three Steps to Service� and the �Basics�. It is this document that
forms not only the core of the induction process for new staff, but also the operational
mantra for all employees (Partlow, 1993). Another major international hotel chain, the
Marriott Corporation, has for some years adopted an employee survey that is conducted
on a twice-yearly basis to monitor employees� attitudes to the company and
management. This process is firmly embedded into the Marriott management
procedures and is now used as one of the benchmarks of management performance. It
ensures that employee opinions are taken into account and allows for a monitoring of
individual departments by senior regional management. Each hotel general manager and
departmental manager is required to discuss the results at employee meetings (personal
communication Wallace, 1997).
The ramifications for the hospitality industry are plain: the climate of the
workplace is a fundamental factor in the provision of high quality services. Without an
understanding of the function of the climate (psychological climate) of the organisation,
any attempt to improve the quality of the service provision will be in doubt. But, as
Schneider et al., (1994) point out, there are in fact three climates that need to be created
by senior managers, in their terms, a climate for innovation, a climate for service
excellence and a climate for citizenship behaviour. These climates can coexistent in a
successful organisation especially where there is implementation of quality management
initiatives. Organisations need to recognise the climate for human resources or
employee well being (called citizenship by Schneider et al., 1996) in their organisations
65
as the basis for the development of a climate for innovation and a climate for service.
They should not be seen as separate entities so much as elements of a greater whole
with the climate for service and the climate for innovation embedded in the larger
concept of a climate for employee well being, which together represent the member
perceptions of the total organisation. Pfeffer (1998) stresses that the successful
management of people will have multiple dimensions that are shown by certain
organisational characteristics,
! Employment security
! Selective hiring of new personnel
! Self-managed teams and decentralisation of decision making as the basic principles
of organisational design
! Comparatively high compensation contingent on organisational performance
! Extensive training
! Reduced status distinctions and barriers, including dress, language, office
arrangements, and wage differences across levels
! Extensive sharing of financial and performance information throughout the
organisation
For Pfeffer these characteristics must be viewed in a holistic way because if
firms try to implement these initiatives on a piecemeal basis they can actually be
counter-productive. Whilst it clearly takes time to implement such an agenda for
change, a time horizon needs to be set for implementation. Pfeffer (1998) has distilled
these seven practices of successful organisations from various studies, related literature,
observation and experience. In the hospitality industry context, very few organisations
66
would be able to claim the successful implementation of such practices, which was a
view supported by Vallen (1993).
2.16 Summary and conclusion
In this chapter, a review of the literature related to the concept of organisational
climate was presented. It was reported that there have been, and continues to be, some
dispute as to the precise definition of this concept. For the purposes of the current study,
organisational climate was described as an enduring characteristic of an organisation
comprising of member collective perceptions about their organisation across a range of
dimensions. It is a characteristic that is produced by interactions between employees,
reflects prevalent norms, and acts as a source of influence for shaping behaviour.
Organisational climate was described as being distinct from organisational
culture. Culture was described as being deeply rooted in the structure of an organisation,
being based upon values, beliefs and assumptions held by its members. Climate was
described to present social environments in relatively static terms measured by a broad
set of dimensions and is considered as temporary. The above discussion showed that
indices or organisational climate maybe determined by the aggregation of psychological
climate values for individual members of the organisation.
A number of instruments have been presented that purport to provide measures
of organisational climate within organisations. Two important (for this thesis)
instruments were the Psychological Climate Questionnaire (PCQ) of Jones and James
(1979) and the modified version of the same instrument presented by Ryder and
Southey (1990). The original instrument provided measures on 5 or 6 dimensions which
were aggregated across individuals to provide organisational climate measures for
organisations and subunits of organisations on each of the dimensions. The version
presented by Ryder and Southey (1990) contained modifications which rectified some
scaling and presentation problems and reworded questions to both remove sexist
language and make it more applicable to a non-military organisation.
67
Implications of the relationship between organisational climate and the delivery
of high quality service within the hospitality industry were discussed. Sinclair (1996),
for example, discussed the need to develop a climate of trust for the successful
implementation of TQM. Other researchers, such as Partlow (1993) provide accounts of
practices and procedures that relate to climate issues, which are seen by their
organisations as central to the quality improvement process. A number of researchers
reported the link between climate and burnout of employees.
A significant link was shown between the organisational climate of employees
and the climate created by employees for their customers. Schneider (1973) reported
that it was this customer climate that was most strongly linked with customers choosing
to switch from one service deliverer to another.
In a related issue, Parkington and Schneider (1979) found a close
correspondence between employee perception of service quality and the perceptions of
the customer. Schneider and Bowen (1985 & 1993) also found this strong link between
employee perception of customer satisfaction and that reported by customers.
The hospitality industry relies heavily upon the direct interaction between employees
and customers to ensure the perception of the delivery of high quality service from the
organisation. The literature presented in this chapter indicates that organisational
climate is multidimensional and may be represented by aggregated scores on
psychological dimensions gathered from individual employees. Within the hospitality
industry, organisational climate is likely to be a key factor (or set of factors) in the
delivery of high quality service to their customers. In Chapter 3 theoretical models will
be presented proposing relationships between the dimensions of organisational climate,
perceived customer satisfaction, and outcomes for hotels. Specific aims and hypotheses
generated from these models will also be presented.
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3.0 Theoretical Models and Hypotheses
3.1 The research question
This study will apply a modified version of the Jones and James (1979)
Psychological Climate Questionnaire to form aggregate scores representing the
organisational climate within the 14 hotels under study. Relationships between the
dimensions of organisational climate, so produced, and (a) employee demographic
variables, (b) employee perceptions of customer satisfaction and (c) hotel performance
(as indexed by REVPAR) will be examined.
These examinations will be conducted in order to satisfy the overall aim of this
study to answer the following research question:
What is the nature and degree of influence that organisational climate has
upon the level of performance of organisations within the Australian hotel
industry?
3.2 The dimensions of organisational climate within the hotels
The instrument used to investigate organisational climate in this study represents
a modification of the instrument presented by Ryder and Southey (1990), which itself
represented a modification of the Psychological Climate Questionnaire presented by
Jones and James (1979). The first specific aim of this study is:
Aim 1: To identify the underlying dimensions of organisational climate
within Australian hotels.
This aim will be satisfied by applying the modified organisational climate
69
questionnaire to employees of the 14 hotels under study, and by appling Principal
Components Analysis (PCA) to the employee responses. Given, firstly, that the
instrument which will be used is not identical to that used in earlier study, and secondly,
that factor structures reflect not only the instrument, but also the sample under study, it
is difficult to state highly specific hypotheses regarding the precise nature of the
dimensions that will be extracted. Although the study of Ryder and Southey produced a
set of organisational climate dimensions broadly consistent with those presented earlier
by Jones and James, they extracted 10, rather than 6 factors, and provided only 2 of
their 6 interpretable factors with nomenclature identical to that used in the earlier study.
Figure 3.1 illustrates the dimensions of organisational climate that might be
expected on the basis of Jones and James (1979).
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Figure 3.1 Organisational Climate Model A: The dimensions of organisational
climate from the study of Jones and James (1979).
It might, however, be possible that part of the difference in factor structure
found by Ryder and Southey, relates to differences between Australian culture and that
of the U.S. If this is the case, the underlying dimensions of organisational climate may
be more similar to those shown in Figure 3.2.
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Figure 3.2 Organisational Climate Model B: The dimensions of organisational
climate from the study of Ryder and Southey (1990).
On the basis of the factor structures of organisational climate presented by Jones
and James (1979) and Ryder and Southey (1990) the following hypothesis is proposed:
Hypothesis 1: A limited number of factors will be identified as being able to
determine the organisational climate across the hotels in this study.
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It should be understood that neither Organisational Climate Model A, or
Organisational Climate Model B, are causal (or �structural�) models. What is illustrated
in Figures 3.1 and 3.2 are possible dimensions which would underlie climate within an
organisation.
3.3 The relationships between employee demographic variables, organisational
climate, and employee perceptions of customer satisfaction
In Chapter 2 it was argued that organisational climate may be represented by the
sum of the perceptions of psychological climate of the individual employees within an
organisation. It was also argued that the environment within the service organisation
would strongly affect the climate within which the customer operated (i.e., be strongly
related to customer satisfaction). Further, a number of studies were presented which
showed strong correspondence between the employee�s perception of customer
satisfaction and customer reports of satisfaction. Structural Model A, presented in
Figure 3.3, proposes that employee demographic variables will affect organisational
climate, and organisational climate affects employee perceptions of customer
satisfaction.
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Figure 3.3 Structural Model A
The second aim of this study is:
Aim 2: To evaluate the notion that employee perception of customer
satisfaction is affected by organisational climate, which is, in turn, affected by
demographic variables of the employees.
Or, in other words, the second aim of this project is to test Structural Model A.
Specific hypotheses generated from Structural Model A comprise:
Hypothesis 2: There is a relationship between an aggregate measure of
organisational climate and employee perception of customer satisfaction.
Hypothesis 3: Employee demographic variables, taken as a multivariate
74
variable, are a predictor of an aggregate measure of organisational climate.
3.4 The relationships between the dimensions of organisational climate,
employee perceptions of customer satisfaction, and performance of hotels.
As outlined in Chapter 2, the TQM literature clearly provides an expectation that
a good organisational climate is associated with high levels of customer satisfaction,
and that customer satisfaction is the key to financial success of an organisation.
Structural Model B, presented in Figure 3.4, describes such a relationship where the set
of dimensions of organisational climate (taken for the moment as represented by the
dimensions described by Jones and James, 1979) predict employee perception of
customer satisfaction, which in turn predicts hotel performance (as indexed by
REVPAR).
The third aim of this study is:
Aim 3: To evaluate the notion that REVPAR is affected by the level of
customer satisfaction (as indexed by employee perception of customer satisfaction)
which, in turn, is affected by the set of dimensions of organisational climate.
Or, in other words, the second aim of this project is to test Structural Model B.
Specific hypotheses generated from Structural Model B comprise;
Hypothesis 4: There will be a relationship between employee perception of
customer satisfaction and REVPAR.
Hypothesis 5: The dimensions of organisational climate, taken as a
multivariate variable, are a predictor of an aggregate measure of employee perception
of customer satisfaction.
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Figure 3.4 Structural Model B
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4.0 Method
4.1 Introduction
This chapter will provide details of the method used in this study to test the
models and hypotheses presented in Chapter 3. The approach of this study, whilst
primarily quantitative in nature, also includes some qualitative analysis and some
qualitative interpretation of the data.
Justification for the particular research paradigm and the method of this study
will be provided. Further, the use of the organisational climate survey method will be
detailed together with the formulation and subsequent modification of the selected
instrument. Details of the sample selection process will be explained and the parameters
of the sample will be described. The selection process of the demographic details that
were deemed to be of importance to the study and the organisational climate
questionnaire will be justified.
To see what, if any, the relationship is between organisational climate and
organisational performance required the collection of data on dependent variables that
measured hotel performance. The two principal measures used for the study were
employee perceptions of customer satisfaction, and the average daily room rate
multiplied by the occupancy percentages for a hotel (REVPAR). This latter measure is a
widely used industry method of comparing and assessing hotel performance. Morey and
Dittman (1995) used REVPAR as a benchmark in the evaluating of hotel general
manager�s performance. Vallen and Vallen (1991) described REVPAR as an important
statistic in hotel appraisals with it being the basis used by Lodging Magazine to define
success in their annual study of hotels. Both of these dependent variables will be
discussed in detail providing the rationale for their selection.
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The pilot study and pre-test procedures are described, as is the affect. This
feedback had regarding some major decisions on the final instruments, the approaches
to the hotels, the process of dealing with the hotels' management and staff, and data
collection methods. A description will also be given of the process required to get co-
operation from the 14 hotels that participated in the study.
The data collection methods themselves will also be analysed, providing a
detailed understanding of the various processes that were required and followed during
the study. This analysis will detail the sensitivities that became apparent from both
middle level and senior managers.
4.2 Justification for the paradigm and method
Justification for the research method is firmly rooted in the human relations�
school of management research. The psychological forces that impact upon individual
employees� behaviours and perceptions have been part of this research stream almost
from the beginning of the school of thought. Human relations management theories that
take the perspective of concern for individual employees, really only began to emerge in
the last 75 years of this century. Prior to that time, the impact of management actions on
the individual was taken very much for granted. Or, as in the hotel industry, there was
often a paternalistic view taken of staff, if they were valued at all by the employer
(Davidson and De Marco, 1999). Of course if they were not valued, their job tenure
would be very short indeed. Labour protection laws were only just being considered in
the first quarter of the century in most western democratic countries. In many respects
Australia was ahead of many countries in protecting workers� rights with its Harvester
judgement of 1914 (Gardner & Palmer, 1997). Whilst the Harvester judgement,
delivered from the industrial court, was about the living wage for a family, it showed
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that the state held employers to be responsible for treating their workers reasonably. Of
course the existence of a powerful trade union movement was crucial in obtaining
fairness of treatment. Traditionally, the worldwide hotel industry has been an industry
where the union movement has never really been strong in membership. This is
particularly so in Australia. A reason put forward by Timo (1993) is the difficulty in
covering a relatively small number of workers on one site.
Many human relations theorists and especially those who write about the
hospitality industry continually stress that understanding employees is fundamental to
achieving good service and customer satisfaction (Meudell & Gadd, 1994; Francese,
1993; Borchgrevink & Susskind, 1999). Organisational climate studies, as discussed in
chapter 2 have been used as a management tool in the understanding and measuring of
employee perceptions about their organisation (Jones & James, 1979; James & James
1989; Schneider & Bowen, 1985; and Ryder & Southey, 1990, among others). These
perceptions can be expressed through a parsimonious set of dimensions that can be
aggregated to the level of analysis of the organisation but can also be used to analyse the
relationships of the individual to various dependent variables such as customer
satisfaction and REVPAR.
Therefore, this particular research method was chosen because it has a
significant research history and tradition of trying to explain and understand individuals
within organisational settings. What is surprising is that there has been no empirical
academic study completed within the hotel industry. Climate studies, as described in
Chapter 2, have been completed in a range of industries and the application of this
research method to service industries has been a focus of Schneider and Bowen (1985).
Yet the construct�s use to date in the hotel industry literature has been confined to
merely descriptive references which combine culture and climate (Fancese, 1999 and
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Vallen, 1993). The only real use of organisational climate instruments has been through
individual hotel companies doing one-off surveys and only one major chain, Marriott
Hotels and Resorts, has adopted it as standard management tool in the understanding of
employee perceptions.
Much of the current leading edge management literature examines change
management in all its many guises and it embraces the concept of the learning
organisation. Senge (1990) enthuses about empowerment and quality products,
processes and services. This is predicated upon a relatively stable work force. Yet in an
industry, such as the four and five star hotel industry, where it is the norm for the labour
costs to take 30 � 40% or more of turnover (Howarth, 1995), virtually no research has
been done on this cost element and the interaction with management change.
4.3 Identification and rationale for the sample
The current study follows the methods of James and Jones (1976); Jones and
James (1979); Schnieder (1973); Schneider and Bowen (1985); Furnham and Drakely
(1993); Patterson et al., (1996); Ryder and Southey (1990) and many other
organisational climate researchers in selecting an organisation as the unit of analysis.
The focus of the data collected for the study was the hotel industry and in particular the
four and five star hotels. Considerable thought was given to how the hotels would be
selected for this Australian survey.
The survey is divided in 2 major parts: (1) assessing the organisational climate;
and (2) ascertaining the demographic and performance data. Whilst the study focused
upon the organisation as one unit of analysis it also uses the individual employee as the
other unit of analysis, making it a multilevel approach. The research models presented
in chapter 3 clearly show that to test the hypotheses posited, there is a need to undertake
multilevel research.
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Because there are many levels and styles of hotels and to ensure that the
individual hotels returned enough questionnaires for meaningful statistical analysis it
was decided to concentrate upon hotels that were at least four star and with in excess of
150 rooms. These parameters would ensure that the total staffing for each hotel was in
excess of 100 people and the sample size would meet the requirements for the proposed
quantitative statistical analysis.
The next issue to resolve was if this should be an Australia wide sample or
concentrated upon, or within, one region. Whilst an Australia wide sample would have
provided some results that could be interpreted more broadly, the sheer scale and
management of the process would present certain logistical problems. Another
consideration was whether to concentrate upon one company with hotels across the
country. This was rejected because of the possible sensitivities one company may have
with the results and the limitations this may place upon publication. Also, there would
be a possible bias of any of the results obtained because of a particular company culture
and procedures.
It was, therefore, considered that a broad regional study that encompassed a
number of sub-regions representing an appropriate range of hotels would be optimal.
This necessitated an examination of the concentration of hotels in the four and five star
category in Australia based upon the regional areas used by the Tourism Forecasting
Council (1998, p. 24-25). These areas were Sydney and NSW; Melbourne and Victoria;
Perth and Western Australia; Brisbane, Gold Coast, and Cairns; Adelaide and South
Australia; Hobart and Tasmania; Canberra; and Darwin and the Northern Territory.
Because of the requirement for a concentration of four and five star hotels with 150
rooms, only three areas geographically matched the profile, viz., Sydney, Melbourne,
and Southeast Queensland (Brisbane, Gold Coast and the Sunshine Coast). Both Sydney
81
and Melbourne were ruled out, because, whilst they had the number of four and five star
hotels, they were heavily concentrated within the cities and did not have the range of
sub-regions. This left the Brisbane, Gold Coast and Cairns regions. As Queensland is so
diverse, it was decided that Southeast Queensland encompassed such a wide variety of
sub-regions and met the requirements in numbers of four and five star hotels that it was
appropriate to concentrate the study upon this area.
What is a four and five star hotel? According to the Royal Automobile Club of
Queensland (RACQ, 1997) the definitions are:
Five star hotels -
International style establishments offering a superior standard of
appointments, furnishings and décor with an extensive range of first class
guest services. A number and variety of room styles and/or suites
available. Choice of dining facilities, 24 hour room service and additional
shopping or recreational facilities available.
Four and half star hotels -
Establishments offering all the comfort of a four star establishment but
with a greater range of facilities, higher levels of presentation and
individual guest services.
Four star hotels -
Exceptionally well appointed establishments with high quality
furnishings and offering a high degree of comfort. Fully air conditioned.
High standard of presentation and guest services provided. Restaurant
and meals available on premises.
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Three star hotels -
Well appointed establishments offering a comfortable standard of
accommodation, with above average floor coverings, furnishings,
lighting and ample heating/cooling facilities.
(Source, RACQ, 1997, p.22)
At the time of the survey, and within the parameters set of four to five star hotels
with 150 rooms plus, Brisbane had 13 hotels, the Gold Coast 15 hotels, and the
Sunshine/Fraser Coast four hotels making a total of 31 eligible hotels in the study area.
Those that actually took part were, five from Brisbane, six from the Gold Coast and
three from the Sunshine/Fraser Coast. This represents 44% of the hotels.
4.4 Gaining co-operation of the hotels
When this research study was initially undertaken it was always realised that
without the full co-operation of sufficient hotels it would not be viable. Two immediate
issues needed to be addressed, firstly, how were hotels to be approached and selected,
and secondly, on what basis could they be persuaded to co-operate. For initial contacts,
the researcher used his personal network of hotel managers, where it became clear after
numerous discussions there was a definite reluctance to take part. The reasons put
forward included the total distrust of academic research. As is made clear by the
comment of a general manager of a Queensland five star hotel when he expressed his
opinion by stating what he thought of such studies as �being of absolutely no relevance
to running an hotel� (name withheld). At the other end of the negative comment
spectrum was a comment by a senior HRM that �it was all very well doing research but
the time it took of staff and management was just too much� (name withheld).
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It was at this stage that a reference group (expert panel) of 6 senior hotel
executives was established. Their advice ranged from the formulation of the instruments
as previously discussed to the lobbying of industry colleagues to encourage them to take
part. Each member of the expert panel was visited and the overall project of ascertaining
organisational climate and comparing it to organisational performance was explained in
detail. Had such a survey been commissioned from private consultants it would cost
several thousands of dollars. As a senior executive of Marriott in Australia was a
member of the reference group, and as Marriott already used an organisational climate
survey, the management benefits were easily made clear to all the executives.
In particular, Mr Grant Bowie, Chairman of the South East Queensland
Accommodation Division of the Queensland Hotel Association agreed to support the
project and call for volunteers from all eligible members within the designated area.
This was done via a regular �Monthly Chairman�s Newsletter�. There is little doubt that
without this support and the various networks of the expert panel it would have proved
extremely difficult to proceed with the level of participation achieved. The researcher
attended several formal and informal meetings with executives in the hotel industry in
order to firstly talk about organisational climate and to inform them that a major study
was to be undertaken.
A comment made by several of the expert panel was that this type of survey,
asking staff their perceptions, was seen as a very threatening process to the management
of a property. These comments were supported by 11 of 14 HR managers of the
properties that eventually took part.
The response to the chairman�s newsletter brought forth 17 hotels that expressed an
interest in taking part. All of these hotels were visited and detailed discussions took
84
place with the general managers and the HRM departments. This entailed a full
explanation of the background of the instrument and its major dimensions. It also
allowed the managers to ask how the results would be processed and how they might be
interpreted. These discussions ended with the offer to each hotel that they would
receive:
! full confidentially and anonymity
! a full descriptive statistical report on their own organisational climate with the
overall means for each dimension broken down into departments
! an interpretative report accompanying the statistical report on of their organisational
climate
! a pre-briefing with both senior management and departmental heads if required
! a post briefing with both senior management and departmental heads if required
In return the hotels would agree to facilitate the distribution of the questionnaire by:
! the general manager including a short note to encourage staff to complete the
questionnaire
! HRM would ensure that the questionnaires were distributed through the
departmental heads
! HRM would ensure that each departmental head was fully briefed on how the
questionnaire should be completed
! HRM would ensure that departmental heads provided a briefing to their staff
! HRM would distribute the management questionnaire
! HRM would act as a collection point
! the general manager or financial controller would complete the profile document
that gave key financial performance indicators
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As can been seen from the above points, the process from both the hotels� and
researcher�s perspective was fairly complex and involved. Of the original 17, 2 hotels
withdrew from participation when the full detail of the study was explained. The
reasons given were, in one case, they were not prepared to spend the time required and
in the second case, the manager was being transferred. A fact he was not aware of when
he responded to the initial request.
One other hotel withdrew from participation at the last minute because they said
they were going to undertake a major re-structure of their organisation. This left the 14
hotels that actually participated.
There was a fairly constant need to keep in weekly touch with the properties because
the actual dates of the survey were being left to them to select in order to fit in with
business requirements. It is noteworthy that the distribution and collection process was
without any real problems. The promised reports on each hotel�s organisational climate
were returned within two months of the data being collected. Whilst the detail of these
findings are fully discussed in Chapters 5, 6 and 7 it is interesting to note that only two
hotels took advantage of the offer to conduct a post report briefing. Four of the other
hotels made contact, by phone, after the reports were sent to discuss the contents. The
eight hotels that did not respond were all contacted. Several said they would get back to
the researcher when they had time to digest the contents, but none has done so. The
other hotels simply expressed their thanks, saying that, their management team would
discuss the report.
The reports were by necessity quite detailed and from the experience of the 2 hotels
who wanted detailed de-briefing for the senior management the main concern was to
ensure that they were reading the results correctly. Contact with the HRM departments
86
of the hotels that required no explanation sometime after has revealed that the survey
confirmed what senior managers already suspected about their organisational climate
and thus were not keen to pursue the issue. A comment was made that �it confirms what
we know but within the operational constraints there is little we can do to improve�
(name withheld).
4.5 Formulation of the survey instruments
The main questionnaire was the organisational climate, a modified version of
Jones and James (1979), plus demographic details for individual employees. Another
instrument was developed to ascertain certain financial and statistical information. This
information was required for the analysis of organisational climate in each hotel to test
the hypothesis that performance is correlated with organisational climate. Also a third
instrument was used to collect data only from managers within each property that
focussed upon demographics and performance measures at the hotel level. This
instrument was primarily used as a check on operational and performance indicators.
4.5.1 The organisational climate questionnaire
The instrument is capable of producing an empirical assessment of the
influences exerted by both situational and individual factors in the development of
work-related perceptions. As discussed in the Chapter 2 section 2.15, it was originally
based upon 35 a priori scales drawn from the literature that are assessed by 145 items.
Ryder and Southey (1990) used the Jones and James instrument to survey a large
public service building, construction and maintenance authority in Western Australia.
They reported that is was necessary to modify the instrument in several ways. Some of
the items were reworded because of culturally specific meanings and the eradication of
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sexist language. Further modification was required to remove wording that referred
specifically to a military setting (the original Jones and James instrument was used on
US Navy personnel). The original questionnaire used a variety of response methods
across items and so the response categories were also modified to a consistent seven
point Likert type scale as this would reduce the time taken by each respondent to
complete the questionnaire. Ryder and Southey also reported that the 145 item revised
questionnaire took approximately 30 minutes to complete.
The instrument used in this study, followed Ryder and Southey�s modifications
but additionally needed to adjust the language from that suitable for the public service to
that of the hotel context. This required only some fine-tuning of certain item wordings.
However, there was a far more contentious issue of the formulating of the current
instrument. A 30-minute completion time for the questionnaire (Ryder & Southey,
1990) was going to be a major impediment that threatened the feasibility of the whole
study. The author�s 20 years of experience within the hotel industry and discussions
with hotel managers confirmed that such a long and complicated survey instrument
would simply not get the required response rate. Sekaran (1992) noted that there is an
inverse relationship between the length of the questionnaire and response rate. Indeed
many hotel managers indicated that participation would depend upon what they saw as
being a reasonable length for the instrument.
After consultation and email discussions with one of the original co-authors of
the questionnaire Professor Lawrence James, Pilot Oil Professor of Management at the
University of Tennessee, Knoxville, it was agreed that a shortened version of the
instrument would still capture the construct. Because the original instrument had
between two and seven items loading upon each factor and taking into account the
analysis of Ryder and Southey�s which showed improved reliabilities it was decided
88
that the first 2 items loading onto the 35 a priori scales would be used. The approach of
using a shortened version of the instrument was endorsed by Professor James (via e-
mail, on the 24th February 1997) and also by Professor Ryder, Professor of
Management, Griffith University, Gold Coast (February 1997).
The shortened version of the organisational climate qustionnaire now consisted
of 70 items with a rooted seven point Likert type scale: 1 - strongly disagree to 7 -
strongly agree. All items were grouped onto five pages with between 13 and 15 items
per page. The 70 items for the 35 a priori scales were randomly distributed thought the
questionnaire. Instructions were given in the short preamble and the rooted seven point
Likert scale and instructions were repeated on every page. The 70 items and the 35 a
priori scales used to develop them are presented in Table 4.1. The full questionnaire is
presented in Appendix A.
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Table 4.1 70 items of the modified version of the PCQ used in this study and 35 the �a priori� scales used by Jones and James (1979).
�a priori� Scale Item # Item
1. Support 8 Your supervisor is friendly and easy to approach.20 Your supervisor is attentive to what you say.
2. Work Facilitation 9 Your supervisor offers new ideas for job and related problems.21 Your supervisor provides the help you need to schedule your work ahead of time.
3. Goal Emphasis 30 Your superior emphasises high standards of performance.33 Your supervisor sets an example by working hard himself or herself.
4. Interaction Facilitation 14 Your supervisor encourages the people who work for him or her to exchange ideas and opinions.45 Your supervisor encourages the people who work for him or her to work as a team.
5. Job Feedback 29 You have good information on where you stand and how your performance is evaluated.38 You are aware of how well your work group is meeting its objectives.
6. Confidence and 4 Staff members generally trust their supervisors.Trust Up 15 Staff members generally trust their managers.
7. Upward Interaction 51 Your manager is successful in dealing with higher levels of management.70 Your immediate supervisor is successful in dealing with higher levels of management.
8. Awareness of Employees 37 Supervisors generally know what is going on in their work groups.needs and problems 49 Managers keep well informed about the needs and problems of employees.
9. Openness of Expression 31 The ideas and suggestions of staff members are paid attention to.44 Communication is hindered by following chain of command rules.
10. Job Variety 25 There is variety in your job.32 You have the opportunity to do a number of different things in your job.
11. Job Challenge 2 Your job requires a high level of skill and training.43 You have opportunities to make full use of your knowledge and skills in your job.
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Table 4.1 Continued
�a priori� Scale Item # Item
12. Professional Esprit 41 The hotel has a good image to outsiders.de Corps 60 Under most circumstances I would recommend this hotel to a prospective staff member.
13. Job Autonomy 1 Opportunity for independent thought and action exists in your job.12 Responsibility is assigned so that individuals have authority within their own area.
14. Organisational Esprit 42 Working in this hotel is beneficial to your career.de Corps 56 This hotel is concerned with assisting the local community.
15. Opportunities for Growth 23 You have opportunities to learn worthwhile skills and knowledge in your job.and Development 48 The hotel emphasises personal growth and development.
16. Workgroup Esprit 61 Most of the personnel in our department would not want to change to another department.de Corps 62 Most members of my work group take pride in their jobs.
17. Role Ambiguity 11 Your job responsibilities are clearly defined.24 New staff members get on-the-job training they need.
18. Efficiency of Job 7 You are able to get the money, supplies, equipment, etc. your work group needs to do its work well.Design 19 Procedures are designed so that resources are used efficiently.
19. Role Conflict 18 You have opportunities to complete the work you start.65 Excessive rules and regulations interfere with how well I am able to do my job.
20. Conflict of Org. 57 Things in this hotel seem to happen contrary to rules and regulations. Goals and Objectives 59 In this hotel things are planned so that everyone is getting in each others way.
21. Job Pressure 26 Your hours of work are irregular.67 Compared with other work groups, my work group is under much less pressure to produce.
22. Planing & Coordination 55 The way your work group is organised hinders the efficient conduct of work.66 Overall I think my immediate supervisor is doing a good job.
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Table 4.1 Continued
�a priori� Scale Item # Item
23. Opportunities to Deal 13 Dealing with other people is part of your job.with Others 68 In your job the opportunities to get to know people are limited.
24. Planning and Effectiveness 5 The methods of your work are kept up to date.47 The hotel strives to do a better job than other hotels of the same type.
25. Ambiguity of Org. 46 It is possible to get accurate information on the policies and objectives of this hotel.Structure 52 The objectives of the hotel are clearly defined.
26. Confidence & Trust 27 Everything in this hotel is checked; individual judgement is not trusted.Down 36 People act as though everyone must be watched or they will slacken off.
27. Job Standards 3 You are required to meet rigid standards of quality in your work.39 Your job demands precision.
28. Consistent Application 17 The hotel�s policies are consistently applied to all staff members.of Org. Policy 50 Discipline in this hotel is maintained consistently.
29. Job Importance 6 You are required to perform tasks on your job which you consider relatively unimportant or unnecessary.54 Your work is important.
30. Reputation for 64 My department, compared to all other departments would be one of the most productive.Effectiveness 69 Compared to all other similar work groups in this hotel, my work group would be the most productive.
31. Workgroup Friendliness 34 A friendly atmosphere prevails among most of the members of your workgroup.and Warmth 40 Members of your work group trust each other.
32. Workgroup Cooperation 10 A spirit of cooperation exists in your workgroup.22 There is friction in your workgroup.
33. Interdepartmental 53 There is conflict between your department and other departments of the hotel.Cooperation 63 Generally there are friendly and co-operative relationships between the different departments of the hotel.
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Table 4.1 Continued
�a priori� Scale Item # Item
34. Org. Communication 16 You are given advanced information about changes which might affect you.Down 58 In this hotel the only source of information on important matters is the grapevine.
35. Fairness & Objectiveness 28 Being liked is important in getting a promotion.of Rewards 35 Hotel politics count in getting a promotion.
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As discussed in Chapter 2 there has been some considerable debate over the
validity of using a multilevel research approach with 2 units of analysis, namely the
organisation and the individual employee, most notably from Glick (1985). This
criticism was rebutted by James, Joyce and Slocum (1988) arguing it is the individuals
that cognise and not the organisation. This study is conceptually based upon the use of
multilevel research, using both the hotels (organisations) and their individual staff
members (employees). Whilst it is both academically interesting and of potential value
in a management sense to study organisational climate in its own right, the position
taken here is that the explanatory power is greatly enhanced when it can be used to
predict and interpret organisational performance. Therefore, the selection of the hotels
was crucially important not only for sampling reasons but also to allow access to a large
number of employees that are required for any organisational climate study.
4.5.2 Hotel profile and hotel managers questionnaire
The main purpose of these instruments was to gain performance data from the
various hotels that would enable the researcher to establish some key performance
indicator that could be related to organisational climate. The instruments sought several
categories of information that included operational, financial and marketing statistics
plus organisational structure and external factors. In order to design the instruments,
input from the expert panel of 6 hotel executives was used.
The �Hotel Profile� - After initial discussions it became clear that to access the
last audited accounts of each property would be extremely problematic because in the
view of the expert panel there would an unwillingness of hotel general managers to
release this information. Secondly, there would be difficulty getting corporate approval
for properties that belonged to large chains.
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It was agreed by the expert panel that the information needed to be of a
generally less sensitive nature and the sort of statistical information that general
managers were giving out on a more routine basis to various federal, state and local
authorities. In essence the information was restricted to room occupancy, average daily
room rate (these 2 statistics are required to calculate REVPAR), standard room rates,
business and revenue mix, staffing statistics and organisational structures. The general
manager or financial controller should be asked to fill in the information requested. The
hotel profile instrument is presented in Appendix B.
The �Hotel Managers Questionnaire� - This replicated the demographic
information that was to be gathered in the main organisational climate instrument to be
given to the employees. None of this information was of concern to the expert panel.
The second section requested some financial performance indicators. It was felt that this
information should be requested on the basis of budget performance on a five point
Likert type scale ranging from 1 - under budget to 5 - well above budget. This would
give detailed indicators on how each hotel was operating but without giving the actual
figures. The format was also followed for operational and customer satisfaction. The
full instrument is presented in Appendix C. Of the expert panel of six executives from
four and five star hotels only three took part in the study.
In addition to the usual demographics of gender and age it was of particular
interest to see what effect education level, length of service within the hotel, length of
time in the job, gross salary, employment status, hours worked, training interval, and
functional area of individuals had upon organisational climate. The above demographic
categories are used in the structural model A as exogenous variables that affect
aggregate organisational climate .
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4.6 Perceptions of customer satisfaction measure
The issue of how to measure the dependent variable customer satisfaction posed
a particular problem of considerable concern. Academic analysis of the literature on
customer satisfaction within the hotel industry brought forth an amazing lack of
empirical data. Most data were based upon qualitative approaches or extremely small
sample sizes. The approach to this issue, was the use of an expert panel as advocated by
Archer (1987), Elkin & Roberts (1987) and Moeller & Shafer (1987) as a method of
assessing how this might be measured. The question of how to measure customer
satisfaction was put to the expert panel all of whom said that they scrutinised the guest
comment cards but they also readily agreed that these represented approximately only
4% of their customers. None of the expert panel used, in their hotels, any other means of
systematically testing their customers� satisfaction. All the hotel managers agreed that
the most reliable method was the feedback obtained by their staff that was then fed back
to the management team.
Lewis and Nightingale (1991) commented that hotel companies have difficulty
in measuring customer satisfaction and, in spite of the inefficiency of comment cards,
many still rely upon them. However, they also make the point that Marriott regularly
surveys its customers randomly and chains like Sheraton are always looking at how the
room comment cards can be improved. Francese (1993) highlighted the fact that hotels
have built up an entrenched bureaucracy and bottom line thinking that often stifles the
employees intuitively providing responsive customer service which, as Parasuraman,
Zeithaml and Berry (1985) have shown, is the key to service quality customer
satisfaction.
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As described in Chapter 2, Schneider and Bowen (1985) empirically
demonstrated that customers and employees share perceptions and attitudes. It is
therefore appropriate to use the employee perceptions of customer satisfaction as a
reasonable measure of organisational performance in regard to customer satisfaction.
Additional to the above, cited links between employee perceptions, customer
service and satisfaction, the recent work by Testra, Skaruppa & Pietrzak (1998) used the
Bagozzi (1992) framework in a study of the phenomenon in cruise line staff. This study
used self-reported perceptions of job satisfaction and service intentions that were
compared to customer satisfaction measured through structural equation modelling.
They found that there was an empirically verifiable relationship between the constructs
and that job satisfaction directly related to customer satisfaction.
The above approach of employees reporting of customer satisfaction has been
adopted as one of the research methodologies used in the current study.
4.7 Organisational performance � Revenue per available room (REVPAR)
Revenue per available room (REVPAR) has been selected as the prime indicator
of a hotel�s performance because it combines 2 of the key performance indicators, that
of occupancy percentage and average daily room rate. Figures on occupancy and room
rates are collected and published by the Australian Bureau of Statistics (ABS) and are
also disseminated by the major tourism bodies such as Tourism Queensland and the
TFC. It is indicative of their importance to the industry in monitoring its performance
that both these measures are collected and published by the ABS and then further
published by main tourism bodies throughout Australia.
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4.7.1 Occupancy percentage
The occupancy percentage is the percentage of the total rooms let for any given
period. The ABS figures are the aggregate performance of all hotels in a particular
category � licensed hotels with facilities, within the region. This category covers all the
major residential 3 to 5 star hotels and immediately provides a benchmark for hotel
operators and managers as to how they are performing against the average for their
particular area. It also can be used in conjunction with previous data to indicate the
market conditions that have changed within the defined area and identify any specific
trends. Many researchers such as Morey & Ditman (1995), Morey (1998) and Vallen &
Vallen (1991) have all shown the relationship between occupancy and profitability; the
higher the occupancy, the higher the hotel�s profitability. It is the �short-hand� used by
hotel managers to judge how they are performing. Logically, if the hotel rooms are full
then the hotel is gaining high revenue and the potential for high profits exists. During
the data collection period for the survey, the 3 areas of Brisbane, Gold Coast and
Sunshine Coast recorded occupancy percentages of 63%, 64% and 57%, respectively,
for the December 1997 quarter, and 64%, 61% and 59.6%, respectively, for the March
1998 quarter (QTTC, 1998).
4.7.2 Average daily room rate
Average daily room rate (ADRR) is the amount of money a hotel receives for
the letting of the rooms for a particular period divided by the number of rooms let. The
ABS figures are the aggregate performance of all hotels in a particular category �
licensed hotels with facilities within the region. This category covers all the major
residential three to five star hotels and provides a benchmark for hotel operators and
managers on performance. It also gives an indication of the prevailing market
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conditions within the defined area. The amount of money a hotel can achieve for each of
its rooms has a direct effect upon its profitability. Apart from the logic of such a
statement it has also been the subject of study by many theorists such as Morey &
Ditman (1995), Morey (1998) and Vallen & Vallen (1991).
However, the measure of a hotel�s performance is a more complicated issue than
that of occupancy percentage and the ADRR. A hotel�s performance affected by a range
of other critical operational decisions and market mix. Hotels have published rates for
differing types of rooms, e.g., suites are more expensive than standard twin rooms. They
also have different rates for various types of business or market mix. The corporate
business executive with an account at the hotel would normally receive a low rate
because of the frequency of visits, whereas a casual visitor just travelling through the
area who arrives without a reservation is likely to pay considerably more for the
equivalent room. Multiple permutations are used in the setting of a room rate and a full
explanation is not appropriate for this study. However, it can been seen when business
and market mix is overlaid onto room types and the standard of amenities offered by
particular properties, how complex the issue of average daily room rates is.
During the period of the data collection for this survey, Brisbane, Gold Coast
and the Sunshine Coast recorded an average daily room rate of $97, $110 and $120
respectively for the December 1997 quarter and $113.60, $127.80 and $135.30
respectively for the March 1998 quarter (QTTC, 1998). It should be noted that the
upward movement of all rates in the March 1998 quarter reflects the peak holiday
season trading when all rates are high, and any annual increase would have occurred
from January 1998. These are yet 2 more factors that need to be taken into account
when assessing ADRR.
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4.7.3 Revenue per Available Room (REVPAR)
The use of average daily room rate (ADDR) multiplied with the occupancy
percentage of the rooms produces figure that is called REVPAR. REVPAR represents a
complex figure combining both elements and is commonly used as one of the main
performance indicators for the worldwide hotel industry. Whilst it is possible to use
either ADDR or Occupancy singularly as points of reference for operating and financial
performance, the combined figure gives a more refined measure. The hotel bedroom is a
very perishable product in terms of its letting ability. Once a hotel bedroom is not let for
a night that potential revenue can never be recovered, which is why occupancy is so
critical to performance. However, most hotel managers worry about letting rooms too
cheaply just to fill capacity, a point was confirmed by the expert panel. Once a hotel is
known for discounting, this creates continual downward pressure on their rates. The
general travel industry, such as, inbound and other travel agents and business groups all
expect to be able to negotiate for low rates. The impact can be very severe on the
ADRR.
Therefore, the combination of occupancy and ADRR in REVPAR does provide
a powerful performance indicator as far as rooms are concerned. A key element of
REVPAR is that it is able to truly represent a hotel�s performance, whether the
marketing strategy is following the occupancy at any cost (profitless volume approach)
or whether the hotel is holding out for a high room rate at the expense of occupancy.
4.8 Pilot and pre-testing procedure
Three instruments were to be used in the research study. As previously
examined the main survey instrument for organisational climate was derived from 2
previous major applications of the instrument from its originators Jones and James
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(1979) and one of its subsequent applications by Ryder and Southey (1990). The main
hotel performance instrument had been designed in conjunction with the expert panel.
The final instrument, aimed at managers only, that required basic demographic,
operational and budget performance data, was also designed in conjunction with the
expert panel.
A range of pre-testing procedures were completed with the main organisational
climate instrument because of the shortening of its length and the minor modification of
wording to make it specific to the hotel context. Initially the organisational climate
instrument was given to small tutorial groups of 15 to 20 third year undergraduate, hotel
management students to complete. The main purpose was to ascertain the time scale
required for completion and assess whether the modification to the wording was fully
understood. Third year hotel management undergraduates were used because all
students had gained work experience within the industry and they were working
currently in a range of casual and part-time positions. This, therefore, provided a very
suitable setting for some pre-testing. Additionally, approximately 30 % of the groups
comprised overseas students, whose first language was not English, that provided an
opportunity to test whether any of the item wording was ambiguous.
The students were not pre-briefed except to ask for their co-operation, and were
asked to assume they were filling in the questionnaire at the request of their supervisor
at work. Students who were not currently working were excluded. They were asked to
follow the directions for the completion of the instrument as given by the introduction
and take whatever time they need to complete it. The students took between 15 and 20
minutes to complete the questionnaire. After all students had finished, they were asked
if there were any difficulties in completion or understanding the questions. Student
comments were recorded. This process was repeated for six tutorial groups.
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An analysis of comments was undertaken and it showed that the instructions
needed slight modification. Although individual comments were made about certain
item wording there was no discernible pattern on any particular item that might indicate
it needed re-wording.
After the re-wording of the instructions, two hotels were approached to
undertake the pre-testing on hotel employees of the main organisational climate
instrument. This was done by using groups of staff varying between four and six from
the food and beverage, rooms, and back of house departments. As with the student pre-
testing, all the staff were thanked for their co-operation and asked to follow the
instructions and take as much time as they required to complete the questionnaire. The
groups all completed the questionnaire within 22 minutes with the fastest time being 14
minutes. All groups were asked for feedback and a number of comments were made
about particular items and their wording; comments such as �a little confusing� or �I
wasn�t quite sure what it was asking�. The process of evaluating all the comments to see
if any pattern emerged that required individual items to be re-worded was followed.
There was no discernible pattern to the staff comments, indicating that the items were
generally well understood. It should be noted that each group was randomly selected, on
the day, by the department head.
The whole pre-testing process in the hotels from greeting to getting feedback
took no more than 40 minutes to complete. It was established that the instrument
generally took under 20 minutes to complete and it was only a very small number of
hotel staff that took the extra 2 minutes. All the questionnaires were checked to see
whether answers showed the expected spread of responses, not all low, middle or high
which might indicate the respondents were not discriminating in their answers.
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It can, therefore, be said that the pre-test process enabled improvements to be
made in the instructions and, when this was completed and used on the hotel staff
sample, the instrument presented no problems for the staff to complete within a fairly
small time frame. The time the questionnaire took individual employees to complete
was critical, as the expert panel were of the view that a industry would not co-operate if
the instrument was too long.
4.9 Administration of climate questionnaire
The 70-item organisational climate questionnaire formed part one of the
omnibus survey. Part 2 consisted of demographic details and background of the
respondents and their perceptions of operational and customer satisfaction. Attention
was given to the readability and layout, with the typeface being �Times New Roman� in
a minimum font of 12-point. All answers either required a number to be circled, on a
Likert type scale, or a box to be ticked. One minor exception was included on the last
question that asked about which department they worked, where it allowed the
respondents to write in a department if theirs was not listed.
Each hotel was assigned a code number that was incorporated into the survey
instrument for ease of identification and tracking purposes. All questionnaires were
delivered by hand to each hotel at least one week prior the date of the study.
Additionally, sealed boxes with slots to take the returned questionnaires were provided
to each HRM department to facilitate collection. An initial step was to ascertain the
numbers of staff in each hotel, the human resource departments supplied the
information. It had been agreed that each HRM department would take the
responsibility of distributing the climate questionnaire within their property to
individual heads of departments. HRM departments were also responsible for briefing
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the department heads on how it should be distributed to employees. Department heads
were aware the survey had the support of senior management as it had been discussed at
the previous week�s departmental heads meeting. The department heads would actually
distribute the questionnaire at the time the weekly wages slips were given out and
simply ask their staff for co-operation in its completion. The staff where told to return it
at their earliest convenience within 3 days.
Each questionnaire was given out with a short introductory letter together with
concise instructions on how it should be completed, and a return addressed envelope.
Employees were told that they had the option of returning the sealed envelope to HRM
or directly to the university. It should be noted that only the employees present on the
day or the next day were given the questionnaires. Therefore employees that were away
for the two days because of roster days off, holidays, part-time employment or casual
employment were not included. Whilst this did reduce the overall numbers it was seen
as a much more manageable process by the hotels that took part.
Less than 10 % of the total response from the employees were returned directly
to the university. Arrangements were made to collect all the returned questionnaires
within seven working days of distribution. With the exception of one hotel with a large
number of employees that returned 61 completed questionnaires after the collection
period there were a very small number forwarded on by the hotels that had been handed
in late. All of the 14 hotels participating selected which week it was appropriate for
them to distribute the surveys because of their individual business commitments. This
process was completed within a three-month time frame.
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4.10 Data collection and sorting procedures
These procedures have, in the main, been previously discussed in the above
headings but it is relevant to list the chronology of events that were followed.
The questionnaires were arranged with the main organisational climate 70 item
instrument being completed first and this was then followed by both the demographic
and performance indicator questions. Oppenheim (1986) and Sekaran (1992) suggested
this format as when the respondent reaches the end of the questionnaire they are likely
to be convinced of the genuineness of the survey.
All the questionnaires were personally collected when they were completed.
This provided an opportunity to discuss with the HRM departments how the process of
distribution had gone. Additionally, informal feedback was also obtained on how the
staff had reacted. No significant problems were reported and the most common reaction
was that the HRM departments thought that the response rate was very good for such a
complicated instrument.
The total number of questionnaires delivered to each hotel was checked with the
completed number of returns and the returned non-distributed ones. These were
deducted from the delivered total so that the completion rate could be computed. All
hotels were given a code and thus each batch was kept completely separate and the data
was entered for each hotel batch by batch for processing on an SPSS computer package.
A standardisation procedure was required on one question from the
questionnaires. As each hotel was allowed to use its own department names in order to
facilitate the reporting back of individual departmental organisational climate for the
hotels, the number of departments varied from a minimum of 8 to a maximum of 20. It
105
was decided that these should be collapsed into consolidated 6-department structure for
the main analysis after the individual hotel reports had been produced.
All data were scanned to ensure that no questionnaire returns showed any
patterns of regularity that might indicate the respondents were not discriminating in
their answers to the 70 item organisational climate instrument. Only six were discarded
because of this possible effect. The questionnaire was encoded to facilitate the entering
and processing the data through the SPSS computer software package.
Chapter 5 reports descriptive and simple statistical analyses of the hotel general
operating statistics and employee demographic data.
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5.0 Hotel General Operating Statistics and Staff Demographic Data
5.1 Introduction
This chapter will present the initial analysis of the data collected in this study.
Whilst the prime focus in the chapter will be on a descriptive analysis of both the
operating statistics of the 14 hotels and the demographic profile of their workforce, it
will be supported by some simple inferential statistical analyses. The combination of a
descriptive analysis with inferential statistics is a common procedure used by
researchers when they are trying to interpret individual functions and behaviours
(Ghauri, Grenhaug and Kristianslund, 1995).
5.2 Analytical procedures
The data gathered in this study fall into 2 broad categories. The first, uses data
for which each data point represents a single value for each of the 14 hotels (hereinafter
referred to as �Hotel Level Data�, e.g., the Rack Rate). With a sample size of only 14,
the analysis of data is limited to descriptive and simple inferential statistical analyses.
The second category uses data for which each data point represents a value for an
individual staff member of a particular hotel (hereinafter referred to as �Staff Level
Data�, e.g., an individual employee�s age). Data may of course be aggregated to
produce, for example, the mean value for a variable of employees within each of the
hotels and so provide new aggregate variables that would then belong to the �Hotel
Level Data� category (e.g., mean age of employees within each hotel). Given the large
sample size of individual employees participating in this study, variables representing
data gathered in the Staff Level Data category may also be subjected to complex
multivariate inferential statistical analysis and structural equation modelling procedures
that will be addressed in subsequent chapters.
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The first part of this chapter reports Hotel Level Data for 14 hotels in South East
Queensland. This section will concentrate upon demographic data, general operational
statistics and ranking of hotels by their REVPAR performance. Whilst REVPAR is a
key performance indicator it must be interpreted in the context of other market
information such as the business mix which is principally concerned with the types of
markets the hotel attracts. As important as the business and revenue mix are, various
other factors need to be used in an interpretative analysis of a hotel�s operation. In this
research, data were also collected on employment statistics such as employee turnover
and the wages-to-revenue ratio to provide a more inclusive set of data from which to
analyses for each hotel�s operation and REVPAR.
In the second part of this chapter, Staff Level Data are reported. For both
employees and managers, the results of key individual demographic data are given
including gender, age profile, educational level, organisational tenure, job tenure, gross
salary, mode of employment, hours worked, training frequency and training needs. In
later chapters causal models will be evaluated that incorporate demographic variables
presented in this chapter. For these demographic variables to be useful as predictors of
outcomes such as the variation in REVPAR between hotels, the staff demographic
variables must not only vary between individuals across the whole sample, but must
also (when used to produce aggregate variables) vary between the hotels.
For example, if each hotel had roughly the same mix of staff, then, across the
whole sample, variables such as age, gender, years of education, etc., would vary from
individual to individual, but aggregate scores on these variables would not vary from
hotel to hotel. If these aggregate scores did not vary between hotels, then these variables
would not be useful in providing an explanation of the variation in REVPAR or
Organisational Climate between hotels. For this reason, in the second part of this
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chapter, aggregate scores on Staff Level Data are presented and simple inferential
statistical comparisons are made to examine whether these demographic variables vary
across the hotels in our sample.
The data have been encoded and analysed using SPSS software (SPSS Inc.,
1998). In chapters 6 and 7 more complex multivariate inferential and structural equation
modelling analyses will be presented.
5.3 Hotel level data
Firstly, within this section it is appropriate to provide some detail on the
individual hotels that took part in the survey. Whilst some preliminary analysis of the
hotels was undertaken in Chapter 3, principally to justify their selection for the sample,
it did not provide any detail of their operational characteristics. In Tables 5.3.1 to 5.3.4 a
summary is provided of the major characteristics and operational data of the hotels in
the sample. These data provide a background for each hotel�s climate and is necessary
information in order to be able to interpret each hotel�s climate score within its own
business context. REVPAR has been selected as the main measure of a hotel�s
performance in order to test (in later chapters) the hypothesis that a good organisational
climate can predict improved organisational performance. Table 5.3.1 sets out the
overall profile of the 14 participating hotels that were divided into four size groups:
100-199, 200-299, 300-399 and 400+ rooms, and that the RACQ rating was between
four and five stars.
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Table 5.3.1 Hotel Operational Statistics
REVPARRank
HotelCode
RoomNos.
RACQStars
RackRate $
ADRR$
Occup.%
REVPAR$
1 SC � 2 100-199 4.5 344.00 216.25 70.10 151.59
2 B - 9 100-199 5 270.00 166.21 74.70 124.16
3 GC � 1 400+ 4.5 170.00 129.50 74.73 96.77
4 GC � 3 300-399 5 280.00 132.01 68.00 89.76
5 B - 6 200-299 4 190.00 107.00 80.50 86.13
6 GC � 11 300-399 5 215.00 125.50 62.30 78.19
7 GC � 4 400+ 4.5 215.00 110.01 71.00 78.11
8 SC � 14 300-399 4 265.00 120.50 64.20 77.36
9 B - 8 100-199 4.5 180.00 102.86 72.60 74.68
10 B - 13 400+ 4.5 279.00 109.81 66.70 73.24
11 GC � 5 300-399 4.5 300.00 120.50 60.01 72.42
12 SC � 10 200-299 4 220.00 108.86 59.80 65.10
13 B - 7 300-399 4.5 200.00 121.86 52.23 63.29
14 GC � 12 200-299 4 225.00 88.80 70.10 62.25
Means 239.50 125.69 67.64 85.22GC = Gold Coast, B = Brisbane, SC = Sunshine Coast
The standard published tariff (Rack Rate) when compared to the average daily
room rate (ADRR) shows that there is little relationship except that ADRR is always
substantially less, with a mean across the sample showing it at $125.90 or 52.48% of the
quoted rack rate.
Table 5.3.1 showed quite large differences between the published Rack Rates
and ADRR for each of the hotels in this sample. The question must be asked �why
publish such a rate�? It is obviously an ambit claim but it does provide a starting point
that hotel marketing executives can use to negotiate the real rates that they are likely to
110
achieve. Occasionally some guests will be charged this rate if demand exceeds supply at
particular times. The rack rate also serves other purposes such as positioning the hotel in
the market place and to some more naïve guests it can be used as the basis to offer
discounts which attracts their business.
For the purposes of this research, how the hotel performs with respect to its
rooms will be judged by its ADRR and occupancy percentage which when combined
produces REVPAR (Table 5.3.1). There are 2 hotels that stand out in terms of ADRR
(hotels 2 and 9) and when their ADRR is combined with their occupancy it produces the
2 best REVPAR figures of $151.59 and $124.16 respectively. The third place in terms
of REVPAR is $96.77 (hotel 1) with 2 other hotels (3 and 6) in the mid-to-high $80
range. The remaining 9 hotels REVPAR was less than $80, with the poorest
performance at $62.25 (hotel 12).
Occupancy percentages for all properties are regularly collected then aggregated
for regions or States and widely disseminated through government and state based
tourism bodies. Occupancy percentage information is the most widely used statistic
upon which to judge the health of the hotel industry. It is certainly true that a good level
of occupancy is required before a hotel can perform well in financial terms, but there is
also the situation were occupancy at any cost is not appropriate. The 2 lowest REVPAR
ranked hotels (hotels 7 and 12) have vastly differing occupancy percentages, 52.23%
and 70.10% respectively. Hotel 7 has retained a very respectable ADRR but at the cost
of occupancy, whereas hotel 12 has done the opposite by achieving good occupancy but
slashing its room rate, with it being the lowest of the sample by a considerable margin.
Table 5.3.2 details another element that feeds into the ADRR, that is, the mix of
market segments of the total business. These market segments are broken into a number
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of categories. The term FIT (free independent traveller) refers to a guest who does not
pre book but turns up at the hotel seeking accommodation and thus is most likely to pay
the full rack rate. Conference delegates are staying in the hotel and attending a
conference either in-house or in the area. Whilst the rate for the conference business is
generally one of the lowest, it has the advantage that the guests normally stay for
reasonable periods of time, viz., 5.6 nights for international delegates, 4.3 nights for
interstate delegates and 2.6 nights for Queensland delegates (QTTC, 1997).
Tour group business is normally the lowest rate (with the possible exception of
aircrew). It is based upon volume and repeatability, and normally subject to yearly
negotiation. These groups can be both domestic and inbound tourists but within the
sample hotels in this study the predominance is mainly inbound Asian groups.
Corporate business is very much dependent upon the location of the hotel. It can
be very high yielding because executives tend to want superior rooms. The hotels in this
sample are all in the price range that would suggest senior executives rather than the
lower level corporate employees. The one exception is the aircrew market whose
business is normally confined to major cities with airports - Brisbane and Coolangatta in
this sample. Although there is an airport at Maroochydore on the Sunshine Coast the
level of airline business generated is negligible. For aircrew contracts it is only the
major hotels that are considered by the airlines and they require the hotel manager to
trade off the daily room rate for a substantial number of guaranteed bed-nights on a
daily basis for the length of the contracted period. In general, this is the best room rate
offered by any major hotel and has the capacity to negatively affect the ADRR quite
considerably. Two hotels in the Brisbane sample (hotels 6 & 13) and one on the Gold
Coast (hotel 5) both have large aircrew contracts.
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Table 5.3.2 Rooms Business Mix
HotelCode
REVPARRank
F.I.T.
%
ConferenceDelegates
%
TourGroups
%
Corpor-Ate%
Govern-Ment
%
Leisure
%
SC � 2 1 80.00 12.00 2.00 5.00 1.00
B - 9 2 13.50 1.50 55.50 9.50 20.00
GC - 1 3 44.00 4.60 38.40 1.80 11.20
GC - 3 4 17.50 36.70 25.20 3.20 17.40
B - 6 5 8.00 5.00 8.00 54.00 7.00 18.00
GC - 11 6 8.00 12.00 51.00 6.00 5.00 18.00
GC - 4 7 25.00 4.00 71.00
SC � 14 8 67.70 25.90 1.90 4.50
B - 8 9 8.00 10.00 20.00 44.00 8.00 10.00
B - 13 10 11.70 8.30 14.30 50.90 10.50 4.30
GC - 5 11 3.00 10.00 45.00 5.00 1.00 36.00
SC � 10 12 60.80 8.20 23.50 7.50
B - 7 13 5.00 16.00 59.00 11.00 9.00
GC - 12 14 81.60 3.20 15.20
GC = Gold Coast, B = Brisbane, SC = Sunshine Coast
The last 2 categories are government and leisure. Generally the government rate
will be somewhere between the conference and corporate rate, whilst the leisure rate
will be the second highest level of normal rates to the rack rate. These are normally pre-
booked and are often accommodation, holiday or short break packages put together by
the hotel companies.
By examining Table 5.3.2 it is evident that not all the sample hotels are in all
market sectors. Despite detailed discussions with the management of the hotels in the
pre-testing processes to ensure the common usage and understanding of the terminology
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for the market sectors, it is clear that not all hotels use the market segments descriptions
provided above. In particular, hotel 2 and hotel 10 have used the FIT category to
account for both FIT and the leisure segments. As they are both high yielding market
segments it has no major effect on the analysis and just reflects these hotels� individual
reporting system. Hotels 1, 4 and 14 also had similar difficulties with splitting their
market share of FIT and leisure.
It is of interest to note that a high level of FIT business does not guarantee good
REVPAR. Hotels 2 and 10 are at different ends of the REVPAR ranking but both have a
high level of FIT. The hotel that was most reliant upon tour group business also returned
the poorest REVPAR ranking (hotel 14). Unless the hotel was able to negotiate an
extremely advantageous contract in terms of room rate such a high percentage of tour
group business will always lead to a poor performance, judged by REVPAR, despite
achieving a reasonable occupancy 70.10%.
The corporate level of business shown was concentrated, as would be expected,
in the Brisbane hotels (6, 7, 8, 9 and 13). This pattern, to a much lesser extent, was also
repeated for the business generated from government organisations.
Table 5.3.3 displays additional information covering revenue generated by
rooms, food and beverage (F&B) and other sources. It also gives 2 key employment
statistics of employee turnover percentage and wage cost as a percentage of revenue.
The percentage of revenue gained from rooms and F&B reflect the type and style of the
operation. The highest figure for room revenue is hotel 12 with 77.7% and the highest
for F&B is hotel 1 with 60.00%. There is no discernible pattern linking the percentage
of room or F&B revenues with the REVPAR ranking.
114
It is worth noting that the hotels which reported significant other revenue, 3, 10
and 14 are all in locations that are either sport or eco-tourism oriented and not in a main
coastal resort or city locations. Their attraction to the customers lies in the very range of
other services in addition to rooms and F&B. To an extent, they tend to offer a more
self-contained package with customers not needing to leave the resort during their stay.
These hotels were also evenly spread in terms of their REVPAR returns.
Table 5.3.3 Hotel Revenue Mix Percentages and Key Employment Percentages
HotelCode
REVPARRank
Rooms%
F&B%
Other%
Employeeturnover%
Wages to Revenue%
SC - 2 1 57.00 36.00 7.00 33.00 29.00
B - 9 2 70.00 26.00 4.00 5.00 32.50
GC - 1 3 26.00 60.00 14.00 not given not given
GC - 3 4 31.40 35.60 33.00 32.30 49.70
B - 6 5 55.00 39.00 6.00 38.00 30.00
GC - 11 6 60.00 34.00 6.00 30.00 34.00
GC - 4 7 75.00 25.00 9.40 39.44
SC - 14 8 45.60 26.20 28.20 38.00 36.60
B - 8 9 59.00 39.00 2.00 not given 34.00
B - 13 10 55.70 41.10 3.20 49.90 37.90
GC - 5 11 49.00 46.00 5.00 not given 38.00
SC - 10 12 29.70 31.40 38.90 103.00 38.30
B - 7 13 71.00 23.00 7.00 30.00 35.00
GC - 12 14 77.70 15.10 7.20 not given 37.07
Means 54.44 34.10 12.42 36.86 36.27GC = Gold Coast, B = Brisbane, SC = Sunshine Coast
In examining the key employment percentages (Table 5.3.3) there are some
115
obvious gaps where individual hotels would not release these figures although having
previously agreed to do so. It was always known that these are very sensitive figures
and thus closely guarded (comments from the expert panel). The hotels were asked on 2
separate occasions to provide the figures but declined. A range of reasons given were
�we have had a change in management personnel which has adversely affected this
year�s figure� (anon); �the executive committee has changed its mind� (anon); and �we
have had a policy change� (anon). Three of the hotels that did not supply the
employment turnover percentages were ranked poorly in their REVPAR performance
(hotels 5, 8 & 13). Only hotel 1 of these non-returns was ranked highly in the REVPAR
performance indicator.
In addition to the non-responses, hotels 4 and 9 returned figures of less than 10%
for employee turnover. Hotel 9 has a relatively small full time staff of 55 casual staff
running a city centre property that is quite new and features an all suite accommodation
layout. Upon questioning, the hotel said the figure was based upon their full time staff
and did not include the management and that 5% was correct. It can be seen that this
particular hotel (9) is deriving a high proportion of its revenue from rooms. They have a
policy of trying to maximise the use of casuals in both the housekeeping and restaurant
areas. When hotel 4 was approached about its low staff turnover figure (9.4% - they
employ 130 full time and 88 casual) they also responded by saying it excluded casual
(all hotels excluded casuals in turnover statistics). Judged in the light of the other
returns from similar hotels the figure for these two hotels is very low and some level of
doubt must exist on its accuracy.
The general industry standard levels for employee turnover discussed by the
expert panel was that 20-30% per year would be very good but that 30-40% was more
likely to be the norm. It was noted, however, that this often rose very substantially in
isolated and remote locations. As can be seen in Table 5.3.3, 6 hotels reported their
employee turnover in the range of 30% to 40%. Two hotels were, however, higher by a
large margin, with hotel 10 with 103% and hotel 13 with 49.9%. Hotel 10 is situated in
116
an isolated offshore location and its turnover figure is similar to other isolated hotels
but, of course, that does not diminish the problem posed by such a turnover level. It also
performed poorly in the REVPAR rating. Hotel 13 is in a city centre location and a
figure of almost 50% must give rise to concern in terms of operational efficiency and
management effectiveness. This hotel was also poorly rated by REVPAR.
The last column in Table 5.3.3 provides another critical employment statistic,
that of wages cost to total revenue. In any service industry, such as hotels, and
especially the four to five star hotel category, this percentage is often indicative of the
level of profitability. The expected range for such hotels is 30% to 40% (Howarth,
1995). Of course this figure is dependent upon the revenue generated as much as it is on
staff pay levels. There has been a significant shift in the pattern of employment over the
last decade toward higher levels of casual employment in the hotel industry (Timo,
1993). Because of the huge variability in demand, hotel companies are increasingly
seeking to use labour as a variable cost rather than a fixed cost. As stated above, hotel 9
employs 50% of its workforce on a casual basis and produced one of the best wages to
revenue percentages.
Most of the percentages are grouped in the mid-to-high 30�s that indicates an
acceptable range for the categories of hotels. There is one exception, however, with a
return of a 49% wages cost to revenue (hotel 3). This must indicate a major imbalance
in wages cost. In terms of its REVPAR performance it was fourth and it also uses a 50%
casual staffing complement. The other data from this hotel suggests that it is not
performing reasonable well but such a high percentage of wage cost must make the
hotel unprofitable. Subsequent to the collection of the data in this particular hotel
several senior management changes have taken place with some 30 staff redundancies
and a major organisational restructure has occurred which would tend to support the
assumption that they were not trading profitability.
117
Table 5.3.4 Comments by hotels on what affected trading conditions
Hotel Code Comments
01 Undertaking room refurbishment02 Undertaking room refurbishment03 Very variable international visitor numbers04 Increase in room supply but no increase in demand05 Slow down in Japanese and Asian markets and opening new 110 room
block06 New hotel and location on South Bank07 Rate drop by major city centre competitors08 Oversupply of Brisbane city rooms and weak commercial growth09 Lack of confidence in marketplace and rate dumping by five star
Brisbane competitors10 Changing market trends and internal organisational cultural change and
restructuring process11 Asian downturn, QTTC ineffectiveness and rate cutting by new hotels12 Downturn in Gold Coast visitor numbers which causes the four and five
star hotels reducing rates which impacts on whole market13 Asia market downturn14 Payroll tax and worker�s compensation payments far too high. Six
retrenchments because of wage increase through enterprise bargain
Table 5.3.4 shows the individual comments made by the managements of the
participating hotels when they were asked what internal/external factors affected their
operations during the year. By far the largest group of comments is related to markets
and marketing - 9 in total. Such concern with the markets and marketing is supported by
leading services marketing theorist�s (Ziethaml, Berry & Parasuraman, 1988) and many
operational management theorists that see the integration of marketing with operations
as the very key to financial success (Slack, Chambers, Harland, Harrison and Johnston,
1995). In simple terms, without attracting a sufficient number of customers, no
operation, especially a hotel can survive.
The second most frequent group of comments were those that related to
competition in rates or rate dumping (the practice of dropping room rates substantially
118
to ensure business). This is one of the first management strategies that some hotels seek
to utilise when there are difficult trading conditions. Such a strategy impacts upon
employees and employers because it has the consequence of reducing the wage cost, by
making people redundant, and increasing the use of casuals who are only used when the
business can support their use.
If the larger hotels cut rates they will draw business from the smaller hotels
unless they also match or go lower. Whilst it can be argued that this is �the market�
working at its most efficient, when a large number of jobs and percentage of capital
investment is involved this is often a less than satisfactory mechanism. It is not for this
thesis to discuss the merits or otherwise of open market economic systems but even in
this small sample it is apparent how dramatic the consequences are for employees and
investors when such circumstances occur.
Four of the hotels mentioned upgrades to their accommodation or being �new� in
the market place. Only 2, however, were concerned about new hotels opening and
leading to an over supply situation in hotel rooms. One comment was directed toward
the employment regulations and one to a management and cultural change process. The
hotel that mentioned the management and cultural issues is the hotel with the largest
employee turnover percentage.
Staff level data
Table 5.4.1 shows the response rate for the survey of the 14 hotels. Several
points need to be made with regard to its interpretation. Firstly, the majority of the
questionnaires were distributed to all staff in each hotel regardless of their employment
status on the one day. Only a very small number of hotels used 2 days. The distributed
number of questionnaires does not equate to the total number of employees of all
119
employment modes for each hotel but merely the employees that were present at the
time of distribution. A full description of the procedure was provided in chapter 3.
Table 5.4.1 Response rate for the survey of the 14 hotels
Hotel Code Distributed ReturnedResponse
Percentage%
01 1400 565 4002 180 132 7303 590 278 4704 155 56 3605 250 91 3606 160 47 2907 90 47 5208 95 45 4709 72 37 5110 250 87 3411 200 88 4412 70 32 4513 282 198 7014 240 75 31
Total 4034 1778 44
The average response rate achieved was 44% from 4034 questionnaires
distributed over 14 hotels in the survey area. The range of returns saw a spread of
response rates from 29% for hotel 6 through to 73% for hotel 2.
As shown in Table 5.4.2 there is a relatively even split between the genders of
the employees with females at four percentage points ahead of males. However the
picture dramatically changes when the managers� profile is examined, with males
exceeding female managers by 31 %. Whilst this gender spilt may well reflect most
industries, it again reinforces the fact that women are under represented at the senior
levels of four and five star hotels.
120
Table 5.4.2 Gender � employees and managers
Employees Frequency %*Male 835 48.0
Female 906 52.0
Total 1792 100.0
ManagersMales 95 65.5
Females 50 34.5
Total 143 100.0
*Percentages were calculated after missing or invalid responses (in this case 2.8%of employees and 0.0% of managers) were excluded.
Table 5.4.3 shows the gender makeup of employees for each of the 14 hotels in
the sample. To examine whether gender mix varied significantly between the hotels a
contingency table analysis was conducted on the data presented in Appendix D.
An important consideration in the calculation of the Chi-square statistic for
contingency table analysis is the magnitude of the expected frequency for each of the
cells (Mason, Lind and Marchal, 1998). The expected frequencies are in the
denominator for the calculation of chi-square. If the expected frequency for any cell is
quite low, the value contributed to the final chi-square value may be disproportionately
large and possibly result in a type I error (inappropriate rejection of the null hypothesis).
When the expected frequency is too low in one or more cells, Mason et. al. (1998)
propose combining several adjacent cells.
Howell, (1997) states that it is still open to question precisely how small is �too
small� for an expected frequency. He reports that the most common convention to deal
with this problem is to require that all expected frequencies should be at least 5. This is
a conservative position that he occasionally violates. Howell reported a computer
simulation conducted by Bradley et al., (1979, cited in Howell, 1997) using tables
121
ranging in size from 2 x 2 to 4 x 4. It found that for the sorts of problems that would
actually arise in practice, the actual proportion of type I errors rarely exceeds .06.
Bearing in mind, first, that the number of cells in the tables presented in the following
sections are greater than those used in the simulation, the possibility of a type I error
should be even lower than reported by Bradley et al., (1979, cited in Howell, 1977).
This occurs in an individual cell that will have a smaller impact on the final chi-square
value when there is a larger total number of cells in the analysis. Secondly, when
collapsing adjacent cells for the analysis, this must be conducted in a manner that will
produce a comparison that is meaningful. The following convention was applied. A
contingency table analysis was conducted to establish the number of cells with expected
values less than 5. In analyses where the expected value of one or more cells was less
than 5, adjacent cells were collapsed in a meaningful way and the analysis re-run. If in
this analysis there were still cells with an expected frequency less than 5, adjacent cells
were again collapsed � provided this could be done in a meaningful way � and the
analysis was re-run. This process continued until either all the cells had expected
frequencies that were less than 5, or further collapsing of cells would result in an
inappropriate aggregation of categories.
122
Table 5.4.3 Gender of employees for each of the 14 hotels
Male Female Row TotalHotel 1 290
52.7%260
47.3%550
31.6%2 55
45.5%66
54.5%121
7.0%3 128
46.7%146
53.3%274
15.7%4 29
53.7%25
46.3%54
3.1%5 45
51.7%42
48.3%87
5.0%6 20
42.6%27
57.4%47
2.7%7 20
40.8%29
59.2%49
2.8%8 9
19.6%37
80.4%46
2.6%9 15
38.5%24
61.5%39
2.2%10 45
52.3%41
47.7%86
4.9%11 39
43.3%51
56.7%90
5.2%12 14
43.8%18
56.3%32
1.8%13 89
46.4%103
53.6%192
11.0%14 34
50.0%37
50.0%74
4.3%Column
Total835
48.0%906
52.0%1741
100.%
Table 5.4.3 shows a great range in the gender balance between the hotels in the
sample. This ranges from equal number of each gender in hotel 14, to 80.4% female in
hotel 8. The level for females employed are not reflected by males in any hotel. The
greatest proportion of male employees occurred for hotel 4 with 53.7% of the
employees being male. In the contingency table analysis of gender across the 14 hotels,
no cell had an expected frequency less than 5. The minimum expected frequency was
15.35 (Appendix D). The analysis found that gender mix varied significantly across the
14 hotels (χ2(13) = 26.49, p < .05).
123
Table 5.4.4 shows the age profile of employees and managers. Whilst there are
very few real insights to be gleaned from the age profile data, these figures reinforce the
fact that the majority of employees are in the categories 15 - 24 and 25 - 34 years of age
and that the split is fairly even between the 2 age groups. It may well be that the
industry should consider recruiting from the older segments of the working population
that seem relatively under represented. An advantage of employing older employees is
that they have a lesser propensity to change jobs frequently.
The management age profiles show that most managers are in the 25 � 34 years
of age. To an extent the large numbers of managers in that bracket does provide
encouragement for young employees who are seeking to move up in the organisation, as
achieving the level of a manager becomes a very attainable career goal. It also reflects
the fact that there is also a very high turnover of managers.
Table 5.4.5 shows the age profile of employees at each of the hotels. An initial
contingency table analysis found 28 cells (33.3%) to have expected frequencies less
than 5, with a minimum expected count of .06 (Appendix D). The age categories were
collapsed from 6 down to 4 (15-24, 25-34, 35-44, and over 45 years). When the analysis
was re-run, one cell (1.8%) had an expected frequency less than 5, and the minimum
expected frequency for this cell was 4.81.
124
Table 5.4.4 Age profile of employees and managers
Employees Frequency %*15 � 24 years 528 30.5
25 � 34 603 34.8
35 � 44 340 19.6
45 � 54 212 12.2
55 �64 45 2.6
65 + 3 .2
Total 1792 100.0Managers15 � 24 years 3 2.1
25 � 34 58 40.0
35 � 44 55 37.9
45 � 54 22 15.2
55 � 64 6 4.1
65 + 1 .7
Total 145 100.0
*Percentages were calculated after missing or invalid responses (in this case 3.4%
of employees and 0.0% of managers) were excluded.
The pattern of a young employee profile exists across all of the hotels in this
sample. The largest proportion of employees in the over 45 years category occurs for
hotel 3 with 21.1%. Although a number of other hotels (11 and 12) approach this figure,
the proportion of employees in this category represents only 2.6% of the employees of
hotel 9. This contingency table analysis found the pattern of distribution of employee
ages to significantly vary between hotels (χ2(39) = 87.94, p < .001).
125
Table 5.4.5 Age profile of employees for each of the 14 hotels
15-24yrs 25-34yrs 35-44yrs 45+yrs RowTotal
HOTEL 1 176 174 105 90 54532.3% 31.9% 19.3% 16.5% 31.5%
2 43 46 18 10 11736.8% 39.3% 15.4% 8.5% 6.8%
3 64 96 57 58 27523.3% 34.9% 20.7% 21.1% 15.9%
4 13 26 10 5 5424.1% 48.1% 18.5% 9.3% 3.1%
5 39 27 12 9 8744.8% 31.0% 13.8% 10.3% 5.0%
6 16 14 13 3 4634.8% 30.4% 28.3% 6.5% 2.7%
7 20 19 4 5 4841.7% 39.6% 8.3% 10.4% 2.8%
8 14 19 7 7 4729.8% 40.4% 14.9% 14.9% 2.7%
9 8 19 11 1 3920.5% 48.7% 28.2% 2.6% 2.3%
10 29 29 16 13 8733.3% 33.3% 18.4% 14.9% 5.0%
11 17 28 28 17 9018.9% 31.1% 31.1% 18.9% 5.2%
12 11 11 4 6 3234.4% 34.4% 12.5% 18.8% 1.8%
13 66 71 28 25 19034.7% 37.4% 14.7% 13.2% 11.0%
14 12 24 27 11 7416.2% 32.4% 36.5% 14.9% 4.3%
Column 528 603 340 260 1731Total 30.5% 34.8% 19.6% 15.0% 100.0%
Table 5.4.6 shows the educational level of employees and managers. The general
pattern of qualifications for the employees follows a predictable pattern of falling
numbers with increasing qualification level. Over 60% of employees have qualifications
at the post-secondary level and above. This figure certainly supports the view that the
industry workforce is relatively well qualified.
126
Table 5.4.6 Educational level of employees and managers
Employees Frequency %*Secondary 601 35.1
Post-secondary 261 15.3
Apprenticeship 233 13.6
Assoc. Dip 279 16.3
Degree 284 16.6
Post Grad. 53 3.1
Total 1792
Managers
Secondary 29 20.3
Post-secondary 14 9.8
Apprent�ship 23 16.1
Assoc. Dip 36 25.2
Degree 33 23.1
Post grad. 8 5.6
Total 145
*Percentages were calculated after missing or invalid responses (in this case 4.5%of employees and 1.4% of managers) were excluded.
The management returns show an encouraging pattern with the largest
percentage of respondents having associate diploma level qualifications, at 24.8%, and
that figure being closely followed by degree qualifications at 22.8%. Only 20% of this
sample had no post secondary qualifications. It was not the aim of this study to examine
qualification patterns in the industry but it would be most interesting to analyse this area
further to see how many managers, and at what levels, have hospitality or related
degrees.
Table 5.4.7 shows the educational level of employees for each of the 14 hotels in
the sample. An initial contingency table analysis found 13 cells (15.5%) to have an
expected frequency less than 5, with the minimum expected frequency to be 0.96
(Appendix D). The education level categories were collapsed from 6 to 5 (secondary,
post-secondary, apprenticeship, associate diploma, degree and postgraduate) and the
127
analysis re-run. Two cells were found to have expected frequencies of less than 5. The
minimum expected frequency was 4.20.
This contingency table analysis found Education Level to vary significantly
across the hotels in the sample (χ2(52) = 74.17, p < .05).
Table 5.4.7 Educational level of employees for each of the 14 hotels
Secondary PostSecondary
Apprent-iceship
Ass.Diploma
Degree &Post-
RowTotal
Hotel 1 200 82 81 75 100 53837.2% 15.2% 15.1% 13.9% 18.6% 31.4%
2 51 11 22 19 15 11843.2% 9.3% 18.6% 16.1% 12.7% 6.9%
3 106 39 36 40 50 27139.1% 14.4% 13.3% 14.8% 18.5% 15.8%
4 15 9 9 12 7 5228.8% 17.3% 17.3% 23.1% 13.5% 3.0%
5 34 11 8 15 20 8838.6% 12.5% 9.1% 17.0% 22.7% 5.1%
6 10 4 8 10 13 4522.2% 8.9% 17.8% 22.2% 28.9% 2.6%
7 17 6 5 9 12 4934.7% 12.2% 10.2% 18.4% 24.5% 2.9%
8 21 9 2 5 8 4546.7% 20.0% 4.4% 11.1% 17.8% 2.6%
9 17 5 3 4 10 3943.6% 12.8% 7.7% 10.3% 25.6% 2.3%
10 24 17 8 16 21 8627.9% 19.8% 9.3% 18.6% 24.4% 5.0%
11 26 16 16 17 13 8829.5% 18.2% 18.2% 19.3% 14.8% 5.1%
12 6 9 3 7 6 3119.4% 29.0% 9.7% 22.6% 19.4% 1.8%
13 49 30 17 40 50 18626.3% 16.1% 9.1% 21.5% 26.9% 10.9%
14 25 13 15 10 12 7533.3% 17.3% 20.0% 13.3% 16.0% 4.4%
Column 601 261 233 279 337 1711Total 35.1% 15.3% 13.6% 16.3% 19.7% 100.0%
Table 5.4.8 gives details of the organisational tenure for employees and
managers. Timo (1993) in discussing employee turnover suggested that there were some
benefits that offset the many negatives of high employee turnover. One benefit was the
128
introduction of employees with fresh attitudes and approach. There is, however, an
overwhelming preponderance of evidence that employee turnover must be a
considerable cost to hotels, with loss of quality and efficiency in operations, involving
continual re-training and management time. Having 58.5% of the staff being with the
organisation for 2 years or less very much reinforces the relatively high employee
turnover that has become endemic within the Australian hotel industry.
The management figures also display a similar pattern in the first 2 categories,
but there is a notable change in the 6 to 8 years with a much larger number staying on
for that period. It is company policy in many of the major national and international
hotel chains that managers should be rotated quite frequently. Black (personal
communication, 8th April 1999) supports this, he recounted that Sheraton see a 3-year
tenure of senior management being the normal maximum. He further stated that senior
management changes always had a considerable impact upon the staff within a hotel
and they were by no means always beneficial.
Table 5.4.8 Organisational tenure for employees and managers
Employees Frequency %*0 - 2 years 1017 56.53 � 5 403 23.26 � 8 186 10.79 � 11 95 5.512 � 14 27 1.615 � 17 11 .6Total 1792 100.0Managers0 � 2 70 48.33 � 5 36 24.86 � 8 26 17.99 � 11 9 6.212 � 14 4 2.8Total 145 100.0*Percentages were calculated after missing or invalid responses (in this case 3.0%of employees and 0.0% of managers) were excluded.
Table 5.4.9 gives details of the organisational tenure for employees for each of
129
the 14 hotels in the sample. An initial contingency table analysis found 40 cells (47.6%)
to have an expected frequency less than 5, with the minimum expected frequency to be
.20 (Appendix D). The organisational tenure categories were collapsed from 6 to 3 (0-2,
3-5, and over 6 years) and the analysis re-run. No cells were found to have expected
frequencies of less than 5. The minimum expected frequency was 5.87.
Although the hotels display large numbers of employees with an organisational
tenure of 2 years or less (with the smallest number falling into this category being hotel
14 with 20% of employees). Four hotels had 20% or more of their employees having
been with the hotel for 6 or more years (hotels 1, 11, 12 and 14, with 27.9%, 23.3%,
25.0% and 20.0%, respectively). This contingency table analysis found organisational
tenure to vary significantly across the hotels in the sample (χ2(26) = 158.88, p < .001).
130
Table 5.4.9 Organisational tenure for employees for each of the 14 hotels
0-2yrs 3-5yrs 6+yrs RowTotal
Hotel 1 268 124 152 54449.3% 22.8% 27.9% 31.3%
2 79 23 19 12165.3% 19.0% 15.7% 7.0%
3 152 78 44 27455.5% 28.5% 16.1% 15.8%
4 27 20 8 5549.1% 36.4% 14.5% 3.2%
5 58 22 8 8865.9% 25.0% 9.1% 5.1%
6 40 5 2 4785.1% 10.6% 4.3% 2.7%
7 46 1 2 4993.9% 2.0% 4.1% 2.8%
8 32 9 5 4669.6% 19.6% 10.9% 2.6%
9 38 0 1 3997.4% 0.0% 2.6% 2.2%
10 59 28 0 8767.8% 32.2% 0.0% 5.0%
11 47 22 21 9052.2% 24.4% 23.3% 5.2%
12 19 5 8 3259.4% 15.6% 25.0% 1.8%
13 122 36 34 19263.5% 18.8% 17.7% 11.0%
14 30 30 15 7540.0% 40.0% 20.0% 4.3%
Column 1017 403 319 1739Total 58.5% 23.2% 18.3% 100.0%
Table 5.4.10 focuses upon the length of time employees and managers have been
in particular jobs. The data for the employees, of course, shows that when compared
with the results for organisational tenure, a higher percentage have held their jobs for 2
years or less (65.7%). The percentage of those in the 3 � 5 year category was 20.7%
giving a cumulative percentage for these two-year bands of 86.4%, almost 5% up on the
equivalent organisational tenure figure. This is even stronger confirmation of the
shortness in time that employees are spending in their jobs and the comments made for
organisational tenure above are reinforced. For the hotel industry in this sample these
figures indicate a significant operational impediment.
131
When examining the managers� data an even more dramatic picture emerges
with 64.8% and 22.1% for the first 2 categories. To some extent this may be seen as
positive because it opens up career path opportunities for ambitious staff but the quality
and depth of their understanding and experience in dealing with people, both customers
and employees, is very limited. In an industry based upon quality service this presents
problems in consistency of the service offered.
Table 5.4.10 Job tenure for employees and managers
Employees Frequency %*0 - 2 years 1134 65.73 � 5 358 20.76 � 8 122 7.19 � 11 79 4.612 � 14 19 1.115 � 17 14 .8Total 1792 100.0Managers0 - 2 years 94 64.83 � 5 32 22.16 � 8 12 8.39 � 11 2 1.412 � 14 5 3.4Total 145 100.0*Percentages were calculated after missing or invalid responses (in this case 3.7%of employees and 0.0% of managers) were excluded.
Table 5.4.11 gives details of the job tenure for employees for each of the 14
hotels in the sample. An initial contingency table analysis found 43 cells (51.2%) to
have an expected frequency less than 5, with the minimum expected frequency to be .25
(Appendix D). The job tenure categories were collapsed from 6 to 3 (0-2, 3-5, and over
6 years) and the analysis re-run. One cell was found to have an expected frequency of
less than 5, and the minimum expected frequency for this cell was 4.20.
132
Table 5.4.11 Job tenure for employees for each of the 14 hotels
Tenure 0-2yrs Tenure 3-5yrs Tenure 6+yrs RowTotal
Hotel 1 312 118 109 53957.9% 21.9% 20.2% 31.2%
2 85 23 12 12070.8% 19.2% 10.0% 7.0%
3 173 70 29 27263.6% 25.7% 10.7% 15.8%
4 29 17 7 5354.7% 32.1% 13.2% 3.1%
5 60 22 5 8769.0% 25.3% 5.7% 5.0%
6 40 4 3 4785.1% 8.5% 6.4% 2.7%
7 46 0 3 4993.9% 0.0% 6.1% 2.8%
8 37 4 5 4680.4% 8.7% 10.9% 2.7%
9 35 1 2 3892.1% 2.6% 5.3% 2.2%
10 66 20 1 8775.9% 23.0% 1.1% 5.0%
11 56 19 15 9062.2% 21.1% 16.7% 5.2%
12 21 3 7 3167.7% 9.7% 22.6% 1.8%
13 137 32 23 19271.4% 16.7% 12.0% 11.1%
14 37 25 13 7549.3% 33.3% 17.3% 4.3%
Column 1134 358 234 1726Total 65.7% 20.7% 13.6% 100.0%
The four hotels with the largest proportion of employees falling into the 6 years
and over tenure category (hotels 1, 11, 12, 14) are the same four hotels with the largest
proportion of their employees falling into the 6 years and over job tenure category
(20.2%, 16.7%, 22.6%, and 17.3%). What is interesting is the range in size of these 4
hotels which range in size from 31 employees (hotel 12) to 539 (hotel 1). This
contingency table analysis found job tenure to vary significantly across the hotels in the
sample (χ2(26) = 110.37, p < .001).
133
In table 5.4.12 the reported gross salary for employees and managers is given. At
first examination it confirms that the hotel industry is indeed a low paying employer
with 85.8% of employees earning less than $30,000 per year. These figures, however,
should be read in conjunction the mode of employment (Table 5.4.7) and hours worked
data (Table 4.4.8) to assess how many employees are full-time and how many are part-
time or casual. Notwithstanding the other data it is very notable that an extremely small
percentage of staff earn in excess of $36,000 (5.5%). It is an inescapable conclusion that
the hotels in this study are not paying what would be considered an attractive salary,
especially to a single wage family.
The majority of managers earn less than $50,000, although a few earn $100,000
or more.
Table 5.4.12 Gross salary for employees and managers
Employees $ Frequency %*0 � 5,000 80 4.86 � 10,000 94 5.611 � 15,000 156 9.316 � 20,000 232 13.921 � 25,000 531 31.826 � 30,000 339 20.331 � 35,000 145 8.736 � 40,000 53 3.241 � 45,000 22 1.346 � 50,000 2 .1 50,000 + 15 .9Total 1792 100.0Managers30 � 39,000 42 30.040 � 49,000 42 30.050 � 59,000 26 18.660 � 69,000 13 9.370 � 79,000 5 3.680 � 89,000 3 2.190 � 99,000 1 .7100,000 + 8 5.7Total 145 100.0
*Percentages were calculated after missing or invalid responses (in this case 3.7%
of employees and 3.4% of managers) were excluded.
Table 5.4.13 gives details of the current gross salary for employees for each of
the 14 hotels in the sample. An initial contingency table analysis found 85 cells (55.2%)
134
to have an expected frequency less than 5, with the minimum expected frequency to be
.04 (Appendix D). The salary categories were collapsed from 11 to 3 ($0-20,000,
$21,000-30,000, and over $30,000) and the analysis re-run. One cell was found to have
an expected frequency of less than 5, and the minimum expected frequency for that cell
was 4.40. Although displaying an across the board trend of relatively low salaries for
employees, there exists a considerable variation between hotels. For example, within the
$31,000 and greater category, the proportion of staff falling into this category ranges
from 3.2% for hotel 12 to 28.0% for hotel 4. Both of these hotels had relatively small
numbers of employees with 31 and 50 respectively.
Table 5.4.13 Gross salary for employees of the 14 hotels
$0-20,000
$21,000-30,000
over30,000
RowTotal
Hotel 1 173 272 77 52233.1% 52.1% 14.8% 31.3%
2 26 70 21 11722.2% 59.8% 17.9% 7.0%
3 104 128 29 26139.8% 49.0% 11.1% 15.6%
4 11 25 14 5022.0% 50.0% 28.0% 3.0%
5 29 45 7 8135.8% 55.6% 8.6% 4.9%
6 15 24 7 4632.6% 52.2% 15.2% 2.8%
7 14 26 6 4630.4% 56.5% 13.0% 2.8%
8 22 20 2 4450.0% 45.5% 4.5% 2.6%
9 9 25 2 3625.0% 69.4% 5.6% 2.2%
10 33 46 7 8638.4% 53.5% 8.1% 5.2%
11 18 49 21 8820.5% 55.7% 23.9% 5.3%
12 5 25 1 3116.1% 80.6% 3.2% 1.9%
13 73 84 32 18938.6% 44.4% 16.9% 11.3%
14 30 31 11 7241.7% 43.1% 15.3% 4.3%
Column 562 870 237 1669Total 33.7% 52.1% 14.2% 100.0%
135
This contingency table analysis found the pattern of gross salary of employees to
vary significantly across the hotels in the sample (χ2(26) = 65.89, p < .001).
Table 5.4.14 shows the data on mode of employment for the hotel employees,
with 60.6% being in full time employment. A relatively low percentage of 10.6% are
part time with casual employment being at 28.8% overall. If these figures are read in
conjunction with the employee salary it certainly confirms that the industry is not
paying its workers highly. This must contribute to the generally high levels of staff
turnover with the associated ramifications for any hotel�s operation and service quality.
All managers were employed on a full time basis.
Table 5.4.14 Mode of employment for employees
Employees Frequency %*
Full time 1041 60.6
Part time 183 10.6
Casual 495 28.8
Total 1792 100.0
*Percentages were calculated after missing or invalid responses (in this case 4.1%)were excluded.
Table 5.4.15 gives details of the mode of employment for employees for each of
the 14 hotels in the sample. An initial contingency table analysis found 4 cells (9.5%) to
have an expected frequency less than 5, with the minimum expected frequency to be
3.30 (Appendix D). The mode of employment categories was collapsed from 3 to 2
(full-time vs part-time and casual) and the analysis re-run. No cells were found to have
expected frequencies of less than 5. The minimum expected frequency was 12.23. A
considerable variation exists between the hotels regarding the proportion of employees
employed full-time, ranging from 50.6% (hotel 10) to 90.3% (Hotel 12). This
136
contingency table analysis found the pattern of mode of employment to vary
significantly across the hotels in the sample (χ2(13) = 67.942, p < .001).
Table 5.4.15 Mode of employment for employees for the 14 hotels
Full- time Part-time &Casual
RowTotal
Hotel 1 304 238 54256.1% 43.9% 31.5%
2 71 48 11959.7% 40.3% 6.9%
3 151 117 26856.3% 43.7% 15.6%
4 44 8 5284.6% 15.4% 3.0%
5 48 38 8655.8% 44.2% 5.0%
6 37 9 4680.4% 19.6% 2.7%
7 34 15 4969.4% 30.6% 2.9%
8 28 18 4660.9% 39.1% 2.7%
9 27 10 3773.0% 27.0% 2.2%
10 44 43 8750.6% 49.4% 5.1%
11 74 16 9082.2% 17.8% 5.2%
12 28 3 3190.3% 9.7% 1.8%
13 104 88 19254.2% 45.8% 11.2%
14 47 27 7463.5% 36.5% 4.3%
Column 1041 678 1719Total 60.6% 39.4% 100.0%
Table 5.4.16 shows the hours worked by employees and when this is linked to
both current gross salary and mode of employment it yet again demonstrates the
relatively low pay for the majority of employees. The single largest category is the 36 �
40 hours per week full time employment with 44.3% of the sample. Hours worked in
excess of 40 hours per week were relatively low percentages. No survey was conducted
on hours worked by managers.
137
Table 5.4.16 Hours worked by employees
Employees Frequency %*0 � 5 hours 8 .5
6 � 10 33 1.9
11 � 15 38 2.2
16 � 20 98 5.7
21 � 25 115 6.7
26 � 30 166 9.6
31 � 35 134 7.8
36 � 40 765 44.3
41 � 45 200 11.6
46 � 50 88 5.1
50 + 82 4.7
Total 1792 100.0
*Percentages were calculated after missing or invalid responses (in this case 3.6%)were excluded.
Table 5.4.17 gives details of the hours worked for employees for each of the 14
hotels in the sample. An initial contingency table analysis found 87 cells (56.5%) to
have an expected frequency less than 5, with the minimum expected frequency to be
0.15 (Appendix D). The hours worked categories were collapsed from 11 to 4 (0-25, 26-
35, 36-40, and over 40 hours) and the analysis re-run. No cells were found to have
expected frequencies of less than 5. The minimum expected frequency was 5.41.
138
Table 5.4.17 Hours worked by employees for the 14 hotels
0-25hrs
26-35hrs
36-40hrs
Over 40hrs
RowTotal
Hotel 1 8115.0%
11220.7%
27150.1%
7714.2%
54131.3%
2 1210.1%
3539.4%
4033.6%
3226.9%
1196.9%
3 6925.3%
3813.9%
10337.7%
6323.1%
27315.8%
4 713.0%
35.6%
2648.1%
1933.3%
543.1%
5 1719.5%
1314.9%
4147.1%
1618.4%
875.0%
6 510.9%
510.9%
1737.0%
1941.3%
462.7%
7 816.3%
714.3%
1938.8%
1530.6%
492.8%
8 919.6%
919.6%
2043.5%
817.4%
462.7%
9 718.9%
38.1%
1951.4%
821.6%
372.1%
10 1011.5%
1213.8%
3843.7%
2731.0%
875.0%
11 1112.2%
88.9%
5257.8%
1921.1%
905.2%
12 39.4%
26.3%
2268.8%
515.6%
321.9%
13 4121.5%
3920.4%
6634.6%
4523.6%
19111.1%
14 1216.0%
1418.7%
3141.3%
1824.0%
754.3%
Column 292 300 765 370 1727Total 16.9% 17.4% 44.3% 21.4% 100.0%
This contingency table analysis found the pattern of hours worked to vary
significantly across the hotels in the sample (χ2(39) = 113.45, p < .001).
139
Table 5.4.18 gives information on training frequency (expressed as time since
last training session) for both employees and managers.
Table 5.4.18 Time since last training session for employees and managers
Employees Frequency %*0 � 1 years 1199 72.11 � 2 238 14.32 � 3 99 6.03 � 4 40 2.44 � 5 29 1.75 � 6 11 .76� 7 47 2.8Total 1792 100.0Managers0 � 1 years 105 74.51 � 2 21 14.92 � 3 6 4.33 � 4 2 1.44 � 5 1 .75 � 6 2 1.46 � 7 4 2.8Total 145 100.0*Percentages were calculated after missing or invalid responses (in this case 7.2%of employees and 2.8% of managers) were excluded.
It is of note that 72.1% of employees have attended a training session within a
12-month period. If this figure is considered at face value, it presents a picture of a very
proactive training regime being implemented by the hotels. This view is reinforced
when one sees that within the last 3 years 92.4% have attended training. There is no
doubt that the training of staff in the hotel industry now receives a very high profile. In
Australian hospitality and tourism training under the auspices of TTA, which is a
government appointed ITAB (Industry Training Advisory Board), all training in this
industry sector has received a very high profile and been recognised as world�s best
practice.
These training frequency figures, however, must be set in the context of
employee turnover and job tenure figures which both indicated the high turnover levels
of employees. It is, therefore, a necessity to have this constant training program
established because of the sheer volume turnover of staff numbers. Obviously such a
140
high level of training also indicates that considerable resources are being expended not
for improvement but to remain competitive in service levels.
The data for the managers also demonstrate that it is the norm to attend training.
This confirms that there may well be a link to the relatively short organisational and job
tenure figures.
Table 5.4.19 gives details of the time since last training session for employees
for each of the 14 hotels in the sample. An initial contingency table analysis found 59
cells (60.2%) to have an expected frequency less than 5, with the minimum expected
frequency to be 0.21 (Appendix D). The time since last training categories were
collapsed from 7 to 3 and the analysis re-run. 3 cells were found to have expected
frequencies of less than 5. The minimum expected frequency was 4.35.
141
Table 5.4.19 Time since last training session for employees for the 14 hotels
0 - 1 yr 1 - 2 yr Over 2years
RowTotal
Hotel 1 36971.4%
7314.1%
7514.5%
51731.1%
2 8271.9%
1614.0%
1614.0%
1176.9%
3 16764.7%
5119.8%
4015.5%
25815.5%
4 4481.5%
713.0%
35.6%
543.2%
5 6677.6%
78.2%
1214.1%
855.1%
6 3884.4%
613.3%
12.2%
452.7%
7 3166.0%
1021.3%
612.8%
472.8%
8 3678.3%
613.0%
48.7%
452.8%
9 3085.7%
38.6%
25.7%
352.1%
10 4962.0%
1012.7%
2025.3%
794.8%
11 7383.0%
66.8%
910.2%
885.3%
12 2990.6%
13.1%
26.3%
321.9%
13 13571.8%
2814.9%
2513.3%
18811.3%
14 5066.7%
1418.7%
1114.7%
754.5%
ColumnTotal
119972.1%
23814.3%
22613.6%
1663100.0%
Although all the hotels demonstrate a high proportion of staff who have attended
a training session within the past 12 months (the lowest being hotel 10, with 62.% of
employees), a considerable range in patterns exist with hotel 6 reporting only 2.2% of
employees to have had their last training session over 2 years ago, to hotel 10 where the
figure was 25.3% of employees. This contingency table analysis found the pattern of
time since last training session to vary significantly across the hotels in the sample
(χ2(26) = 47.69, p < .01).
Table 5.4.20 shows the responses to the question of whether more training is
needed for both employees and managers. The result shows an interesting contrast with
142
the employees saying a very firm �No� to more training (61.6%), whereas the managers
had an almost overwhelming response the other way, with 72.5% wanting more
training.
The managers seem to be looking to training as a tool that will assist them in
their ever increasingly complex task of running a hotel.
Table 5.4.20 Employees and managers were asked, do you need training?
Employees Frequency %*
Yes 659 38.4
No 1056 61.6
Total 1792 100.0
Managers
Yes 103 72.5
No 39 27.5
Total 145 100.0
*Percentages were calculated after missing or invalid responses (in this case 4.3%of employees and 2.1% of managers) were excluded.
5.5 Summary and Discussion
In this chapter, first the general operating statistics were reported for the 14
hotels in our sample, and second a comprehensive presentation of the demographic data
from the managers and employees of our sample was reported.
The former data were important in providing a description of the organisations
under study, and reported important indices of hotel performance such as ADRR and
REVPAR. These data were relevant to this study as they provided the context and
143
framework within which the collection of the organisational climate information took
place. In order to interpret the organisational climate data with the more sophisticated
statistical techniques this context and framework of the study is important.
Specifically, the Hotel Level Data show hotels 2 and 9 to record the best
REVPAR figures, and that some hotels would appear to concentrate too much on
ADRR at the expense of occupancy e.g. hotel 7. Conversely, others concentrated upon
occupancy at the expense of ADRR, e.g. hotel 6. The lowest REVPARs were recorded
by hotel 7 and 12, with the latter hotel being very reliant upon tour groups in the room
revenue mix. Again, hotels 2 and 9 did well in the high yielding room revenue sectors of
FIT and corporate customers. The employee statistics of turnover and wages to sales
again reinforced that hotel 2 and 9 were performing well.
When managers were asked to make comments on any issues that affected their
performance the majority of comments were directed toward the marketing function. In
an increasingly competitive market, looking for the extra market share is vital in any
hotel�s performance.
In addition to the Hotel Level Data, demographic data were reported that were
gathered from individual staff members within the hotels (Staff Level Data). These data
are important for the current study as, firstly, when aggregated they provide a profile of
the employees across the 14 hotels, secondly, also when aggregated they provide a basis
for comparison of the different hotels, and thirdly, these variables are incorporated into
Structural Model A.
The overall response rate from staff members varied from 29% to 73 % with
average rate of 44% equating to 1778 respondents for the demographic information.
144
This has allowed a reasonable level of confidence in the results and their subsequent
analysis in conjunction with Organisational Climate data in later chapters.
As far as gender is concerned, the results show that whilst management is still
heavily biased toward males the actual workforce is much more evenly split with the
bias in favour of females. The workforce is concentrated in the under 35 years of age
range (65.3%) with the managers showing a slightly older profile with 67.9% in the 25
� 44 age bracket.
The educational qualification�s data show 65% of the employees with post-
secondary qualification and 28.7% of managers with undergraduate or post-graduate
qualifications. The training intervals show that the vast majority of both managers and
employees had attended training during the year (74.5% and 72.1% respectively). This
certainly presents a picture of an educated and trained workforce.
However, these figures need to be set against those for organisational tenure,
with employees at 56.5% and managers at 48.3% for less than 2 years service. This data
are compounded by the job tenure figures of less than 2 years (employees 65.7% and
managers 64.8%). It is the major problem for the industry with employee turnover norm
being in the 30-40% range. When the salary levels are examined, with 85.8% of staff
under $30,000 and 94.5% under $35,000, it provides an insight why the industry has
high employee turnover. This is further supported by the mode of employment figures
showing that full-time positions account for 60.6%, casuals 28.8% and part-time 10.6%.
The results in the need for training may well indicate that employees are getting
to the stage that further training could in fact be counter productive to the operation; it is
possible employees are attending as a requirement without any real motivation to learn
or improve. As hotels are spending large sums of money on training a more in depth
145
analysis of the type and nature of the training for employees may be warranted. The
completion of the training session feedback form may not be providing any real insights
as to the value of the training.
In addition to qualitative descriptions of the staff member demographic data,
quantitative inferential statistical comparisons were conducted to compare the 14 hotels
in this study on each of the demographic variables recorded from employees. These
were important comparisons for this study. A structural model had been proposed
(Structural Model A) which proposes that these demographic variables affect
Organisational Climate. For these variables to explain variation in Organisational
Climate between the hotels, it is a necessary condition for a variation in these
demographic variables between hotels. In each case it was found that staff across the 14
hotels significantly differed on each of these variables.
The dimensions of organisational climate from the sample will be investigated in
Chapter 6. Chapter 7 will investigate the relationships between REVPAR, employee
demographic variables, and the organisational climate.
146
6.0 The dimensions of Organisational Climate in 14 Australian
Hotels
6.1 Introduction
This chapter, firstly, will present a reliability analysis on the participants�
responses to the 70 items of the modified version of the Psychological Climate
Questionnaire (PCQ) used in this study. Secondly, an analysis of the underlying
dimensions of Organisational Climate within the sample will be conducted by applying
a type of Factor Analysis, Principal Components Analysis (PCA), to the participant
responses to the modified PCQ.
6.2 Reliability analysis
6.2.1 Approaches to the estimation of reliability of a test instrument
The term reliability, when referring to a psychological test instrument such as a
questionnaire, has been described as �the attribute of consistency in measurement�
(Gregory, 1996, p. 84). Gregory describes reliability as �best viewed as a continuum
ranging from minimal consistency of measurement (e.g., simple reaction time) to near
perfect reliability of results (e.g., weight)� (p. 84). The simplest method of determining
the reliability of test scores is the administration of the same test on 2 occasions to the
same set of respondents. In this situation, a perfectly reliable test would provide
identical responses for all respondents on both test occasions. In such a situation, the
correlating scores from the first administration with those of the second administration
would find a perfect correlation (r = 1.00). Should the instrument be �perfectly
unreliable� respondents would have different scores on the first administration with
147
respect to the second administration, and there would be no correlation between test
scores (r = 0.00).
The administration of this instrument on 2 occasions to the respondents was not
a practical approach given the constraints of the current study.
An approach at estimation of the reliability of an instrument that is presented to
respondents only once is �split-half reliability�. In this approach the test is split into 2
equivalent halves, and the scores for respondents on one half are correlated with those
scores on the second half of the test. The difficulty in this approach is determining
whether the 2 halves are equivalent. Chronbach (1951, cited in Gregory, 1996) proposed
the coefficient alpha (commonly referred to as �Chronbach�s Alpha�) which �may be
thought of as the mean of all possible split-half coefficients. A test with �robust�
reliability would be expected to display a Chronbach Alpha in excess of 0.90.
The reliability of individual items within an instrument may also be examined.
The scores for each individual item within the instrument may be correlated with scores
on the total test (Gregory, 1996). An instrument with a high level of internal consistency
would consist of items that are reasonably homogeneous and which display high item-
total correlations.
6.2.2 Reliability analysis of responses to the 70 item version of the psychological
climate questionnaire
Table 6.1 provides the results of the reliability analysis of the 70 items of the
modified version of the PCQ for the 1401 participants who provided a complete set of
responses. The analysis indicated a particularly high level of reliability with a
148
Chronbach�s Alpha of .959. The individual item-total correlation coefficients ranged in
magnitude from .09 to .72.
149
Table 6.1 Statistics for each of the 70 items of the modified version of the Psychological Climate Questionnaire entered into the
reliability analysis.
Item Mean S. Dev
ScaleMean ifItemDeleted
ScaleVarianceif ItemDeleted
CorrectedItem-TotalCorr.
Alpha ifDeleted
1 Opportunity for independent thought and action exists in your job. 4.95 1.53 341.40 2783.70 .61 .96 2 Your job requires a high level of skill and training. 5.05 1.57 341.30 2821.82 .36 .96 3 You are required to meet rigid standards of quality in your work. 5.87 1.15 340.48 2829.86 .43 .96 4 Staff members generally trust their supervisors. 5.04 1.50 341.31 2776.08 .67 .96 5 The methods of your work are kept up to date. 5.23 1.31 341.12 2801.23 .58 .96 6 You are required to perform tasks on your job which you consider
relatively unimportant or unnecessary.4.62 1.65 341.73 2830.54 .29 .96
7 You are able to get the money, supplies, equipment, etc. your work groupneeds to do its work well.
4.47 1.70 341.88 2815.12 .37 .96
8 Your supervisor is friendly and easy to approach. 5.69 1.36 340.66 2792.29 .62 .96 9 Your supervisor offers new ideas for job and related problems. 5.08 1.46 341.27 2777.32 .68 .9610 A spirit of cooperation exists in your workgroup. 5.34 1.45 341.01 2783.89 .64 .9611 Your job responsibilities are clearly defined. 5.43 1.40 340.92 2802.17 .54 .9612 Responsibility is assigned so that individuals have authority within their
own area.5.08 1.51 341.27 2790.67 .57 .96
13 Dealing with other people is part of your job. 6.41 .95 339.94 2861.79 .21 .9614 Your supervisor encourages the people who work for him or her to
exchange ideas and opinions.5.17 1.57 341.18 2775.31 .64 .96
15 Staff members generally trust their managers. 4.76 1.62 341.59 2762.40 .70 .9616 You are given advanced information about changes which might affect
you.4.53 1.68 341.82 2773.66 .61 .96
17 The hotel�s policies are consistently applied to all staff members. 4.64 1.73 341.71 2775.12 .58 .9618 You have opportunities to complete the work you start. 5.44 1.20 340.91 2818.99 .50 .9619 Procedures are designed so that resources are used efficiently. 4.92 1.50 341.43 2779.41 .65 .9620 Your supervisor is attentive to what you say. 5.27 1.42 341.08 2777.48 .70 .9621 Your supervisor provides the help you need to schedule your work ahead
of time.5.05 1.41 341.30 2786.42 .64 .96
22 There is friction in your workgroup. 4.37 1.85 341.98 2792.97 .45 .96
150
Table 6.1 (continued)
Item Mean S. Dev
ScaleMean ifItemDeleted
ScaleVarianceif ItemDeleted
CorrectedItem-TotalCorr.
Alpha ifDeleted
23 You have opportunities to learn worthwhile skills and knowledge in yourjob.
4.82 1.67 341.53 2774.41 .61 .96
24 New staff members get on-the-job training they need. 5.06 1.59 341.29 2796.06 .51 .9625 There is variety in your job. 4.86 1.76 341.49 2795.00 .46 .9626 Your hours of work are irregular. 3.53 2.16 342.82 2858.96 .09 .9627 Everything in this hotel is checked, individual judgement is not trusted. 4.00 1.59 342.35 2845.33 .21 .9628 Being liked is important in getting a promotion. 2.91 1.64 343.44 2824.39 .33 .9629 You have good information on where you stand and how your
performance is evaluated.4.59 1.57 341.76 2778.84 .62 .96
30 Your superior emphasises high standards of performance. 5.53 1.26 340.82 2806.69 .57 .9631 The ideas and suggestions of staff members are paid attention to. 4.79 1.47 341.57 2769.79 .72 .9632 You have the opportunity to do a number of different things in your job. 5.02 1.59 341.33 2797.23 .50 .9633 Your supervisor sets an example by working hard himself or herself. 5.08 1.69 341.27 2764.76 .66 .9634 A friendly atmosphere prevails among most of the members of your
workgroup.5.68 1.25 340.67 2810.15 .54 .96
35 Hotel politics count in getting a promotion. 3.14 1.58 343.21 2827.54 .32 .9636 People act as though everyone must be watched or they will slacken off. 4.03 1.58 342.32 2809.47 .43 .9637 Supervisors generally know what is going on in their work groups. 5.10 1.38 341.25 2791.42 .62 .9638 You are aware of how well your work group is meeting its objectives. 5.05 1.32 341.30 2799.42 .59 .9639 Your job demands precision. 5.51 1.27 340.84 2825.70 .42 .9640 Members of your work group trust each other. 5.11 1.38 341.24 2801.89 .55 .9641 The hotel has a good image to outsiders. 5.65 1.24 340.70 2816.21 .51 .9642 Working in this hotel is beneficial to your career. 5.34 1.59 341.01 2785.75 .57 .9643 You have opportunities to make full use of your knowledge and skills in
your job.4.97 1.76 341.38 2762.47 .64 .96
44 Communication is hindered by following the chain of command rules. 3.85 1.49 342.50 2814.85 .42 .9645 Your supervisor encourages the people who work for them to work as a
team.5.78 1.25 340.77 2797.66 .64 .96
46 It is possible to get accurate information on the policies and objectives ofthis hotel.
5.23 1.38 341.12 2802.82 .54 .96
151
Table 6.1 (continued)
Item Mean S. Dev
ScaleMean ifItemDeleted
ScaleVarianceif ItemDeleted
CorrectedItem-TotalCorr.
Alpha ifDeleted
47 The hotel strives to do a better job than other hotels of the same type. 5.77 1.21 340.59 2812.87 .54 .9648 The hotel emphasises personal growth and development. 5.05 1.53 341.30 2777.39 .65 .9649 Managers keep well informed about the needs and problems of
employees.4.53 1.62 341.82 2761.33 .71 .96
50 Discipline in this hotel is maintained consistently. 4.78 1.49 341.57 2794.02 .56 .9651 Your manager is successful in his dealing with higher levels of
management.5.04 1.43 341.31 2789.46 .61 .96
52 The objectives of the hotel are clearly defined. 5.47 1.22 340.88 2810.66 .56 .9653 There is conflict between your department and other departments of the
hotel.4.35 1.72 342.00 2816.61 .35 .96
54 Your work is important. 6.13 1.08 340.22 2833.20 .43 .9655 The way your work group is organised hinders the efficient conduct of
work.4.41 1.74 341.94 2817.10 .35 .96
56 This hotel is concerned with assisting the local community. 4.77 1.45 341.58 2823.76 .38 .9657 Things in this hotel seem to happen contrary to rules and regulations. 3.97 1.55 342.38 2812.15 .42 .9658 In this hotel the only source of information on important matters is the
grapevine.4.59 1.65 341.76 2790.67 .52 .96
59 In this hotel things are planned so that everyone is getting in each othersway.
5.22 1.32 341.13 2814.47 .48 .96
60 Under most circumstances I would recommend this hotel to a prospectivestaff member.
5.37 1.35 340.98 2800.02 .58 .96
61 Most of the personnel in my department would not want to change toanother department.
4.61 1.60 341.74 2823.46 .34 .96
62 Most members of my work group take pride in their jobs. 5.21 1.35 341.15 2799.01 .58 .9663 Generally there are friendly and co-operative relationships between the
different departments of the hotel.5.16 1.25 341.19 2815.91 .50 .96
64 My department, compared to all other departments would be one of themost productive.
5.15 1.38 341.20 2847.17 .24 .96
65 Excessive rules and regulations interfere with how well I am able to do myjob.
4.60 1.50 341.75 2818.95 .39 .96
66 Overall I think my immediate supervisor is doing a good job. 5.51 1.37 340.84 2789.32 .64 .96
152
Table 6.1 (continued)
Item Mean S. Dev
ScaleMean ifItemDeleted
ScaleVarianceif ItemDeleted
CorrectedItem-TotalCorr.
Alpha ifDeleted
67 Compared with other work groups, my work group is under much lesspressure to produce.
5.06 1.56 341.29 2861.54 .12 .96
68 In my job the opportunities to get to know people are limited. 4.95 1.66 341.40 2836.24 .26 .9669 Compared to all other similar work groups in this hotel, my work group
would be the most productive.4.75 1.36 341.64 2862.79 .13 .96
70 Your immediate supervisor is successful in dealing with higher levels ofmanagement.
5.08 1.42 341.27 2790.44 .61 .96
N = 1401Scale Mean = 346.35Scale Variance = 2883.93Alpha = .9594
153
6.3 Statistical techniques to identify underlying dimensions in a data matrix of
participant responses
6.3.1 Factor analysis
Factor analysis is a generic name for one of the multivariate techniques that is
used to ascertain the underlying structure in a data matrix (Hair et al., 1995). It analyses
a large number of variables by identifying common and unique sets of variance that are
referred to as dimensions, factors or components. These techniques allow the researcher
to summarise and reduce the data. The process of summary and reduction allows the
data to be described by a much smaller number of variables than the original. Factor
analysis is a technique that considers all the variables simultaneously. It is an
interdependence technique where the variates (factors) are formed to explain the whole
variable set and thus each variate is predicted by all of the others. Factor analysis may
be either exploratory where the data are searched for the underlying structure or
confirmatory. In confirmatory factor analysis the researcher is seeking to confirm a
structure that has already been identified from previous research. There are 2 main
factor analytic methods, Principal Components Analysis (PCA), sometime called just
�component analysis� and Common Factor Analysis.
6.3.2 Principal components analysis
PCA relies upon the total variance to derive the factors with small proportions of
unique variance. This technique is appropriate when the main concern is to predict the
minimum number of factors that are required to account for the maximum proportion of
the variance and when there is an a priori set of variables (Ghauri et al., 1995).
154
Whilst PCA provides a parsimonious description of a dataset, like all methods of
factor analysis it suffers from the problem of factor indeterminacy. That is, for any data
set the factor solution is not unique.
6.4 Principal components analyses of organisational climate data
The major thrust of this research was to collect data that provided the basis for
determining organisational climate and its effect upon hotel performance. Jones and
James (1979) conducted a literature search and identified 35 a priori scales, which
could relate to organisational climate. They produced a 145-item questionnaire, which
attempted to measure these 35 scales. Each scale was represented by between 2 and 7
items in their questionnaire. Thirty-five composite variables were produced representing
these theoretical scales and these 35 variables were entered into an exploratory PCA
which produced 6 factors with eigenvalues greater than 1. This questionnaire was
presented to 2 other samples and 5 of the 6 factors were reproduced. On the basis of the
Jones and James study, organisational climate may be seen as composed of 6
dimensions, as illustrated in Figure 6.1
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Figure 6.1 Organisational Climate Model A: The dimensions of Organisational
Climate from the study of Jones and James (1979).
Ryder and Southey (1990), in their study of a public service organisation in
Western Australia, modified the questionnaire of Jones and James. The original
instrument had between 3 and 5 scaled responses. Ryder and Southey used a consistent
7 point anchored scale. Again between 2 and 7 items were used to represent the original
35 a priori scales. An exploratory PCA was again applied to the 35 component
variables with their sample size of only 147. Ten factors were extracted, although only 6
were identified as being interpretable. On the basis of the Ryder and Southey study
156
organisational climate may be seen as composed of 6 dimensions, as illustrated in
Figure 6.2.
Figure 6.2 Organisational Climate Model B: The dimensions of Organisational
Climate from the study of Ryder and Southey (1990).
157
The current study modifies the questionnaire used by Ryder and Southey which
was based upon the original Jones and James instrument, such that only 2 items are used
to represent each of the 35 original �a priori� components. This reduced the
questionnaire length to 70 items.
6.5 Variables entered into the PCA
In this study a consistent 7 point anchored scale is used across all items of a
modified version of the PCQ. In this respect, the instrument used here is similar to that
used by Ryder and Southey (1990). Ryder and Southey (1990) present an exploratory
PCA of responses of 147 participants to a modified version of the PCQ. In that study
between 2 and 7 items were used to represented the Jones and James original 35 �a
priori� scales and these 35 composite items were entered into their PCA. Given the
present study used only two items to represent each of these 35 �a priori� scales (and so
a precise replication of the either the Jones and James, or the Ryder and Southey PCA is
not possible). Additionally, the substantial sample size of the present study (1,401), it
was decided to conduct the PCA presented here on the 70 individual items of the
modified PCQ used in this study. Further, given the differences in the factors described
by the James and Jones, and the Ryder and Southey studies and the use of individual
items in the current analysis, it was decided it would be more appropriate to conduct an
exploratory, rather than confirmatory, PCA. The PCA, when completed, was followed
by a Varimax rotation.
6.6 Proportion of variance explained by principal components
The PCA followed by a Varimax Rotation extracted 13 components with
eigenvalues greater than 1 (Appendix E). The 13 components accounted for 57.6% of
the total variance (Table 6.2). Given the large number of items entered into this analysis
158
it represents a good solution. The number of components expected to be extracted
generally lies in the range of K/3 and K/5, where K represents the number of variables
entered into the analysis (Tabachnick & Fidell, 1996, p. 672).
Table 6.2 Percentage of variance explained by Principal Components with
Eigenvalues greater than 1
Factor % of
Variance
Cumulative
%
1 29.0 29.0
2 4.5 33.5
3 3.6 37.1
4 3.3 40.4
5 2.7 43.1
6 2.5 45.6
7 2.1 47.7
8 2.0 49.6
9 1.7 51.4
10 1.7 53.1
11 1.6 54.6
12 1.5 56.1
13 1.5 57.6
6.7 Rotated principal component loadings
The rotated factor component loadings are presented for items of the modified
PCQ in Table 6.3. For each item, only the �primary� loading is presented (that is the
greatest loading for that item across the factors), and only items with primary loadings
on factors 1 through 7 are included (Appendix E presents both primary and minor
loadings for all items). For comparison purposes, Table 6.3 also includes, for both the
Jones and James, and Ryder and Southey studies, the factor upon which each individual
159
item would be assigned on the basis of the loadings of the composite variables used in
the earlier studies.
160
Table 6.3 Primary Rotated Component loadings for items of the modified version of the PCQ. Also included for comparison purposes are
the factors upon which those items loaded in the earlier studies of Jones and James (1979) and Ryder and Southey (1990).
Item # Item Loading Jones &James
Ryder &Southey
Factor 1 Leader facilitation and support66 Overall I think my immediate supervisor is doing a good job. .79 3 3 8 Your supervisor is friendly and easy to approach. .76 3 120 Your supervisor is attentive to what you say. .75 3 1 9 Your supervisor offers new ideas for job and related problems. .74 3 133 Your supervisor sets an example by working hard himself or herself. .68 3 170 Your immediate supervisor is successful in dealing with higher levels of management .64 3 121 Your supervisor provides the help you need to schedule your work ahead of time. .64 3 114 Your supervisor encourages the people who work for him or her to exchange ideas and
opinions..63 3 1
45 Your supervisor encourages the people who work for them to work as a team. .60 3 1 4 Staff members generally trust their supervisors. .55 5 131 The ideas and suggestions of staff members are paid attention to. .54 5 137 Supervisors generally know what is going on in their work groups. .53 5 130 Your superior emphasises high standards of performance. .53 3 151 Your manager is successful in his dealing with higher levels of management. .49 3 115 Staff members generally trust their managers. .48 5 129 You have good information on where you stand and how your performance is evaluated. .41 2 138 You are aware of how well your work group is meeting its objectives. .38 2 1
Factor 2 Professional and organisational esprit47 The hotel strives to do a better job than other hotels of the same type. .69 1 448 The hotel emphasises personal growth and development. .66 5 252 The objectives of the hotel are clearly defined. .65 1 441 The hotel has a good image to outsiders. .62 5 246 It is possible to get accurate information on the policies and objectives of this hotel. .61 1 460 Under most circumstances I would recommend this hotel to a prospective staff member. .55 5 256 This hotel is concerned with assisting the local community. .50 5 2
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Table 6.3 (continued)
Item # Item Loading Jones &James
Ryder &Southey
Factor 2 Professional and organisational esprit (continued)42 Working in this hotel is beneficial to your career. .48 5 249 Managers keep well informed about the needs and problems of employees. .48 5 150 Discipline in this hotel is maintained consistently. .46 1 4
Factor 3 Conflict and ambiguity19 Procedures are designed so that resources are used efficiently. .60 1 218 You have opportunities to complete the work you start. .57 5 311 Your job responsibilities are clearly defined. .56 1 2 7 You are able to get the money, supplies, equipment, etc. your work group needs
to do its work well. .54 1 216 You are given advanced information about changes which might affect you. .54 1 617 The hotel�s policies are consistently applied to all staff members. .51 1 4 5 The methods of your work are kept up to date. .49 1 424 New staff members get on-the-job training they need. .46 1 212 Responsibility is assigned so that individuals have authority within their own area. .45 2 2
Factor 4 Regulations, organisation and pressure65 Excessive rules and regulations interfere with how well I am able to do my job. .66 5 355 The way your work group is organised hinders the efficient conduct of work. .66 3 359 In this hotel things are planned so that everyone is getting in each others� way. .65 1 357 Things in this hotel seem to happen contrary to rules and regulations. .59 1 344 Communication is hindered by following chain of command rules. .56 5 158 In this hotel the only source of information on important matters is the grapevine. .55 1 667 Compared with other work groups, my work group is under much less pressure to produce. .46 5 336 People act as though everyone must be watched or they will slacken off. .39 6 4
Factor 5 Job variety, challenge and autonomy25 There is variety in your job. .77 2 232 You have the opportunity to do a number of different things in your job. .76 2 223 You have opportunities to learn worthwhile skills and knowledge in your job. .54 5 2
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Item # Item Loading Jones &James
Ryder &Southey
Factor 5 Job variety, challenge and autonomy (continued)43 You have opportunities to make full use of your knowledge and skills in your job. .46 2 261 Most of the personnel in my department would not want to change to another department. .44 4 2 1 Opportunity for independent thought and action exists in your job. .37 2 2
Factor 6 Workgroup co-operation, friendliness and warmth40 Members of your work group trust each other. .69 4 534 A friendly atmosphere prevails among most of the members of your workgroup. .64 4 522 There is friction in your workgroup. -.55 4 510 A spirit of cooperation exists in your workgroup. .54 4 562 Most members of my work group take pride in their jobs. .50 4 2
Factor 7 Job standards 2 Your job requires a high level of skill and training. .71 2 2 3 You are required to meet rigid standards of quality in your work. .69 6 439 Your job demands precision. .66 6 454 Your work is important. .46 2 4
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6.8 Interpretation of meaning of the principal components
Of the 13 components extracted, 7 were found to be interpretable and related to
those previously described by either Jones and James (1979) or Ryder and Southey
(1990). Table 6.4 shows the 7 factors obtained from the PCA for this study with
corresponding factors from both the Jones and James, and the Ryder and Southey
studies.
Factor 1 accounted for 29% of the variance and included items such as �Overall I
think my immediate supervisor is doing a good job�, and �Your supervisor is attentive to
what you say�. This component was labelled �Leader facilitation and support� and was
judged to be consistent with factor 3 from Jones and James �Leader facilitation and
support� (with 11 of the 17 items loading here on factor 1 representing sub-components
of composite variables loading on this factor in the earlier study). It was also consistent
with factor 1 �Leader facilitation and support� from Ryder and Southey (16 of the 17
items loading here on factor 1 representing sub-components of composite variables
loading on this factor in the earlier study).
Factor 2 accounts for 4.5% of the variance and includes items such as �The hotel
strives to do a better job than other hotels of the same type�, and �The hotel has a good
image to outsiders�. This factor was labelled �Professional and organisational esprit� and
was seen to be consistent with factor 5 from Jones and James �Professional and
organisational esprit� (6 of 10 items loading here on factor 2 representing sub-
components of composite variables loading on this factor in the earlier study). This is a
component that was not identified by Ryder and Southey.
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Table 6.4 Relationship between principal components (Factors) found in this study, and those found by Jones and James
(1979) and Ryder and Southey (1990). The proportion of items falling on the corresponding factor in each of the earlier studies is
also indicated.
Factor Jones and James (1979) Ppn Ryder and Southey (1990) Ppn
F1- Leader facilitation and support F3- Leader facilitation and support 11/17 F1- Leader facilitation and support 16/17
F2- Professional and organisational F5- Professional and organisational 6/10 -
esprit. esprit.
F3- Conflict and ambiguity. F1- Conflict and ambiguity 7/9 -
F4- Regulations, organisation and - F3- Conflict and pressure 5/8
pressure.
F5- Job variety, challenge and F2- Job challenge, importance 4/6 F2- Job variety, challenge and esprit 6/6
autonomy. and variety
F6- Work group co-operation, F4- Workgroup cooperation 5/5 F5- Workgroup reputation, co-operation 4/5
friendliness and warmth. friendliness and warmth. friendliness and warmth.
F7- Job standards F6- Job standards 2/4 F4- Organisational planning openness 3/4
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Factor 3 accounted for 3.6% of the variance and included items such as
�Procedures are designed so that resources are used efficiently�, �Your job
responsibilities are clearly defined�, and �The methods of your work are kept up to
date�. This component was labelled �Conflict and ambiguity� and was seen to be
consistent with factor 1 from Jones and James �Conflict and ambiguity� (7 of the 9 items
loading here on factor 3 representing sub-components of composite variables loading on
that factor in the earlier study).
Factor 4 accounted for 3.3% of the variance, and included such items as
�Excessive rules and regulations interfere with how well I am able to do my job�,
�Things in this hotel seem to happen contrary to rules and regulations�, and �Compared
with other work groups, my work group is under much less pressure to produce�. This
component was labelled �Regulations, organisation and pressure� and appeared to
overlap with that identified by Ryder and Southey as �Conflict and Pressure� (5 of the 8
items loading on factor 4 representing sub-components of composite variables loading
on that factor in the earlier study).
Factor 5 accounted for 2.7% of the variance and included items such as �There is
variety in your job�, and �You have opportunities to learn worthwhile skills and
knowledge in your job�. This component was labelled �Job variety, challenge and
autonomy and was consistent with factor 2 identified by Ryder and Southey �Job
variety, challenge and esprit� (6 of the 6 items loading on factor 5 representing sub-
components of composite variables loading on that factor in the earlier study).
Factor 6 accounted for 2.5% of the variance and included items such as
�Members of your work group trust each other.� and �A friendly atmosphere prevails
among most of the members of your workgroup�. This component was labelled
166
�Workgroup co-operation, friendliness and warmth� and was consistent with both Jones
and James factor 4 �Workgroup cooperation friendliness and warmth� (5 out of 5 items),
and factor 5 from Ryder and Southey �Workgroup reputation, co-operation, friendliness
and warmth� (4 out of 5 items).
Factor 7 accounted for 2.1% of the variance and included items such as �Your
job requires a high level of skill and training�, �You are required to meet rigid standards
of quality in your work�, and �Your job demands precision�. This component was
labelled �Job standards�. Despite 3 of the 4 items presented here falling on factor 4 of
Ryder and Southey (�Organisational planning openness�), and although only 2 of the 4
items loading on factor 6 from Jones and James (�Job standards�), the same label was
applied as all of the 4 items falling on this factor in the present study are consistent with
the spirit of the factor definition provided by Jones and James.
6.9 Variation in climate dimensions between hotels
6.9.1 Generating climate dimension sores
The preceding sections of this chapter describe an examination of the instrument
used here to measure organisational climate and its underlying dimensions across the 14
hotels participating in this study. Whilst such investigations are of interest in their own
right, the principal purpose of this study is to examine the relationship between
Organisational Climate and Hotel Performance. To make such comparisons possible, it
is necessary to generate new variables to act as indices of each of the underlying
dimensions, and to provide an overall index of Organisational Climate.
Following a PCA as conducted here, there are a variety of methods that may be
followed to generate new variables. One would be to use the factor scores for each
167
participant, and thereby have a score on each factor for each participant. These scores
could then be averaged across employees within each hotel, and thereby provide an
Organisational Climate score for each hotel on each of the underlying dimensions. Such
an approach, however, has the disadvantage that it provides no basis for comparison in
future investigations should the same instrument be applied to other samples (as
equivalent factor scores would not exist).
The approach taken here was as follows. For each of the 7 factors, for each
participant, the arithmetic mean was calculated across all of the PCQ items with their
primary loading on that factor. This created 7 new variables, with each participant
having a score on each variable. An eighth variable was then produced which consisted
of the arithmetic mean of these 7 variables to produce a single Composite Measure of
Organisational Climate. The means were then calculated for each of these new variables
to provide a score for each hotel on each of the 7 underlying Organisational Climate
dimensions, and on the Composite Measure of Organisational Climate. These values are
presented in Table 6.5.
168
Table 6.5 Mean scores on climate dimensions and for the Composite Measure
of Organisational Climate across the 14 Hotels in the study.
Hotel F1 F2 F3 F4 F5 F6 F7 Composite
1 4.79 5.04 4.83 3.89 4.58 4.72 5.37 4.75
2 5.36 5.56 5.29 3.31 5.10 5.21 6.00 5.12
3 5.40 5.55 5.19 3.36 5.08 5.15 5.83 5.08
4 5.15 4.96 4.84 3.70 5.08 5.01 5.78 4.93
5 5.17 5.33 4.96 3.63 4.96 4.98 5.63 4.95
6 5.17 5.13 4.87 3.53 4.92 5.05 5.65 4.90
7 5.55 5.54 5.13 3.10 4.83 5.27 5.59 5.00
8 5.36 5.16 5.07 3.05 4.83 5.11 5.74 4.90
9 5.19 5.44 5.10 3.26 4.68 4.89 5.53 4.87
10 5.35 5.13 4.79 3.48 5.25 5.20 5.76 4.99
11 5.50 5.29 5.18 3.25 5.05 5.15 5.76 5.02
12 6.01 5.91 5.86 2.73 5.71 5.32 6.19 5.39
13 5.08 4.74 4.80 3.71 4.71 4.91 5.61 4.80
14 4.90 4.77 4.59 3.61 4.90 4.99 5.68 4.78
6.9.2 Comparison of Climate Dimensions between the 14 Hotels in the Study.
Although more complicated multivariate and structural equation modelling
techniques will be applied in Chapter 7 to the Organisational Climate data, a necessary
condition for an explanation of variation in hotel performance as a function of
Organisational Climate is a variation in Organisational Climate between the hotels. To
establish whether this necessary condition exists, a set of simple univariate statistics was
calculated.
The mean score for each of the underlying dimensions of organisational
climate, and for the Composite Measure of Organisational Climate (Table 6.5), were
compared across the 14 hotels in the study. Given these new variables represented
169
orthogonal underlying dimensions, a multivariate analysis of variance was considered
inappropriate due to its assumptions of underlying relationships between the dependent
variables. Consequently, the mean score for each of the underlying climate dimensions
were compared across the 14 hotels using 7 separate one-way Analysis of Variance
(ANOVA). To control for Type I error, as a consequence of the number of comparisons,
using a Bonferroni adjustment, the alpha level was adjusted from .05 to .006. The
results of these analyses are summarized in Table 6.6.
Table 6.6
Summary of results of 7 oneway ANOVA�s. Each ANOVA compared the 14
means of each of the hotels on one of the 7 dimensions of organizational climate.
D.F.Bet.
D.F.within
BetweenSS
Within SS
TotalSS
F Prob.
Leader facilitation andsupport
12 1566 133.42 1578.96 1712.38 11.03 .0000
Professional andorganisational esprit
12 1612 130.89 1377.07 1507.96 12.77 .0000
Conflict andambiguity
12 1626 75.60 1604.27 1679.87 6.39 .0000
Regulations,organisation andpressure
12 1596 133.47 1491.89 1625.35 11.90 .0000
Job variety, challengeand autonomy
12 1632 111.41 2183.47 2294.88 6.94 .0000
Workgroupco-operation,friendliness andwarmth
12 1641 67.59 872.56 940.15 10.59 .0000
Job standards 12 1650 79.78 1441.19 1520.97 7.61 .0000
Composite 12 1367 33.05 496.00 529.00 7.59 .0000
These analyses compared separately for each of the 8 variables (each of the
dimensions of Organisational Climate and the Composite Measure of Organisational
Climate), the mean scores on that variable for each of the 14 hotels in the study. These
170
analyses established that significant differences existed between hotels on each of the 7
dimensions of Organisational Climate, and on the Composite Measure or Organisational
Climate (in all cases p < .0001). Consequently it is possible that Organisational Climate
might provide some degree of explanation for differences between hotel performance.
This issue will be addressed in Chapter 7.
6.10 Summary and Discussion
In this chapter, employee responses to an instrument designed to provide
measures of organisational climate in 14 hotels were presented. The instrument used
was a version of the Psychological Climate Questionnaire first presented by Jones and
James (1979). The instrument was modified in 2 important ways, first, following the
study of Ryder and Southey using a consistent 7 point anchored scale for participant
responses. Second, whereas both of the earlier studies had used between 2 and 7 items
to represent 35 �a priori� scales, this study consistently used only 2 items.
Consequently, the instrument used here comprised of only 70 items, rather than the 145
of the original.
A reliability analysis was conducted on the participants� responses. A coefficient
alpha (Chronbach�s) of 0.96 was found. This represents an excellent result and indicates
a high level of internal consistency for the instrument as applied in this study.
Homogeneous tests exhibit high values of the coefficient alpha (Gregory, 1996, p. 96),
whereas heterogeneous tests which �measure more than one trait invariably produce low
values of coefficient alpha�. Such an outcome provides support for the notion of
calculating a single overall index of organisational climate from the version of the PCQ
used in this study.
171
An analysis of the item-total correlations for each item in the instrument found
some items to correlate as high as .72 with the total test score. Some items, however,
displayed correlations as low as .09. Such information would be useful in the future
should an even shorter version of the PCQ be developed. It might be decided to remove
items with low item-total correlations, particularly where items do not have significant
loadings on any of the principal components described below. Such an exercise is,
however, beyond the scope of the current investigation.
Applying PCA to the responses of hotel employees to the modified and
simplified form of the PCQ used in this study found 7 underlying dimensions of
Organisational Climate that were interpretable. These 7 dimensions accounted for 48%
of the variance.
Jones and James� analysis of responses from U.S. naval personnel produced a 6
factor solution accounting for 59% of the variance. They labelled their factors �Conflict
and Ambiguity�, �Job Challenge, Importance, and Variety�, �Leader Facilitation and
Support�, �Workgroup Cooperation, Friendliness, and Warmth�, �Professional and
Organisational Esprit�, and �Job Standards�. Jones and James repeated their study using
samples of firemen and health managers to assess the generalisability of their factor
structure in representing Organisational Climate. They replicated the first 5 of their 6
dimensions across their samples.
Ryder and Southey (1990) modified the Jones and James questionnaire to
provide a consistent method of response by participants to each question. These authors
produced a solution accounting for 57% of the variance which extracted 10 factors, of
which 6 were deemed to be interpretable. They labelled their factors as �Leader
Facilitation and Support�, �Job Variety, Challenge and Autonomy�, �Conflict and
172
Pressure�, �Organisational Planning Openness�, �Workgroup Reputation, Cooperation,
Friendliness and Warmth�, and �Perceived Equity�.
Although Ryder and Southey concluded �that the major dimensions of the PCQ
are stable and may provide a comparative framework in the study of organisational
climates� (page 49), they provided only one of their dimensions with the same label as
provided by Jones and James.
In the current study, a simplified version of the Ryder and Southey version of
the PCQ was used. The version used a consistent number of items (2) representing each
of the 35 �a priori� scales originally proposed by Jones and James. This contrasts with
both of the earlier studies that used up to 7 items to represent one of these sub-scales.
The PCA presented here produced 7 factors for which labels were proposed; �Leader
facilitation and support�, �Professional and organisational esprit�, �Conflict and
ambiguity�, �Regulations, organisation and pressure�, �Job variety, challenge and
autonomy�, �Workgroup co-operation, friendliness and warmth�, and �Job standards�.
An interesting mix of factors emerged when contrasted with the earlier 2 studies.
Five of the components presented here were interpreted as essentially the same, and
given the same labels, as those presented by Jones and James. �Leader facilitation and
support� (which was also found by Ryder and Southey), �Professional and
organisational esprit�, �Conflict and ambiguity�, �Workgroup co-operation, friendliness
and warmth� and �Job standards�. Of the remaining 2 factors extracted here, �Job
variety, challenge and autonomy� was interpreted as essentially the same, and given the
same label, as factor 2 of Ryder and Southey. The component labeled here as
�Regulations, organisation and pressure� was found to have significant overlap with
�Conflict and Pressure� of Ryder and Southey.
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Although it was identified in all three studies, the factor Leader Facilitation and
Support explained the largest amount of variance in both the current study, and that of
Ryder and Southey. In the original study by Jones and James, this factor accounted for
the third largest amount of variance. This pattern across studies may simply reflect the
existence of a greater variation in leadership within civilian organisations (both private
and public) than would be found within the military sample used by Jones and James.
On the basis of the analysis presented here Organisational Climate within the 14
Australian hotels used in this sample may be considered to be composed of 7 underlying
dimensions (Aggregate Organisational Climate Model C). Each of which has been
described within either of the earlier studies of Jones and James, and Ryder and
Southey. This is illustrated in Figure 6.3.
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Autonomy
Figure 6.3 Organisational Climate Model C: The dimensions of Organisational
Climate of the 14 Hotels participating in the current study.
The PCA presented here produced a lower proportion of variance explained by
the 7 factors for which labels were proposed, than was the case for the original version
of the PCQ presented by Jones and James (1979). Here the 7 dimensions accounted for
48% of the variance whereas Jones and James presented factors accounting for 59% of
the variance. A number of reasons could explain the differences between the 2 studies.
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First, in their original study Jones and James used enlisted U.S. Navy personnel. In the
present study, both full-time (permanent), and casual employees completed the study. It
must be remembered that a PCA is not simply an analysis of the instrument, but also an
analysis of the population responding to the instrument. It is entirely possible that
permanent and casual staff would represent essentially different sub-populations. If this
were true, the presence of the two sub-populations would serve to increase the variance
to be explained, and thereby reduce the proportion of variance explained by the
principal components. Second, this instrument is a simplified version of that originally
presented by Jones and James in which only 70 items were presented compared with
144. Both the original version of the PCQ and the modified version used by Ryder and
Southey, used up to seven items, and never less than two, to represent each of the 35 �a
priori� scales. In this study two items were used to represent each of these �a priori�
scales. The averaging procedure of the earlier study would serve to minimise noise in
the data matrix. Third, given the relatively large size of the sample, the analysis
conducted here was performed on the individual items and not aggregate constructs.
Analysing scores on the individual items (as was done here) would serve to increase the
level of unique variance for each item, and thereby reduce the proportion of variance
that may be explained by common factors.
A similar comparison with the results of Ryder and Southey is difficult. They
report �the solution accounted for 57.4% of variance� (page 48). However, it is not clear
whether they are reporting the variance accounted for by the 10 factors they extracted
with eigenvalues greater than unity (which would be the usual interpretation of their
precise wording), or by the 6 factors they deemed to be interpretable (which is implied,
but not explicitly stated in their results). If it is the former, then the 13 factors extracted
by the analysis reported here accounted for a comparable proportion of variance
(57.6%).
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On the basis of the loadings of items of the PCQ on different principal
components, new variables were produced to provide each participant with a score on
each of the underlying dimensions of Organisational Climate and provide an overall
index of Organisational Climate termed here �Composite Measure of Organisational
Climate�. Simple univariate comparisons were made by calculating the mean scores on
these new variables across employees within each hotel and applying ANOVA to
compare these means across the 14 hotels. Such variation in Organisational Climate
between the hotels leaves open the possibility that Organisational Climate may provide
some explanation of variation between hotels in their financial performance. This issue
will be examined in Chapter 7.
In summary, in this study the employees of 14 hotels completed a version of the
original Jones and James (1979) PCQ. The items were both modified following the
procedures described by Ryder and Southy (1990) so that each item required a
consistent mode of response, and reduced in length from 144 to 70 items. A reliability
analysis demonstrated a high level of internal consistency with a coefficient Alpha
(Chronbach�s) of 0.96. A PCA extracted 7 interpretable components that accounted for
48% of the variance. A comparison with the factors presented by Jones and James, and
Ryder and Southey found 5 of the 7 to be consistent with factors extracted in the former
study, and the remaining 2 to be consistent with factors extracted in the latter study.
Univariate analyses demonstrated that each of the dimensions of Organisational Climate
varied significantly across the 14 hotels participating in this study.
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7.0 Analyses of the relationships between: Employee Demographic
Variables, Organisational Climate, Customer Satisfaction, and
REVPAR
7.1 Overview
In this chapter the relationships between the dimensions of organisational
climate, customer satisfaction and REVPAR will be examined. As described in chapter
4, data presented in this study fall into 2 categories. The first represents data on
variables for which a single value exists for each of the 14 hotels (Hotel Level Data). A
particular variable may fall into this category of data either as a consequence of the fact
that it is inherently only a property of the hotel and not the individual staff member (e.g.
REVPAR), or as a result of the aggregation of scores of staff members within each hotel
to produce a score which is an �average� value for staff members of that hotel. The
second category of data represents variables where there is a score for each individual
staff member who participated in the study (Staff Level Data).
It is important to understand the different outcomes that may come about by
analysing data from these different categories. For example, there may be 2 variables
for which there are scores for each staff member in the sample. This then allows the
calculation of the aggregate scores for each hotel consisting of the mean score of the
staff members� responses for that hotel. This would yield 2 new variables, each
consisting of 14 cases. It is mathematically possible to, first, analyse the relationship
between these 2 variables using the scores from all of the individual staff members
participating in the study and find a significant relationship between the 2 variables, and
second, analyse the relationship between these 2 variables using the aggregate scores
and find no variation between hotels on either variable and find a non-significant
relationship between the 2 variables.
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Different analyses within this chapter will be undertaken at different levels of
the data from the responses of staff members across 14 hotels. Consequently, on some
occasions it is necessary to perform analyses in which an individual �case� represents an
individual employee and in other analyses an individual �case� may represent an Hotel.
The models outlined in Chapter 3 guide the overall analyses presented in this
chapter. First, an explanation of the statistical and modelling procedures is presented.
Second, a method to define an aggregate (composite) measure of Organisational
Climate is presented. Third, a series of analyses are presented to examine the viability of
Structural Model A. Fourth, a series of analyses are presented to examine the viability
of Structural Model B.
7.2 Statistical analyses and modeling techniques used in this chapter
7.2.1 Multiple linear regression
This statistical technique relies upon two or more predictors that are jointly
regressed against the criterion variable, and is known as multiple linear regression. The
correlation coefficient r indicates the strength of the relationship between 2 variables
but does not give the magnitude of the variance in that dependent variable that will be
explained when several independent variables are theorised to simultaneously influence
it. Where the dependent variable is, for example, organisational climate it may be
explained by a range of independent demographic and other variables (predictors).
Multiple linear regression is a technique that provides the calculation of the
multiple correlation coefficient R which is an index of correlation between a set of
independent variables and the dependent variable. More importantly, the technique
provides an explanation of how much of the dependent variable is explained by a set of
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predictors by providing an index of the proportion of variance explained in the
dependent variable by the set of independent variables (R2).
7.2.2 Structural equation modelling
Ullman (1996) describes structural equation modeling as �a collection of
statistical techniques that allow examination of a set of relationships between one or
more IVs [independent variables], either continuous or discrete, and one or more DVs
[dependent variables], either continuous or discrete� (p. 709). Structural equation
modeling is used to examine the efficacy of proposed cause and effect relationships
between a set of variables. In its most simple form, structural equation modeling
represents the application of correlation and multiple linear regression to evaluate the
strength of the relationships between variables. Within the framework of this type of
analytical approach, for an hypothesised model to be a viable explanation of the
relationships between variables, it is necessary, but not sufficient, for correlation
coefficients or regression coefficients to be significant between variables for which
cause and effect relations are proposed.
A number of indices of the goodness of fit of the hypothesised model in
providing a parsimonious explanation of the relationships between variables exist. A
good fit is sometimes indicated by a non-significant χ2 value (Ullman, 1996). Ullman,
however, lists a number of problems associated with the use of χ2 as a goodness of fit
index when conducting structural equation modeling. First, with small samples the
computed χ2 need not have a χ2 distribution. Second, with large samples trivial
differences between an estimated population values may be significant. Third, when
assumptions underlying the χ2 test statistic are violated, the associated probability levels
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are unreliable. As a consequence of these problems, Ullman (1996) reports that
numerous measures of goodness of fit have been proposed.
The Goodness of fit index (GFI) calculates a weighted proportion of variance in
the sample covariance matrix accounted for by the estimated population covariance
matrix (Tanaka and Huba, 1989, as cited in Ullman, 1996). Tanaka and Huba propose
the GFI to be analogous to R2 in multiple regression. A GFI of 1 represents a perfect fit
between the hypothesised model and the observed data. A GFI value in excess of .9 is
usually accepted as indicating a good fit of the model to the empirical data.
The Adjusted Fit Index (AGFI) represents a modification of the GFI which
incorporates both the number of parameter estimates in the model and the number of
data-points in the sample. The greater the number of estimated parameters, the smaller
the AGFI value is relative to the GFI. Conversely, the smaller the number of data-
points, the smaller the AGFI will be with respect to the GFI.
One goal of all modeling, including structural equation modeling, is parsimony.
As a general rule, increasing the number of parameters in a model will serve to increase
the fit of the model to observed data. A good model, therefore, contains as few
parameters as possible. A number of Parsimony fit indices exist. These include;
The PGFI represents a modification of the GFI to take into account parsimony of
the model (Mulaik, et. al., 1989, as cited in Ullman, 1996). The PGFI may be described
by
PGFI = [1 � (No. of estimated parameters/No. of data-points)] * GFI
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The closer this fit index is to a value of 1.00, the better the fit of the model. This
index provides a heavy penalty for the estimation of a large number of parameters
within a model and is usually much smaller than other indices of fit (Ullman, 1996).
The Akaike Information Criterion (AIC) and the Consistent Akaike Information
Criterion (CAIC) are 2 different measures of parsimony of fit that each use χ2 and the
degrees of freedom:
AIC = χ2model � 2dfmodel
CAIC = χ2model � (lnN + 1) dfmodel
For these 2 indices small values indicate a parsimonious fit. Ullman reports,
however, there to be no consensus regarding precisely how small is small enough.
7.3 Structural Model A: Relationship between demographic variables,
Organisational Climate and Customer Satisfaction.
In Structural Model A (Figure 7.1) it is proposed, firstly, that a number of
employee demographic variables (Gender, Age, Education, Organisational Tenure, Job
Tenure, Income, Hours Worked, Employment Status and Training Interval) will affect
Organisational Climate (as represented by our Composite Measure of Organisational
Climate). Secondly, that Organisational Climate will affect customer satisfaction (as
represented by our measure of Employee Perceptions of Customer Satisfaction).
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Figure 7.1 Structural Model A
This model was examined, firstly, by applying multiple regression using the
demographic variables listed above to predict our Composite Measure of Organisational
Climate. Secondly, Pearson�s correlation coefficient (r) was used to establish whether
there is a link between Organisational Climate and Employees� Perceptions of Customer
Satisfaction, and third, structural equation modeling techniques were used to devise an
overall index of goodness of fit of the model.
7.3.1. Multiple linear regression analysis examining the relationship between
employee demographic variables and organisational climate proposed by
structural model A.
Scores for each employee on 9 demographic variables (Gender, Age, Education,
Organisational Tenure, Job Tenure, Income, Hours Worked, Employment Status, and
Training Interval) were entered into a Multiple Linear Regression analysis as predictor
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variables (Independent Variables) of our Composite Measure of Organisational Climate
(Dependent Variable).
Significant correlation�s (p< .001) were found between our Composite Measure
of Organisational Climate and the predictor variables Income (r = .107), Hours Worked
(r = .148), Employment Status (r = -.090), and Training Interval (r = -.120). The
Multiple Linear Regression indicated a significant link between the set of predictor
variables and Organisational Climate (F(10,1359) = 6.58, p < .001). Overall, however, this
link was relatively modest with a multiple correlation coefficient of R = .21. This result
means that, while the relationship between the set of predictor variables is statistically
significant, only 4.5% (R2 = .045) of the variance in our Composite Measure of
Organisational Climate may be explained by these 9 demographic variables.
An examination of the relative contribution of different predictor variables as
indexed by their regression coefficients (Table 7.1), shows significant individual
contribution for hours worked per week (t(1359) = 3.092, p < .01) and time since last
training session (t(1359) = -3.84, p < .001).
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Table 7.1 Regression coefficients and associated probabilities for Multiple
Linear Regression using demographic variables to predict Composite Measure of
Organisational Climate.
Variable B S.E. B Beta t Sig t
Constant 4.605 .156 29.427 .000
Gender .048 .034 .039 1.381 .168
Age -.007 .019 -.012 - .375 .708
Education -.017 .011 -.046 -1.615 .107
Length of Service -.014 .024 -.023 - .566 .571
Length in Job -.008 .025 -.013 - .323 .747
Gross salary .021 .014 .0614 1.536 .125
Hours/week .042 .013 .133 3.092 .002
Mode of Employment -.001 .028 -.001 - .032 .974
Time since Training -.051 .013 -.109 -3.837 .000
7.3.2 An examination of the relationship between organisational climate and
employee perception of customer satisfaction as proposed by Structural Model A.
A second stage of analysis for Structural Model A was conducted which
examined the relationship predicted by the model to exist between the Composite
Measure of Organisational Climate and Employees� Perception of Customer
Satisfaction. The individual responses of each employee in the sample were entered into
the analysis which found a significant correlation between the 2 measures (r= .425,
p<.001). This analysis indicated that 18.1% of the variance in employee perceptions of
customer satisfaction could be accounted for by the Composite Measure of
Organisational Climate.
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When this analysis was conducted for each hotel individually, the relationship
was shown to hold for each of the hotels (Table 7.2).
Table 7.2 Pearson r correlation coefficients examining the relationship
between the Composite Measure of Organisational Climate and Employee
Perceptions of Customer Satisfaction for each of the Hotels participating in the
study.
Hotel Pearson r Sample size
1 .439** 428
2 .372** 95
3 .274** 230
4 .346* 49
5 .254* 64
6 .435** 35
7 .388* 41
8 .316* 40
9 .515** 29
10 .287* 71
11 .318** 74
12 .479* 22
13 .561** 144
14 .449** 60
*significant at .05 level**significant at .01 level
The analyses above show significant relationships between the Composite
Measure of Organisational Climate and Employee Perceptions of Customer Satisfaction
when analyses are carried out on data from individuals within the organisations studied.
It is, however, possible that these results need not reflect the differences between the 14
hotels studied here. To examine this, the Mean Composite Measure of Organisational
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Climate score, and Mean Customer Satisfaction score was calculated for each Hotel
(Table 7.3).
Table 7.3 Mean Composite Measure of Organisational Climate, Mean
Employee Perception of Customer Satisfaction and REVPAR for each of the 14
Hotels.
Hotel Organisation Customer REVPAR
1 4.7384 3.77 96.77
2 5.1061 4.15 151.59
3 5.0809 4.25 89.76
4 4.9139 3.55 78.11
5 4.9166 3.99 72.42
6 4.9362 3.91 86.13
7 4.9553 3.96 63.29
8 4.8533 3.91 74.68
9 4.9184 4.5 124.16
10 4.9736 3.95 65.10
11 5.0399 3.96 78.19
12 5.3833 4.07 62.25
13 4.8178 3.67 73.24
14 4.8139 3.58 77.36
The correlation between these 2 variables was then calculated to show a
correlation of r = .469. This analysis shows that 22% of the variance in Mean Customer
Satisfaction between the Hotels could be explained by differences in Mean
Organisational Climate between the Hotels.
7.3.3 Structural equation modeling
The data were entered into the AMOS program (Arbuckle, 1997) and the
analysis calculated the goodness of fit of the empirically derived data with the
theoretical model (Appendix F). Maximum likelihood estimation was employed to test
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all models. The independence model that tests that the hypothesised variables showed
that they are uncorrelated and therefore were easily rejected (χ2(66) = 3332, p < .001).
Despite the analysis finding a significant chi-square (χ2(10) = 59.462, p < .001), the
goodness of fit indices demonstrated support for the model (Table 7.4).
Table 7.4 Goodness of fit and parsimony of fit indices for structural equation
analysis.
Index Value
Goodness of fit indices NFI 0.982GFI 0.992AGFI 0.938IFI 0.985CFI 0.985RMR 0.040
Parsimony of fit indices PGFI 0.127AIC 195.8CAIC 610.3
The fit indices �goodness of fit index� (GFI) and the �adjusted fit index� (AGFI)
are both in excess of .9 and indicate a good fit of the model to the data (Tabachnick and
Fidell, 1996, p. 750). The degree of parsimony fit indices, the PGFI, the Akaike
Information Criterion (AIC) and the Consistent Akaike Information Critereon (CAIC),
however, with values of 0.135, 173.5 and 519.9 respectively are relatively low in the
former case, and high in the latter 2 cases (Tabachnick and Fidell, 1996, p. 750). These
indices reflect the fact that a relatively large numbers of variables were used in the
model to produce the fit achieved.
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7.3.4 Summary of analysis of Structural Model A
Although Structural Equation Modeling Analysis yielded a respectable
Goodness of Fit index (.991), this index could be viewed as indicating that the large
sample size used in this analysis provided strong evidence of a weak relationship. The
nine demographic variables were shown in the regression analysis to have a significant
predictive relationship with the Composite Measure of Organisational Climate. And
given all 10 predictor variables combined served only to explain 4.5% of the variance, it
is doubtful that in �real world� applications such a relationship would be useful in
examining financial outcomes for hotels.
Although the only fair interpretation is that poor support was found for the
global model presented here. The relatively strong relationship between mean employee
perceptions of customer satisfaction and mean Composite Measure of Organisational
Climate in which 22% of the variation in one was explained by the other may have �real
world� application. This relationship is relevant to the following analysis of Structural
Model B.
7.4 Structural Model B: The relationship between Organisational Climate,
Customer satisfaction, and REVPAR.
In Structural Model B relationships were proposed between the underlying
dimensions of Organisational Climate, Employee Perceptions of Customer Satisfaction,
and REVPAR. The PCA presented in Chapter 5 did not provide a perfect replication of
the factor structure described by either Jones and James (1979) or by Ryder and Southey
(1990), rather a factor structure that essentially reflected a mix of factors consistent with
factors described in both of the earlier studies. On the basis of the results presented in
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Chapter 6, a slightly modified version of Structural Model B is now presented in Figure
7.2.
Autonomy
Figure 7.2 Structural Model B
This model was tested by first, applying multiple regression using the 7
dimensions of Organisational Climate to predict Employee Perception of Customer
Satisfaction. Second, a Pearson r correlation was used to establish whether there is a
link between Employee Perception of Customer Satisfaction and REVPAR, and third,
structural equation modeling techniques were used to devise an overall index of
goodness of fit of the model.
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7.4.1 Multiple linear regression analysis examining the relationship between
organisational climate dimensions and employee perception of customer
satisfaction proposed by Structural Model B.
For each of the employees, the factor scores on each of the 7 dimensions of
Organisational Climate were entered as predictor (Independent) variables of Employee
Perception of Customer Satisfaction (Dependent Variable) in a Multiple Linear
Regression.
Employee Perception of Customer Satisfaction was shown to be significantly
correlated with each of the 7 factors (Table 7.5).
Table 7.5 Correlations between Employee Perception of Customer Satisfaction
and each of the 7 dimensions of Organisational Climate.
Factor Pearson r
1. Leader Facilitation and Support .386*
2. Professional and Organisational Esprit .534*
3. Conflict and Ambiguity .412*
4. Regulations, Organisation and pressure -.309*
5. Job variety, challenge and autonomy .293*
6. Workgroup cooperation, friendliness and warmth .294*
7. Job standards .247*
* correlation is significant at the 0.001 level
The Multiple Linear Regression indicated a significant link between the set of
predictor variables and Employee Perception of Customer Satisfaction (F(7,1381) =
83.953, p < .001). This link was reflected by a relatively strong multiple correlation
coefficient of R = 0.547. This result means that 30% (R2 = 0.30) of the variance in
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Employee Perception of Customer Satisfaction may be explained by these 7 dimensions
of Organisational Climate.
An examination of the relative contribution of different predictor variables as
indexed by their regression coefficients (Table 7.6), shows significant individual
contribution for Professional and Organisational Esprit (t(1374) = 13.713, p < .001),
Regulations, Organisation and Pressure (t(1374) = -2.626, p < .005), and Job Variety,
Challenge and Autonomy (t(1374) = -2.946, p < .005).
Table 7.6 Regression coefficients and associated probabilities for Multiple
Linear Regression using Organisational Climate Dimensions to predict Employee
Perception of Customer Satisfaction.
B Std.Err.
Beta T Sig.
Constant 2.024 .177 11.44 .0001. Leader Facilitation and
Support-9.8-03 .027 -.015 -.364 .719
2. Professional andOrganisational Esprit
.351 .026 .490 13.71 .000
3. Conflict and Ambiguity 4.6E-02 .025 .067 1.841 .0664. Regulations, Organisation and
pressure-5.0E-02 .019 -.070 -2.63 .009
5. Job variety, challenge andautonomy
-5.7E-02 .019 -.098 -2.95 .003
6. Workgroup cooperation,friendliness and warmth
4.5E-02 .028 .047 1.612 .107
7. Job Standards 2.2E-02 .021 .030 1.070 .285
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7.4.2 An examination of the relationship between REVPAR and employee
perception of customer satisfaction as proposed by Structural Model B.
Structural Model B predicts a relationship to exist between REVPAR and
Employee Perception of Customer Satisfaction. Seemingly the question as to whether
this relationship exists between 2 continuous variables may simply be answered by
conducting a simple correlation between the 2 variables. These 2 variables, however,
each fall into the 2 different categories of data (Hotel Level Data, and Staff Level Data)
and so 2 different analyses were conducted, one at each level of data.
In the first analysis, the 2 variables were examined at the Staff Data Level. Each
employee had a score for Employee Perception of Customer Satisfaction. A score for
each employee was assigned for REVPAR, simply by assigning the REVPAR score for
the hotel in which the employee worked. A simple Pearson r correlation was then
conducted which found a significant correlation between REVPAR and Employee
Perception of Customer Satisfaction (r = 0.112, p < .001).
In the second analysis (conducted at the Hotel Level), for each hotel an
aggregate score for Employee Perception of Customer Satisfaction was calculated by
simply taking the mean score of this variable across all employees in the hotel.
REVPAR and Mean Employee Perception of Customer Satisfaction were then entered
into a simple Pearson r correlation. If one accepts the a priori notion that should
customer satisfaction affect REVPAR, then the effect should be positive, then the
analysis finds a significant effect for Average Customer Satisfaction as a predictor of
REVPAR (r = 0.479, P < .05, one-tailed). Regardless of whether this a priori
assumption is made, the analysis found 23% of the variance (r2) in REVPAR to be
explained by variation in Mean Employee Perception of Customer Satisfaction.
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7.4.3 Structural equation modeling
The Staff Level Data were entered into the AMOS program (Arbuckle, 1997)
and the analysis calculated the goodness of fit of the empirically derived data with the
theoretical model (Appendix F). Maximum likelihood estimation was employed to
estimate all models. The independence model that tests the hypothesis that all variables
are uncorrelated was easily rejected (χ2(36) = 5284, p < .001). Despite the analysis
finding a significant chi-square (χ2(7) = 49.004, p < .001) the goodness of fit indices
demonstrated a good fit for the model (Table 7.7).
Table 7.7 Goodness of fit and parsimony of fit indices for structural equation
analysis.
Index ValueGoodness of fit indices
NFI 0.991GFI 0.993AGFI 0.953IFI 0.992CFI 0.992RMR 0.354
Parsimony of fit indicesPGFI 0.154AIC 125.0CAIC 363.4
The fit indices �goodness of fit index� (GFI) and the �adjusted fit index� (AGFI)
are both in excess of .9 and indicate a good fit of the model to the data (Tabachnick and
Fidell, 1996, p. 750).
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7.4.4 Summary of analysis of Structural Model B
The structural equation modeling analysis provided good support for the model
with a goodness of fit index of .993. Further, the magnitude of the relationships between
variables, within the model are likely to provide useful insights in �real world�
applications. Using Multiple Linear Regression, it was shown that the seven dimensions
of Organisational Climate explained 30% of the variation, in employee perception of
customer satisfaction. A correlation analysis found that 23% of the variation in
REVPAR could be explained by the variation in mean employee perception of customer
satisfaction.
7.5 Summary and discussion
In this chapter, the relationships between a set of employee demographic
variables (described in Chapter 5), the seven dimensions of Organisational Climate
(extracted in Chapter 6), a Composite Measure of Organisational Climate (described in
Chapter 6), employee perception of customer satisfaction, and a measure of hotel
financial performance (REVPAR) were investigated.
Two structural models were presented which proposed a priori relationships
between these variables. In the first model (Structural Model A), it was proposed that
variation within staff demographic variables would cause variation in hotel
Organisational Climate (as indexed by the Composite Measure of Organisational
Climate). And that variation in hotel Organisational Climate would cause variation in
customer satisfaction (as measured by Employee Perception of Customer Satisfaction).
A significant relationship between the Composite Measure of Organisational
Climate and Employee Perception of Customer Satisfaction was found. This was true
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for the Staff Level Data, where correlation was performed on scores from each
individual employee. In this analysis, 18.1% of the variation in customer satisfaction
was explained by variation in the Organisational Climate. More importantly, this
relationship also held for data at the Hotel Level, when aggregate variables were
produced by averaging scores for employees within each hotel, and thereby producing a
single score for Organisational Climate and a single score for Customer Satisfaction for
each of the 14 hotels. The correlation of these 2 new variables found 22% of the
variance between hotels in Employee Perception of Customer Satisfaction could be
explained by variation between hotels in Organisational Climate. These results provided
strong support for part of Structural Model A.
Analysis of relationships within other parts of Model A did not produce such
strong support. Nine employee demographic variables were proposed to affect
Organisational Climate within the hotels. Regression analysis found a significant
relationship to exist. Although this relationship was statistically significant, only 4.5%
of the variation in the composite measure of Organisational Climate was found to be
explained in terms of the 10 demographic variables. These results serve to describe a
small, but significant, effect on Organisational Climate of employee demographic
variables, which served to produce a respectable overall goodness of fit index for this
model. Overall it should be interpreted that the large number of cases entered into the
structural equation modeling analysis provided strong support for a large number of
weak relationships. Taken as a whole the model is not likely to have relevance in �real
world� applications. The particular link within the model between the Composite
Measure of Organisational Climate and employee perception of customer satisfaction
would, however, be relevant and also relates to the analysis of Structural Model B.
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In the second structural model (Structural Model B), it was proposed that
variation in the 7 dimensions of Organisational Climate (described in Chapter 6) would
produce variation in customer satisfaction (as indexed by Employee Perception of
Customer Satisfaction), which in turn would lead to variation in REVPAR. Good
support was found for this model both in terms of statistical significance and in terms of
magnitude of effects.
First, all of the dimensions of Organisational Climate are significantly correlated
(at the .001 level) with Employee Perception of Customer Satisfaction. When used as
predictors in a multiple linear regression, variation of this set of 7 Organisational
Climate dimensions was found to explain 30.0% of the variation in Employee
Perception of Customer Satisfaction.
The second part of Structural Model B predicted a relationship between
customer satisfaction and a measure of hotel financial performance (REVPAR). A
significant correlation was found between these 2 variables when conducted using Staff
Level Data (r = .112). More importantly, when an aggregate variable was produced by
calculating the mean score for Employee Perception of Customer Satisfaction for each
of the hotels, and this new variable was correlated with REVPAR (and thereby
conducting an analysis at the Hotel Level), 23% of the variation in REVPAR between
the hotels could be explained by variation in Employee Perception of Customer
Satisfaction.
The results of this analysis provide strong support for Structural Model B. The
magnitude of the relationships between the variables indicates that these relationships
may be viewed as having a commercial, as well as a statistical and theoretical,
significance.
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In conclusion, the major finding of this chapter is that Organisational Climate,
whether treated as a multidimensional construct or as an aggregate measure, can be
demonstrated to have both a statistically significant, and a commercially relevant, effect
on customer satisfaction (as indexed by employee perceptions). Further, customer
satisfaction has also been found to have both a statistically significant, and
commercially relevant, effect on REVPAR.
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8.0 General Discussion and Conclusions
8.1 Overview of study
This study gathered data from 14 four to five-star hotels in South-East
Queensland, Australia, in an attempt to examine the nature and degree of influence
organisational climate has upon the level of performance of organisations within the
Australian hotel industry. Employee Perception of Customer Satisfaction was studied
both as an index of hotel performance and as an intervening variable between
organisational climate and REVPAR (average daily room rate multiplied by occupancy
rate) - an index of hotel financial performance.
In addition to measures attempting to represent organisational climate and
customer satisfaction, a number of descriptive statistics were gathered relating to both
the operating characteristics of hotels, and the employees working within these
organisations. The gathering of this information represented an attempt to satisfy the
first 2 aims of this study;
To provide a profile of the hotels participating in the study in terms of their key
operating characteristics.
To provide a profile, in terms of key demographic variables, of staff employed
within the hotels in our sample.
8.2 Hotel operating statistics
Despite the fact that all hotels in the study were in the range four to five-star, as
expected, the hotels nevertheless varied considerably across their key operating
statistics. The size of the hotels, for example, varied from less than 200 rooms to over
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400 and occupancy rates varied from 52% to 80%. The rack rate for hotels varied
between $170 and $344. Interesting as these variations between hotels may be, the
principal aim of this study was to examine variation in organisational climate and its
effect on hotel financial performance. A necessary condition for such a comparison to
be meaningful is the existence of significant variation in financial performance, as
indexed here by the measure REVPAR. This important variable demonstrated a
considerable degree of variation between hotels, from a figure of $62 to $124.
8.3 Staff demographic data
Overall the staff profile indicated a young, educated and trained, relatively
gender-balanced group which received comparatively low levels of remuneration and
displayed a high level of turnover.
The data demonstrated roughly equal numbers of males and female employees
across the sample. When this analysis was confined to management, however, females
were found to be under represented. In general the workforce is a young one, with the
majority of employees being under 35 years of age. The pattern of young employees
was also reflected in management, where most managers were in the 25-34 years
category.
The workforce was found to be well qualified with over 60% of staff to have
qualifications at the post-secondary level and above. Employees also reported a high
frequency of job training with 72% of employees having attended a training session
within the past 12 months. Despite employees demonstrating relatively high levels of
education and training, remuneration was relatively poor. This was true, even when the
mode of employment of many employees was accounted for. Only 5% of employees
received in excess of $36,000 p.a., despite the fact that 61% of employees are in full-
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time employment and 65% of employees worked 36 hours or more per week. This
compares with a current average full-time wage in Australia of $38,615.20 and average
total earnings (which includes overtime) of $40,768 (Martin, 1999).
Perhaps understandably, in the light of the remuneration data, there was a very
high turnover of staff, with 58.5% of employees having been with their organisation for
less than 2 years. These figures, of course, were reflected in the job tenure data with
66% of employees in their current job for less than 2 years.
Although it is beyond the analyses and aims of the current project, the data
presented here leave open the question as to whether the high turnover in staff is in part
a consequence of the relatively poor remuneration given to a relatively well educated
workforce. It would seem that the workforce is generally capable of finding what they
perceive as more rewarding employment elsewhere. If this is indeed the case, the
opportunity costs of such a situation need to be considered and investigated. The costs
would certainly involve the ongoing direct costs of continually training large numbers
of new employees. Further, there is naturally a direct cost to the employer arising from
new staff performing less efficiently in their positions, whilst undergoing orientation,
training and learning their job, than would an experienced employee.
The high turnover of staff may, of course, not be strongly related to level of
remuneration. Vallen (1993) reported that service jobs with a high degree of customer
interactions have a higher level of burnout. Within Vallen�s survey, hospitality firms
rarely used a consultative style. He concluded that high burnout was correlated with low
organisational climate scores in highly autocratic organisations. In the context of the
current study, a workforce which is not motivated to remain with its employer, is a
workforce that would be expected to generate a less than optimal organisational climate.
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As will be described below, such a climate may well result in lower levels of customer
satisfaction and poorer financial outcomes for hotels.
8.4 Variation in staff demographic variables between hotels
One of the models tested in this study (Structural Model A) predicted staff
demographic variables would affect organisational climate. For such variables to
explain differences in organisational climate between hotels, the variables need also
demonstrate variation between hotels. The third aim of this study was to establish
whether the staff demographic variables vary significantly between the hotels in the
sample.
All of the key staff demographic variables were found to significantly vary
between hotels. Gender-balance varied between hotels from close to 50/50 in one hotel
to 80% female to 20% male in another. Although across all of the hotels, the workforce
was young, the percentage of employees in the �over 45 years of age� category ranged
from 3% to 21%. The level of education varied from 53% with post-secondary
qualifications to 81%. Organisational tenure varied from a figure as low as 40% of staff
having been with the organisation less than 2 years to figures in excess of 85% for 3
other hotels. Job tenure was reported to range between hotels from 49% to 94% of
employees having been in their current job for less than 2 years. Hotels varied between
3% and 24% of employees earning in excess of $31,000 p.a. In one hotel, only 51% of
employees were in full-time employment whereas this ran as high as 90% in another. In
terms of hours worked per week, one hotel had only 58% of employees work 36 hrs or
more per week, whereas this figure was as high as 84% in another. Although all hotels
displayed high proportions of staff having received training in the last 12 months, the
figure varied from as low as 62% of employees to 91%.
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Like the hotel operating statistics, these data indicate that despite the fact that all
of the hotels were four to five-star, there was considerable variation in employee
characteristics across the hotels. This situation leaves open the possibility that these
employee variables will serve to explain some of the variation in organisational climate
between the hotels.
8.5 The Measurement of organisational climate within the hotels of the sample
The principal thrust of this thesis was the investigation of organisational climate
and its effect on hotel performance. Individual employees in this study were presented
with an instrument which aimed to measure their perceptions of the psychological
climate in which they worked. Organisational climate within each hotel was estimated
by averaging these responses.
The instrument used was a modified version of one presented in an earlier study
by Ryder and Southey (1990), which itself was a modification of the Psychological
Climate Questionnaire presented by Jones and James, 1979). The instrument was
modified for this study principally to reduce its length to something that was practicable
for use within the hospitality industry. To this end, the instrument was reduced from its
original 145 items, to 70 items.
The fourth specific aim of the study was to identify the underlying dimensions of
organisational climate within Australian hotels. This was addressed by a Principal
Components Analysis (PCA) that was applied to the employee responses to the
organisational climate Questionnaire.
H1 A limited number of factors will be identified as being able to determine the
organisational climate across the hotels in this study.
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The first hypothesis was supported in so far as the PCA extracted 7 interpretable
underlying dimensions. These dimensions were given the following labels.
! Leader facilitation and support
! Professional and organisational esprit
! Conflict and ambiguity
! Regulations, organisation and pressure
! Job variety, challenge and autonomy
! Workgroup co-operation, friendliness and warmth
! Job standards
Although the instrument used in this study used only half the items, and on many
items required a different style of response, these factors were consistent with those
originally described by Jones and James for a sample of U.S. military personnel. Their
study produced the following factors;
! Conflict and Ambiguity
! Job Challenge, Importance, and Variety
! Leader Facilitation and Support
! Workgroup Cooperation, Friendliness, and Warmth
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! Professional and Organisational Esprit
! Job Standards
As can be seen above, 5 of the components extracted in this study were interpreted
as essentially the same and given the same title as components described in the Jones
and James study. Of the remaining 2 components extracted here, the first, �Job variety,
challenge and autonomy� was interpreted as essentially the same, as the second factor of
Ryder and Southey. The second, �Regulations, organisation and pressure� was found to
have significant overlap with Ryder and Southey�s component �Conflict and Pressure�.
Although it might be argued that the present study did not extract precisely six
underlying dimensions as was reported in the two earlier studies of Jones and James,
and Ryder and Southey. When the results are analysed, in terms of the item loadings
and the associated meaning of these dimensions, the results are broadly consistent with
those of Jones and James. When they are not consistent with that study, they would
appear to be consistent with the results of Ryder and Southey.
By the application of PCA to the data, it was possible to describe the underlying
dimensions of organisational climate within the sample of 14 hotels. Further, this
analysis provided a method by which each of the employees in the sample could be
assigned a value for each of these 7 psychological dimensions. An overall composite
value of psychological climate, for each employee could also be produced by averaging
these seven dimensions. By aggregating over these scores, it was also possible to assign
a value for each of the hotels on each of the underlying dimensions of organisational
climate and on the composite measure of organisational climate. These procedures then
allowed the possibility of further analyses to examine the relationship of these
dimensions to variables of hotel performance.
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8.6 Testing Structural Model A
In Chapter 3 a structural model (Structural Model A) was presented which
proposed that organisational climate would be affected by the demographic
characteristics of employees within the hotels, and that organisational climate would
affect employee perception of customer satisfaction. The fifth aim of this project was
effectively to test Structural Model A.
Figure 8.1 Structural Model A
This model directly produced hypotheses 2 and 3;
H2 There will be a significant correlation between an aggregate measure
of organisational climate and employee perception of customer satisfaction.
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In a strict statistical sense hypothesis 2 may be interpreted as being supported; a
multiple linear regression using the demographic variables produced a statistically
significant effect (p < .001). However, only 4.5% of the variance in organisational
climate was explained by the 9 predictor variables. Consequently, 95.5% of variation in
organisational climate was not predicted by the relationship. Although a real but weak
link might be argued between these variables, its utility in any practical real world
application would be extremely limited.
H3 Employee demographic variables, taken as a multivariate variable, will
be a significant predictor of an aggregate measure of organisational climate.
Hypothesis 3 received strong support. When analyses were conducted at the
hotel level, a correlation (r) of .469 was found between the composite measure of
organisational climate and Employee Perception of Customer Satisfaction. This analysis
shows that 22% of the variance in Mean Customer Satisfaction between the Hotels
could be explained by differences in Mean organisational climate between the Hotels.
When examined overall, Structural Model A provides a poor explanation of the
overall relationship between the variables. The link between the employee variables and
that of global organisational climate provides very little explanation of variation in
organisational climate, despite the fact that 9 variables are used to provide the
explanation. Given that other parts of the model display such poor links, the fact that a
relatively strong relationship is found between the Composite Measure of organisational
climate and Employee Perception of Customer Satisfaction does not provide strong
support for the model. Of itself, however, the result is very interesting. The result is
consistent with the notion that organisational climate has a major impact on customer
satisfaction. This relationship was investigated further in Structural Model B.
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8.7 Testing Structural Model B
In Chapter 3, a structural model (Structural Model B) was presented which
proposed that the financial performance of the hotels (as indexed by REVPAR) would
be affected by customer satisfaction (as indexed by Employee Perception of Customer
Satisfaction). Further, the seven dimensions of organisational climate would affect that
customer satisfaction. The sixth aim of this project was effectively to test Structural
Model B.
Figure 8.2 Structural Model B.
This model directly produced hypotheses 4 and 5;
H4 There will be a significant correlation between employee perception of
Autonomy
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customer satisfaction and REVPAR.
H5 The dimensions of organisational climate, taken as a multivariate
variable, will be a significant predictor of an aggregate measure of employee
perception of customer satisfaction.
H4 was supported when analyses were conducted on both employee level and
hotel level data. The correlation (r) between Average Customer Satisfaction (hotel level)
and REVPAR was 0.479. This indicated that 23% of the variance in REVPAR could be
explained by variation in Mean Employee Perception of Customer Satisfaction. This
result provided strong evidence of a link between customer satisfaction and hotel
financial performance.
H5 was also supported. Using multiple linear regression, it was found that the
set of 7 dimensions of organisational climate accounted for 30% of the variation in
customer satisfaction (as indexed by Employee Perceptions of Customer Satisfaction).
This result provides strong evidence of a link between organisational climate and
customer satisfaction.
8.8 Implications of the result that Structural Equation Model B is supported
Taken together, in conjunction with the structural equation modelling analysis
(in which GFI = .993), these two results provide strong evidence in support of
Structural Equation Model B. Further, this evidence is not simply of a link that might
have some theoretical significance, the magnitude of the relationships between
organisational climate, Employee Perception of Customer Satisfaction, and REVPAR,
are of an order which has significant practical implications for hotel management. This
outcome leads to predictions that programs which increase the positive aspects of
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organisational climate, would serve to produce an increase in customer satisfaction
which in turn would result in an increase in REVPAR.
These results serve to underline the fact that the major resource component in
service delivery is the hotel employee (the deliverer of the service). This is important in
the context of Bandura�s statement that �people act on their judgments of what they can
do, as well as their beliefs about the likely effects of various actions� (as cited in
Kopelman, Brief, and Guzzo, 1990, p. 294). Schneider (1989, p. 748) stated,
�employees observe what happens to them (and around them) and then draw
conclusions about the organisational priorities. They then set their own priorities
accordingly�. In an industry in which the quality of service delivered to a customer is
directly dependent upon individual employees, the environment, or climate, in which
that employee works will directly modulate the quality of service delivered. This
conclusion is supported elsewhere by the results of Schneider and Bowen (1985) and
Cole, Bacayan and White (1993) who provide evidence that a good organisational
climate has a positive effect on service outcomes. Schneider (1973) similarly found that
it was the atmosphere in a bank, whether it was �warm and friendly� that best predicted
customer-switching intentions.
The hotel industry, like most service industries, is one in which the vast majority
of its output is intangible and represents a coincidence of production and consumption.
Within such a framework our results serve to reinforce Schneider, Gunnarson and Niles-
Jolly in their claim that �in the absence of direct control of the service counter, it is the
climate and culture that determines high quality service� (1994, p. 23). As stated by
Schneider and Bowen �because services themselves yield little tangible evidence as a
useful basis for evaluation, it is how they are delivered, and the context in which they
are delivered that is important� (1985, p. 431).
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A prescription that emerges from this project would be for all hotels in the
industry to follow the lead of the Marriott group, the only hotel to be named in Fortune
Magazine�s top one hundred American companies (Branch, 1999), to instigate a regular
measurement of organisational climate. And further, that such measurement of
organisational climate not be an end in itself, but a tool to guide and evaluate programs
to improve organisational climate (and therefore customer satisfaction and financial
returns) on an ongoing basis.
8.9 The validity of measures used in this study
As in all research, this research is only as valid as the variables are valid in
measuring concepts they purport to measure.
8.9.1 The index of financial performance REVPAR
In the case of financial performance, the variable used here to index this
performance was REVPAR. This measure is a standard measure used within the hotel
industry as a yardstick by which hotel performance may be compared. It is a relatively
�concrete� variable in which there may be little dispute as to its interpretation.
8.9.2 Organisational climate
In the case of organisational climate. The instrument used in the current study
was, in one sense, new. In its current form, this is the first time it has been applied to a
large sample. Having said that, although the instrument may be described as �new�, the
items within the instrument are not. Although it contained a set of only 70 items, these
items were previously used in a much longer instrument used by Ryder and Southey,
and represented modifications of items of an instrument presented by Jones and James
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(1979). PCA have been reported on both of these earlier versions of the instrument.
Despite this being a new instrument, the PCA conducted on the responses of the
employees in this study were consistent with those of the earlier 2 studies. In this study,
5 of the factors extracted were given the same nomenclature as factors presented by
Jones and James. A sixth factor was given the same label as a factor described by Ryder
and Southey and a seventh factor was judged to have significant overlap with another of
Ryder and Southey�s factors.
The order of the factors differ from those of Jones and James. This is not
unusual in PCA studies as the order reflects the proportion of variance accounted for in
each individual sample. In this case the fact that Leadership facilitation and support
came out as the first factor (i.e. accounting for the largest proportion of the variance) is
understandable given one might expect greater variation in leadership style within less
bureaucratic private organisations than one might expect in the military (the sample
used by Jones and James) and so one might expect it to be able to explain a greater
proportion of the variance in the sample. In this respect, this study was also consistent
with that of Ryder and Southey, who also used a civilian sample and also found
Leadership facilitation and support to account for the largest proportion of variance
explained.
The consistency of the PCA results with those reported elsewhere provides some
degree of confidence in the instrument measuring organisational climate � or at least
measuring organisational climate within the terms of the concept as it is currently dealt
with by many researchers within the literature.
The question of the dimensions that are extracted when the data are factor
analysed is an important one. Is it certain that the underlying dimensions described in
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this study are the true underlying dimensions of organisational climate within the hotels
studied? Possibly not. Within the context of the various factors that have been proposed
in the literature, is it reasonable that the underlying dimensions described in this study
are valid descriptions of organisational climate within the hotels studied? Probably yes.
The answer to the first question was in the negative due to the following
problem. A factor analysis (in this case PCA) produces underlying orthogonal
dimensions that sum linearly, and which are produced from a matrix of numbers that
represent the responses of a particular group of people to a particular set of questions. If
an instrument contained no questions related to leadership, for example, then no
underlying dimension related to leadership would be extracted. Further, if an instrument
were produced with varying numbers of questions related to leadership, then the
leadership dimension, if extracted, would account for varying proportions of variance
depending on the proportion of items in the instrument that were related to leadership.
So in an absolute sense, it is impossible to know whether the instrument included the
perfect set of items to identify the true underlying dimensions.
The answer to the second question was in the positive. Although the instrument
used here had not been used before in its current form, it represented a development of
one presented earlier by Jones and James (1979). The set of questions developed in the
original version was collated following interviews, observations, and literature reviews.
This procedure identified 35 a priori scales. Each one of these �scales� was included in
the original instrument with each being represented by between 2 and 7 items. These
same a priori scales were included in the instrument used here where each scale was
represented by 2 items. In these terms, the instrument used here can be considered to
have included a broad range of concepts that have been associated with organisational
climate within the literature. Within this context, the dimensions of organisational
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climate identified here would be interpreted as a reasonable description of the
dimensions present within the hotels of this study.
8.9.3 Customer Satisfaction
Following advice given directly by a panel of industry experts, it was decided to
use customer satisfaction, as measured by employee Perception of Customer
Satisfaction. Industry advice indicated that within the hotel industry, poor levels of
customer satisfaction are first noticed by staff prior to the formal feedback on the
quality of service.
Given that this measure correlated with measures of organisational climate, the
argument might be presented that this correlation merely reflects the fact that employees
working in a better organisational climate are more likely to perceive customers as more
satisfied than will employees working in a poorer organisational climate. Regardless of
the level of satisfaction actually felt, or reported, by the customers themselves.
A number of arguments may be presented to regarding this proposal. First, a
number of studies have shown a close correspondence between employee estimates of
customer satisfaction and that reported by customers (Parkington and Schneider, 1979;
Schneider and Bowen, 1985; 1993).
Second, and more importantly, had the outcome of this study been merely the
demonstration of a correlation between organisational climate and Employee Perception
of Customer Satisfaction, then the outcome might well be open to major criticism and
concern regarding confounding variables. This study, however, shows more than this.
This study found, firstly, organisational climate (both in terms of an overall measure of
organisational climate, and in terms of multiple correlation of the dimensions of
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organisational climate) and Employee Perception of Customer Satisfaction to be
correlated. The 7 dimensions of organisational climate were found to account for 30%
of the variation in employee perception of customer satisfaction. Secondly, and
importantly, in addition Employee Perception of Customer Satisfaction was
significantly correlated with financial performance as indexed by REVPAR.
In a �worst case� the results presented here would necessarily be interpreted as
indicating that 23% of the variation in REPAR between the hotels may be explained as
a direct consequence of variation in Organisational Climate. On the other hand, the
interpretation presented here, of Organisational Climate affecting customer satisfaction
and customer satisfaction affecting REVPAR, provides a parsimonious explanation of
the process by which Organisational Climate would produce an effect on hotel financial
performance. That is the process by which variation in Organisational Climate may
result in variation in REVPAR between hotels is by its effect on customer satisfaction
which in turn produces an effect on REVPAR.
8.10 The issue of multilevel variables and the interpretation of relationships
When conducting a study that examines organisational climate and an
organisation�s performance, issues related to the level of measurement arise. Within this
thesis, the terms �Staff Level Data� and �Hotel Level Data� have been used to
differentiate 2 levels of measurement. The term �Staff Level Data� referred to scores for
which a single score existed for each employee, whereas the term �Hotel Level Data�
referred to scores for which a single score existed only for each hotel.
In one sense, organisational climate is a property of the organisation and not the
individual. Performance indicators of the organisation are also almost exclusively
expressed as properties of the organisation, and not of the individual within the
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organisation.
Although organisational climate is a property of the organisation, the way in
which a value for organisational climate is usually assigned to an organisation (or
organisational sub-unit) is to obtain measures of Psychological Climate for individuals
within the organisation and to then average the scores across individuals within the
organisation. This procedure was carried out in this study, the result of which was that
for each employee there existed a score on each of the 7 dimensions of organisational
climate and on the Composite Measure of organisational climate. For each hotel, 8 new
variables were calculated which represented the mean score across employees within
each hotel for each dimension and for the composite measure. Similarly, in this case, the
index of customer satisfaction was Employee Perception of Customer Satisfaction, and
so again the value on this variable was obtained for each of the employees. A new
variable was then produced which represented the mean Employee Perception of
Customer Satisfaction across the employees within each of the hotels. By creating these
mean scores for each of the hotels the procedure created Hotel Level Data from Staff
Level Data.
This procedure is an intuitively obvious method to provide scores on variables
for each of the 14 hotels. Variables that represent data on the Hotel Level scale are
critically important to examine whether the relationships between the variables explain
differences between the hotels. For example, it is quite possible for there to be a strong
and highly significant relationship between the Composite Measure of Organisational
Climate and Employee Perceptions of Customer Satisfaction when analysing Staff
Level Data. At the same time, it is also possible that the relationship between the Hotel
Level Data (that is the Mean Composite Measure of Organisational Climate, and the
Mean Employee Perceptions of Customer Satisfaction) to be null and the statistical
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significance zero. So demonstration of a relationship between variables using Staff
Level data is insufficient evidence to provide an explanation of variation between the
hotels.
Consequently, in the results presented here, comparisons were presented at both
the Staff Level and Hotel Level. The critical question for each of the comparisons
presented was whether, when using Hotel Level Data the relatively large proportion of
the variance in one variable was explained by another variable. Such comparisons were
critical in evaluating the efficacy of proposed relationships between variables in the
study.
For the application of structural equation modeling techniques, one needs a large
number of scores for each of the parameters in the structural model. To apply such
techniques to Structural Model B, necessitated the use of Staff Level data. In this
situation, it was necessary to generate Staff Level Data from Hotel Level Data, rather
than the reverse that had been done. This arose as the variable REVPAR only exists as a
single value for each of the hotels. To generate a value for each employee, each
employee was simply assigned the REVPAR value for the hotel within which they
worked. This enabled structural modeling techniques to be applied to provide an overall
index of the fit of the model to complement to comparisons that were presented using
correlational and regression techniques.
It would be difficult to interpret this step as in any way leading to a false
conclusion of a relationship existing between Employee Perception of Customer
Satisfaction and REVPAR, as proposed by Structural Model B. The correlation when
using Staff Level Data (r=.112, i.e. the correlation used within the structural equation
modeling procedure) actually served to underestimate the magnitude of the relationship
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between the variables. The Hotel Level (r=.479) in which 23% of the variation in
REVPAR could be accounted for by the variation in Mean Employee Perception of
Customer Satisfaction.
In all cases presented here, relationships between variables described when
analysing Staff Level Data, were only judged to be meaningful when those relationships
between variables were also represented by significant proportions of variance when
using Hotel Level Data.
8.11 Generalising results
This study is interested in generating statements that are relevant to the hotel
industry and, in particular, the Australian hotel industry. There are 2 limitations
regarding such generalisation of the results presented here. First, the study was limited
to hotels within Queensland, and second, the study was limited to four to five-star
hotels.
With regard to the first limitation, it is unlikely that this geographic limitation
will to a large extent limit the generalisation of these results to four to five-star hotels in
the rest of Australia. Within Australia, employees are highly mobile and move from
resort to city and back very easily (Timo, 1993). This characteristic alone means that the
sample of employees is representative of a group beyond the geographical limits
implied by the location of the hotels in the sample. With regards to the hotels
themselves, with the exception of two properties that are actually owned and operated
by the same company, hotel management companies ran the other hotels. In some cases
it may be argued that one would expect greater variation between different hotel chains,
than between hotels within the same chain in different states of Australia.
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With regard to the second limitation, it is not unlikely that the results reported
here will better describe the important relationships between the variables of
organisational climate, customer satisfaction, and REVPAR for hotels with four to five
star ratings than for hotels with less than four stars. Hotels with different star ratings, by
their very nature, will lead to both different expectations of the style and degree of
service and interaction between hotel staff and customers. Having said this, it awaits
further study to determine how these variables might interact to predict hotel financial
performance for hotels with different star ratings.
8.12 Future research
The outcomes of this project strongly suggest that future studies which
incorporate measures of organisational climate, hotel performance and direct measures
of customer satisfaction could provide further evidence of the importance of
organisational climate to financial performance of hotels within the Australian industry.
Further research could examine how to increase the quality of the organisational
climate, and thereby affect changes in customer satisfaction and REVPAR. There are a
number of features of the hotels that are already very positive with regard to the
generation of a good organisational climate. The staff tended to be young with a
reasonable gender mix, and are relatively well educated. One particular feature should
be examined, is the very high turnover rate of staff evidenced within the hotels. Changes
that serve to reduce the turnover rate may well serve to increase the quality of the
organisational climate.
8.13 Summary and conclusion
This study gathered data from 14 four to five star hotels in South-East
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Queensland, Australia, in an attempt to examine the nature and degree of influence
organisational climate has upon the performance of hotels. Employee Perception of
Customer Satisfaction was studied both as an index of performance and as an
intervening variable between organisational climate and financial performance as
indexed by REVPAR. The data provided a description of a, young, relatively gender
balanced, well educated and trained workforce which received relatively low levels of
financial remuneration and displayed very high levels of turnover. A new instrument
was used to measure the dimensions of organisational climate across the hotels. PCA
produced results consistent with earlier (and longer) versions of the instrument which
had been reported elsewhere. This analysis described organisational climate within the
sample to be composed of 7 underlying dimensions. The most important finding of the
study was that variation in these 7 dimensions of organisational climate accounted for
30% of the variation in Employee Perception of Customer Satisfaction. Furthermore,
that Employee Perception of Customer Satisfaction accounted for 23% of the variation
in REVPAR between the hotels. For the hotel industry represented in this sample, 30%
variation in their customer satisfaction and 23% variation in their REVPAR was directly
accounted for by their organisational climate. Organisational climate is a constant that
can be measured and improved by the application of good management practices.
220
TABLE OF APPENDICES
Appendix A Organisational climate questionnaire, employee demographics, andemployee perception of operations and customer satisfaction
Appendix B Hotel Profile Instrument
Appendix C Hotel managers demographics, operation performance and perception ofcustomer satisfaction
Appendix D Staff Demographic Data and Contingency Table Analyses
Appendix E Reliability Analyses and Principal Components Analysis of EmployeeOrganisational Climate Data
Appendix F Amos Printouts of Structural Equation Modelling analyses
221
Appendix A
Organisational climate questionnaire, employee demographics, and
employee perception of operations and customer satisfaction
222
CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUSTELEPHONE: (07) 5594 8771
PART 2 � EMPLOYEES DEMOGRAPHIC DETAILS
Please tick one box only for each question
1. Gender Male ❑Female ❑
2. Age 15-20 yrs ❑21-30 � ❑31-40 � ❑41-50 � ❑over 50 ❑
3. Education - Highest level attempted:
Secondary ❑Post-Secondary Certificate ❑❑❑❑Apprenticeship ❑Associate/Diploma ❑Degree ❑Post Graduate Diploma/Degree ❑
4. Total length of service with the hotel:
Less than 6 months ❑6 months to 1 year ❑1-2 years ❑3- 4 years ❑5 -7 years ❑7-10 years ❑over 10 years ❑
223
5. Length of time in present job:
Less than 6 months ❑❑❑❑6 months to 1 year ❑1- 2 years ❑3 - 4 years ❑5 - 7 years ❑7 -10 years ❑ over 10 years ❑
6. Current gross salary:
$10-15,000 ❑$16-20,000 ❑$21-25,000 ❑$26-30,000 ❑$31-35,000 ❑$36-40,000 ❑$41-45,000 ❑$46-50,000 ❑Over $50,000 ❑
7. How would you rate the hotel�s operational performance in the following areas? Pleaserate even if you do not directly work in the areas. (Tick one box on each line)
OPERATIONAL PERFORMANCE:
Area VeryMarginal
UnderPerforming
AcceptablePerformance
GoodPerformance
OutstandingPerformance
A. Food &Beverage ❑ ❑ ❑ ❑ ❑
B. Rooms ❑ ❑ ❑ ❑ ❑
C. Overall ❑ ❑ ❑ ❑ ❑
224
8. How would you rate the hotel�s performance in satisfying customers in the followingareas? Please rate even if you do not directly work in the areas. (Tick one box on each line)
CUSTOMER SATISFACTION:
Area VeryLow Low Average High Extremely
High
A. Food &Beverage ❑ ❑ ❑ ❑ ❑
B. Rooms ❑ ❑ ❑ ❑ ❑
C. Overall ❑ ❑ ❑ ❑ ❑
9. Overall Performance Rating. Please take into account all factors that affected
performance both internally and externally. (Tick the appropriate response)
OVERALL PERFORMANCE:
1-Poor to 5-Outstanding
1❑ 2❑ 3❑ 4❑ 5❑
10. Is your position? Full time ❑Part time ❑Casual ❑
11. Average hours worked per week: > 10 ❑11 � 15 ❑16 � 20 ❑21 � 30 ❑31 � 35 ❑36 � 40 ❑41 � 45 ❑46 � 50 ❑50 + ❑
225
12. When did you last take part in a formal education or training session (in-house orexternal)?
In the last 3 months ❑3 to 6 months ❑6 to 12 months ❑1 to 2 years ❑2 to 3 years ❑4 to 5 years ❑5 years or more ❑
13. Do you feel that you need to undertake additional education or training for yourpresent position?
Yes ❑No ❑❑❑❑
14. Please indicate the department in which you work:
Food and Beverage Service ❑ Kitchen & Stewarding ❑
Front Office & Reservation5s ❑ Housekeeping & Linen Room ❑
Purchasing, Stores & Accounts ❑ Marketing, Sales & Public Relations ❑
Administration ❑ Engineering, Maintenance & Security ❑
Conference & Convention ❑ Concierge & Porters ❑
Casino Operations ❑ Casino Administration ❑
❑ ❑
❑ ❑
❑ ❑
Other please state: ___________________________________________________________________
Are there any other departmental areas that you would like included?: __________________________
Many thanks for your time and trouble. Please seal questionnaire in the envelope provided
and place it in the box provided at the human resources office. Alternatively you may mail it
directly to the university.
226
CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUSTELEPHONE: (07) 5594 8771
PART 1 � EMPLOYEE OPINION SURVEY
QUESTION 1
1 2 3 4 5 6 7
Strongly Disagree Tend to Unsure Tend to Agree StronglyDisagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of thefollowing statements:
Circle the most appropriate response: SD D TD U TA A SA
a. Opportunities for independent thought and action exist in your job. 1 2 3 4 5 6 7
b. Your job requires a high level of skill and training. 1 2 3 4 5 6 7
c. You are required to meet rigid standards of quality in your work. 1 2 3 4 5 6 7
d. Staff members generally trust their supervisors. 1 2 3 4 5 6 7
e. The methods of your work are kept up-to-date. 1 2 3 4 5 6 7
f. You are required to perform tasks on your job which you considerrelatively unimportant or unnecessary. 1 2 3 4 5 6 7
g. You are able to get the money, supplies, equipment, etc., your workgroup needs to do its work well. 1 2 3 4 5 6 7
h. Your supervisor is friendly and easy to approach. 1 2 3 4 5 6 7
i. Your supervisor offers new ideas for job-related problems. 1 2 3 4 5 6 7
j. A spirit of co-operation exists in your work group. 1 2 3 4 5 6 7
k. Your job responsibilities are clearly defined. 1 2 3 4 5 6 7
l. Responsibility is assigned so that individuals have authority withintheir own area. 1 2 3 4 5 6 7
m. Dealing with other people is part of your job. 1 2 3 4 5 6 7
n. Your supervisor encourages the people who work for him or herto exchange ideas and opinions. 1 2 3 4 5 6 7
227
QUESTION 2
1 2 3 4 5 6 7
Strongly Disagree Tend to Unsure Tend to AgreeStronglyDisagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of the followingstatements:
Circle the most appropriate response: SD D TD U TA A SA
a. Staff members generally trust their managers. 1 2 3 4 5 6 7
b. You are given advance information about changes(policies, procedures, etc.) which might affect you. 1 2 3 4 5 6 7
c. The hotel�s policies are consistently applied to all staffmembers. 1 2 3 4 5 6 7
d. You have opportunities to complete the work you start. 1 2 3 4 5 6 7
e. Procedures are designed so that resources (equipment, people, time,etc.) are used efficiently. 1 2 3 4 5 6 7
f. Your supervisor is attentive to what you say. 1 2 3 4 5 6 7
g. Your supervisor provides the help you need to schedule your workahead of time. 1 2 3 4 5 6 7
h. There is friction in your work group. 1 2 3 4 5 6 7
i. You have opportunities to learn worth while new skills andknowledge in your job. 1 2 3 4 5 6 7
j. New staff members get the on-the-job training they need. 1 2 3 4 5 6 7
k. There is variety in your job. 1 2 3 4 5 6 7
l. Your hours of work are irregular. 1 2 3 4 5 6 7
m. Everything in this hotel is checked, individual judgementis not trusted. 1 2 3 4 5 6 7
n. Being liked is important in getting a promotion. 1 2 3 4 5 6 7
228
QUESTION 3
1 2 3 4 5 6 7
Strongly Disagree Tend to Unsure Tend to Agree StronglyDisagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of thefollowing statements:
Circle the most appropriate response: SD D TD U TA A SA
a. You have good information on where you stand and how yourperformance is evaluated. 1 2 3 4 5 6 7
b. Your supervisor emphasises high standards of performance. 1 2 3 4 5 6 7
c. The ideas and suggestions of staff members are paid attention to. 1 2 3 4 5 6 7
d. You have the opportunity to do a number of different things in yourjob. 1 2 3 4 5 6 7
e. Your supervisor sets an example by working hard himself/herself. 1 2 3 4 5 6 7
f. A friendly atmosphere prevails among most of the membersof your work group. 1 2 3 4 5 6 7
g. Hotel �politics� count in getting a promotion. 1 2 3 4 5 6 7
h. People act as though everyone must be watched or theywill slack off. 1 2 3 4 5 6 7
i. Supervisors generally know what is going on in their work groups. 1 2 3 4 5 6 7
j. You are aware of how well your workgroup is meetingits objectives. 1 2 3 4 5 6 7
k. Your job demands precision. 1 2 3 4 5 6 7
l. Members of your work group trust each other. 1 2 3 4 5 6 7
m. The hotel has a good image to outsiders. 1 2 3 4 5 6 7
n. Working in this hotel is beneficial to your career. 1 2 3 4 5 6 7
o. You have opportunities to make full use of your knowledge andskills in your job. 1 2 3 4 5 6 7
229
QUESTION 4
1 2 3 4 5 6 7
Strongly Disagree Tend to Unsure Tend to Agree StronglyDisagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of thefollowing statements:
Circle the most appropriate response: SD D TD U TA A SA
a. Communication is hindered by following chain of command rules. 1 2 3 4 5 6 7
b. Your supervisor encourages the people who work for them to workas a team. 1 2 3 4 5 6 7
c. It is possible to get accurate information on the policies andobjectives of this hotel. 1 2 3 4 5 6 7
d. The hotel strives to do a better job than other hotelsof the same type. 1 2 3 4 5 6 7
e. The hotel emphasises personal growth and development. 1 2 3 4 5 6 7
f. Managers keep well informed about the needs and problems ofemployees. 1 2 3 4 5 6 7
g. Discipline in this hotel is maintained consistently. 1 2 3 4 5 6 7
h. Your manager is successful in his dealings with higher levels ofmanagement. 1 2 3 4 5 6 7
i. The objectives of the hotel are clearly defined. 1 2 3 4 5 6 7
j. There is conflict (rivalry and hostility) between your departmentand other departments of the hotel. 1 2 3 4 5 6 7
k. Your work is important. 1 2 3 4 5 6 7
l. The way your work group is organised hinders the efficient conductof work. 1 2 3 4 5 6 7
m. This hotel is concerned with assisting the local community. 1 2 3 4 5 6 7
n. Things in this hotel seem to happen contrary to rules andregulations. 1 2 3 4 5 6 7
230
QUESTION 5
1 2 3 4 5 6 7
Strongly Disagree Tend to Unsure Tend to Agree StronglyDisagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of thefollowing statements:
Circle the most appropriate response: SD D TD U TA A SA
a. In this hotel about the only source of information on importantmatters is the grapevine (rumour). 1 2 3 4 5 6 7
b. In this hotel, things are planned so that everyone is gettingin each other�s way. 1 2 3 4 5 6 7
c. Under most circumstances I would recommend this hotel to aprospective staff member. 1 2 3 4 5 6 7
d. Most of the personnel in my department would not want to changeto another department. 1 2 3 4 5 6 7
e. Most members of my work group take pride in their jobs. 1 2 3 4 5 6 7
f. Generally there are friendly and cooperative relationships between thedifferent department of the hotel. 1 2 3 4 5 6 7
g. My department, compared to all other department in the hotelwould be one of the most productive. 1 2 3 4 5 6 7
h. Excessive rules and regulations interfere with how well I am able todo my job. 1 2 3 4 5 6 7
i. Overall, I think my immediate supervisor is doing a good job. 1 2 3 4 5 6 7
j. Compared with other works groups, my work group is under much lesspressure to produce. 1 2 3 4 5 6 7
k. In my job, the opportunities to get to know people are limited. 1 2 3 4 5 6 7
l. Compared to all other similar works groups in the hotel, my groupwould be the most productive. 1 2 3 4 5 6 7
m. Your immediate supervisor is successful in dealing with higherlevels of management. 1 2 3 4 5 6 7
231
Appendix B
Hotel Profile Instrument
232
CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUSTELEPHONE: (07) 5594 8771
HOTEL PROFILE
Please use the 1996/1997 financial year as the basis for your answers:
1. What is your annual room occupancy level in percentage terms?_________________%
2. What is your annual average daily room rate?$_________________
3. What is your rack rate for a standard room? $_________________
4. What is your accommodation business mix?
F.I.T %Conferences %Group Tour %Corporate %Government %Leisure %
100%
5. What is your revenue mix?Rooms %F & B %Other %
100%
6. What is your staffing level? (please include management)
Peak LowFull TimePart Time
Casual
233
047. What is your annual staff turnover percentage? _________________%
8. What is your wage cost to revenue percentage? _________________%
9. Which of the following organisational structures and levels best represents your hotel?(Please tick)
General Manager General Manager General Manager Other (please state)Executive Committee Executive Committee Executive Committee_______________Department Heads Senior Department Heads Department Heads_______________Supervisors Other Department Heads Operational Staff_______________Operational Staff Assistant Dept Heads_______________
Supervisors_______________
Assistant Supervisors_______________
Operational Staff_______________
A ❑ B ❑ C ❑ D ❑
10. What external factors (beyond your control) affected your operational performancelast year?
__________________________________________________________________
__________________________________________________________________
PLEASE ATTACH ADDITIONAL NOTES IF REQUIRED
11. Were there any special internal circumstances that affected your operationalperformance last year?
__________________________________________________________________
__________________________________________________________________
PLEASE ATTACH ADDITIONAL NOTES IF REQUIRED
234
Appendix C
Hotel managers demographics, operation performance and perception of
customer satisfaction
235
CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUSTELEPHONE: (07) 5594 8771
GENERAL MANAGER, SENIOR EXECUTIVESAND DEPARTMENT HEADS ONLY
PART 1 - DEMOGRAPHIC DETAILS
Please tick one box only for each question
1. Gender: Male ❑Female ❑
2. Age: 15-24yrs ❑25-34 � ❑35-44 ❑45-54 � ❑55-64 ❑65+ ❑
3. Education - Highest level attempted (not necessarily completed):
Primary ❑Secondary ❑Post-Secondary Certificate ❑Apprenticeship ❑Associate/Diploma ❑Degree ❑Post Graduate Diploma/Degree ❑
236
4. Total length of service with the hotel:
0-2 yrs ❑3-5 yrs ❑6-8 � ❑9-11 � ❑12-14 � ❑15-17 � ❑
5. Length of time in present job:
0-2yrs ❑3-5 yrs ❑6-8 � ❑9-11 � ❑12-14 � ❑15-17 � ❑
6. Current gross salary: $30 � 39000 ❑$40 � 49000 ❑$50 � 59000 ❑$60 � 69000 ❑$70 � 79000 ❑$80 � 89000 ❑$90 � 99000 ❑
over $100,000 ❑
7. Do you receive additional benefits?
Yes ❑No ❑
8. Please give a dollar value of the benefits: ___________________
237
9. When did you last undertake a formal training or education program (in house orexternal?
0 months to 1 year ❑1 to 2 years ❑2 to 3 years ❑3 to 4 years ❑4 to 5 years ❑5 to 6 years ❑6 to 7 years ❑
10. Do you feel that you need to undertake additional education or training for yourpresent position?
Yes ❑No ❑
If yes please indicate what education or training would be most appropriate:
_____________________________________________________________________
_____________________________________________________________________
238
PART 2 � PERFORMANCE INDICATORS
Please rate the hotels performance in the following areas (tick the appropriate box):
It is important that you use your judgement alone in this rating.
11. FINANCIAL PERFORMANCE
Revenue UnderBudget
MarginallyUnderBudget
On BudgetMarginally
AboveBudget
Well AboveBudget
A. Food &Beverage ❑ ❑ ❑ ❑ ❑
B. Rooms ❑ ❑ ❑ ❑ ❑
C. Overall ❑ ❑ ❑ ❑ ❑
12. FINANCIAL PERFORMANCE
GrossOperating
Profit
UnderBudget
MarginallyUnderBudget
On Budget MarginallyAbove Budget
Well AboveBudget
A. Food &Beverage ❑ ❑ ❑ ❑ ❑
B. Rooms ❑ ❑ ❑ ❑ ❑
C. Overall ❑ ❑ ❑ ❑ ❑
13. OPERATIONAL PERFORMANCE
Area VeryMarginal
UnderPerforming
AcceptablePerformance
GoodPerformance
OutstandingPerformance
A. Food &Beverage ❑ ❑ ❑ ❑ ❑
B. Rooms ❑ ❑ ❑ ❑ ❑
C. Overall ❑ ❑ ❑ ❑ ❑
239
14. CUSTOMER SATISFACTION
Area VeryLow Low Average High Extremely
High
A. Food &Beverage ❑ ❑ ❑ ❑ ❑
B. Rooms ❑ ❑ ❑ ❑ ❑
C. Overall ❑ ❑ ❑ ❑ ❑
15. Please indicate any specific techniques used to ascertain customer satisfaction:
In room comment cards ❑Restaurant comment cards ❑Staff incident/compliment reports ❑Own telephone survey of guests ❑Market research survey ❑Focus group ❑
Other (please state) ______________________________
16. Overall Performance Rating
Please take into account all factors that affected performance both internally and
externally (tick your response)
1-Poor to 5-Outstanding
1 ❑ 2 ❑ 3 ❑ 4 ❑ 5 ❑
17. Who do you consider to be your main competitors?
Please place in the collection box in the human resources office at your earliest convenience.
240
Appendix D
Staff Demographic Data and Contingency Table Analyses
241
HOTEL hotel code by GENDER gender
GENDER Page 1 of 1 Count | Col Pct |male female | Row | 1 | 2 | TotalHOTEL --------+--------+--------+ 1 | 290 | 260 | 550 | 34.7 | 28.7 | 31.6 +--------+--------+ 3 | 55 | 66 | 121 | 6.6 | 7.3 | 7.0 +--------+--------+ 4 | 128 | 146 | 274 | 15.3 | 16.1 | 15.7 +--------+--------+ 5 | 29 | 25 | 54 | 3.5 | 2.8 | 3.1 +--------+--------+ 6 | 45 | 42 | 87 | 5.4 | 4.6 | 5.0 +--------+--------+ 7 | 20 | 27 | 47 | 2.4 | 3.0 | 2.7 +--------+--------+ 8 | 20 | 29 | 49 | 2.4 | 3.2 | 2.8 +--------+--------+ 9 | 9 | 37 | 46 | 1.1 | 4.1 | 2.6 +--------+--------+ 10 | 15 | 24 | 39 | 1.8 | 2.6 | 2.2 +--------+--------+ 11 | 45 | 41 | 86 | 5.4 | 4.5 | 4.9 +--------+--------+ 12 | 39 | 51 | 90 | 4.7 | 5.6 | 5.2 +--------+--------+ 13 | 14 | 18 | 32 | 1.7 | 2.0 | 1.8 +--------+--------+ 14 | 89 | 103 | 192 | 10.7 | 11.4 | 11.0 +--------+--------+ 15 | 37 | 37 | 74 | 4.4 | 4.1 | 4.3 +--------+--------+ Column 835 906 1741 Total 48.0 52.0 100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 26.49008 13 .01460Likelihood Ratio 27.73679 13 .00985Mantel-Haenszel test for 4.09789 1 .04294 linear association
Minimum Expected Frequency - 15.348
Number of Missing Observations: 51
242
HOTEL hotel code by AGE age
AGE Page 1 of 1 Count | Col Pct |15-24yrs 24-34yrs 35-44yrs 45-54yrs 55-64yrs 65+yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | TotalHOTEL --------+--------+--------+--------+--------+--------+--------+ 1 | 176 | 174 | 105 | 71 | 18 | 1 | 545 | 33.3 | 28.9 | 30.9 | 33.5 | 40.0 | 33.3 | 31.5 +--------+--------+--------+--------+--------+--------+ 3 | 43 | 46 | 18 | 10 | | | 117 | 8.1 | 7.6 | 5.3 | 4.7 | | | 6.8 +--------+--------+--------+--------+--------+--------+ 4 | 64 | 96 | 57 | 48 | 10 | | 275 | 12.1 | 15.9 | 16.8 | 22.6 | 22.2 | | 15.9 +--------+--------+--------+--------+--------+--------+ 5 | 13 | 26 | 10 | 3 | 2 | | 54 | 2.5 | 4.3 | 2.9 | 1.4 | 4.4 | | 3.1 +--------+--------+--------+--------+--------+--------+ 6 | 39 | 27 | 12 | 8 | 1 | | 87 | 7.4 | 4.5 | 3.5 | 3.8 | 2.2 | | 5.0 +--------+--------+--------+--------+--------+--------+ 7 | 16 | 14 | 13 | 3 | | | 46 | 3.0 | 2.3 | 3.8 | 1.4 | | | 2.7 +--------+--------+--------+--------+--------+--------+ 8 | 20 | 19 | 4 | 4 | 1 | | 48 | 3.8 | 3.2 | 1.2 | 1.9 | 2.2 | | 2.8 +--------+--------+--------+--------+--------+--------+ 9 | 14 | 19 | 7 | 6 | | 1 | 47 | 2.7 | 3.2 | 2.1 | 2.8 | | 33.3 | 2.7 +--------+--------+--------+--------+--------+--------+ 10 | 8 | 19 | 11 | 1 | | | 39 | 1.5 | 3.2 | 3.2 | .5 | | | 2.3 +--------+--------+--------+--------+--------+--------+ 11 | 29 | 29 | 16 | 11 | 2 | | 87 | 5.5 | 4.8 | 4.7 | 5.2 | 4.4 | | 5.0 +--------+--------+--------+--------+--------+--------+ 12 | 17 | 28 | 28 | 13 | 4 | | 90 | 3.2 | 4.6 | 8.2 | 6.1 | 8.9 | | 5.2 +--------+--------+--------+--------+--------+--------+ 13 | 11 | 11 | 4 | 4 | 2 | | 32 | 2.1 | 1.8 | 1.2 | 1.9 | 4.4 | | 1.8 +--------+--------+--------+--------+--------+--------+ 14 | 66 | 71 | 28 | 20 | 4 | 1 | 190 | 12.5 | 11.8 | 8.2 | 9.4 | 8.9 | 33.3 | 11.0 +--------+--------+--------+--------+--------+--------+ 15 | 12 | 24 | 27 | 10 | 1 | | 74 | 2.3 | 4.0 | 7.9 | 4.7 | 2.2 | | 4.3 +--------+--------+--------+--------+--------+--------+ Column 528 603 340 212 45 3 1731 Total 30.5 34.8 19.6 12.2 2.6 .2 100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 108.65497 65 .00056Likelihood Ratio 108.98016 65 .00052Mantel-Haenszel test for .02426 1 .87622 linear association
Minimum Expected Frequency - .055Cells with Expected Frequency < 5 - 28 OF 84 ( 33.3%)
Number of Missing Observations: 61
243
HOTEL hotel code by EDUCAT education level
EDUCAT Page 1 of 1 Count | Col Pct |secondar post sec apprenti assocait degree post gra |y ondary ceship e diplom d Row | 1 | 2 | 3 | 4 | 5 | 6 | TotalHOTEL --------+--------+--------+--------+--------+--------+--------+ 1 | 200 | 82 | 81 | 75 | 82 | 18 | 538 | 33.3 | 31.4 | 34.8 | 26.9 | 28.9 | 34.0 | 31.4 +--------+--------+--------+--------+--------+--------+ 3 | 51 | 11 | 22 | 19 | 11 | 4 | 118 | 8.5 | 4.2 | 9.4 | 6.8 | 3.9 | 7.5 | 6.9 +--------+--------+--------+--------+--------+--------+ 4 | 106 | 39 | 36 | 40 | 46 | 4 | 271 | 17.6 | 14.9 | 15.5 | 14.3 | 16.2 | 7.5 | 15.8 +--------+--------+--------+--------+--------+--------+ 5 | 15 | 9 | 9 | 12 | 5 | 2 | 52 | 2.5 | 3.4 | 3.9 | 4.3 | 1.8 | 3.8 | 3.0 +--------+--------+--------+--------+--------+--------+ 6 | 34 | 11 | 8 | 15 | 19 | 1 | 88 | 5.7 | 4.2 | 3.4 | 5.4 | 6.7 | 1.9 | 5.1 +--------+--------+--------+--------+--------+--------+ 7 | 10 | 4 | 8 | 10 | 11 | 2 | 45 | 1.7 | 1.5 | 3.4 | 3.6 | 3.9 | 3.8 | 2.6 +--------+--------+--------+--------+--------+--------+ 8 | 17 | 6 | 5 | 9 | 10 | 2 | 49 | 2.8 | 2.3 | 2.1 | 3.2 | 3.5 | 3.8 | 2.9 +--------+--------+--------+--------+--------+--------+ 9 | 21 | 9 | 2 | 5 | 7 | 1 | 45 | 3.5 | 3.4 | .9 | 1.8 | 2.5 | 1.9 | 2.6 +--------+--------+--------+--------+--------+--------+ 10 | 17 | 5 | 3 | 4 | 7 | 3 | 39 | 2.8 | 1.9 | 1.3 | 1.4 | 2.5 | 5.7 | 2.3 +--------+--------+--------+--------+--------+--------+ 11 | 24 | 17 | 8 | 16 | 17 | 4 | 86 | 4.0 | 6.5 | 3.4 | 5.7 | 6.0 | 7.5 | 5.0 +--------+--------+--------+--------+--------+--------+ 12 | 26 | 16 | 16 | 17 | 11 | 2 | 88 | 4.3 | 6.1 | 6.9 | 6.1 | 3.9 | 3.8 | 5.1 +--------+--------+--------+--------+--------+--------+ 13 | 6 | 9 | 3 | 7 | 3 | 3 | 31 | 1.0 | 3.4 | 1.3 | 2.5 | 1.1 | 5.7 | 1.8 +--------+--------+--------+--------+--------+--------+ 14 | 49 | 30 | 17 | 40 | 44 | 6 | 186 | 8.2 | 11.5 | 7.3 | 14.3 | 15.5 | 11.3 | 10.9 +--------+--------+--------+--------+--------+--------+ 15 | 25 | 13 | 15 | 10 | 11 | 1 | 75 | 4.2 | 5.0 | 6.4 | 3.6 | 3.9 | 1.9 | 4.4 +--------+--------+--------+--------+--------+--------+ Column 601 261 233 279 284 53 1711 Total 35.1 15.3 13.6 16.3 16.6 3.1 100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 88.74589 65 .02685Likelihood Ratio 89.06722 65 .02546Mantel-Haenszel test for 9.25581 1 .00235 linear association
Minimum Expected Frequency - .960Cells with Expected Frequency < 5 - 13 OF 84 ( 15.5%)
Number of Missing Observations: 81
244
HOTEL hotel code by LENGTH_S length of service
LENGTH_S Page 1 of 1 Count | Col Pct |0-2yrs 3-5yrs 6-8yrs 9-11yrs 12-14yrs 15-17yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | TotalHOTEL --------+--------+--------+--------+--------+--------+--------+ 1 | 268 | 124 | 70 | 67 | 15 | | 544 | 26.4 | 30.8 | 37.6 | 70.5 | 55.6 | | 31.3 +--------+--------+--------+--------+--------+--------+ 3 | 79 | 23 | 19 | | | | 121 | 7.8 | 5.7 | 10.2 | | | | 7.0 +--------+--------+--------+--------+--------+--------+ 4 | 152 | 78 | 44 | | | | 274 | 14.9 | 19.4 | 23.7 | | | | 15.8 +--------+--------+--------+--------+--------+--------+ 5 | 27 | 20 | 5 | 2 | 1 | | 55 | 2.7 | 5.0 | 2.7 | 2.1 | 3.7 | | 3.2 +--------+--------+--------+--------+--------+--------+ 6 | 58 | 22 | 7 | 1 | | | 88 | 5.7 | 5.5 | 3.8 | 1.1 | | | 5.1 +--------+--------+--------+--------+--------+--------+ 7 | 40 | 5 | | 1 | | 1 | 47 | 3.9 | 1.2 | | 1.1 | | 9.1 | 2.7 +--------+--------+--------+--------+--------+--------+ 8 | 46 | 1 | 2 | | | | 49 | 4.5 | .2 | 1.1 | | | | 2.8 +--------+--------+--------+--------+--------+--------+ 9 | 32 | 9 | 5 | | | | 46 | 3.1 | 2.2 | 2.7 | | | | 2.6 +--------+--------+--------+--------+--------+--------+ 10 | 38 | | 1 | | | | 39 | 3.7 | | .5 | | | | 2.2 +--------+--------+--------+--------+--------+--------+ 11 | 59 | 28 | | | | | 87 | 5.8 | 6.9 | | | | | 5.0 +--------+--------+--------+--------+--------+--------+ 12 | 47 | 22 | 9 | 11 | | 1 | 90 | 4.6 | 5.5 | 4.8 | 11.6 | | 9.1 | 5.2 +--------+--------+--------+--------+--------+--------+ 13 | 19 | 5 | 1 | 2 | 1 | 4 | 32 | 1.9 | 1.2 | .5 | 2.1 | 3.7 | 36.4 | 1.8 +--------+--------+--------+--------+--------+--------+ 14 | 122 | 36 | 8 | 11 | 10 | 5 | 192 | 12.0 | 8.9 | 4.3 | 11.6 | 37.0 | 45.5 | 11.0 +--------+--------+--------+--------+--------+--------+ 15 | 30 | 30 | 15 | | | | 75 | 2.9 | 7.4 | 8.1 | | | | 4.3 +--------+--------+--------+--------+--------+--------+ Column 1017 403 186 95 27 11 1739 Total 58.5 23.2 10.7 5.5 1.6 .6 100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 364.57871 65 .00000Likelihood Ratio 371.23288 65 .00000Mantel-Haenszel test for 7.96189 1 .00478 linear association
Minimum Expected Frequency - .202Cells with Expected Frequency < 5 - 40 OF 84 ( 47.6%)
Number of Missing Observations: 53
245
HOTEL hotel code by LENGTH_J length of job
LENGTH_J Page 1 of 1 Count | Col Pct |0-2yrs 3-5yrs 6-8yrs 9-11yrs 12-14yrs 15-17yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | TotalHOTEL --------+--------+--------+--------+--------+--------+--------+ 1 | 312 | 118 | 49 | 49 | 11 | | 539 | 27.5 | 33.0 | 40.2 | 62.0 | 57.9 | | 31.2 +--------+--------+--------+--------+--------+--------+ 3 | 85 | 23 | 11 | | 1 | | 120 | 7.5 | 6.4 | 9.0 | | 5.3 | | 7.0 +--------+--------+--------+--------+--------+--------+ 4 | 173 | 70 | 23 | 3 | 1 | 2 | 272 | 15.3 | 19.6 | 18.9 | 3.8 | 5.3 | 14.3 | 15.8 +--------+--------+--------+--------+--------+--------+ 5 | 29 | 17 | 2 | 3 | 1 | 1 | 53 | 2.6 | 4.7 | 1.6 | 3.8 | 5.3 | 7.1 | 3.1 +--------+--------+--------+--------+--------+--------+ 6 | 60 | 22 | 4 | 1 | | | 87 | 5.3 | 6.1 | 3.3 | 1.3 | | | 5.0 +--------+--------+--------+--------+--------+--------+ 7 | 40 | 4 | 1 | 1 | | 1 | 47 | 3.5 | 1.1 | .8 | 1.3 | | 7.1 | 2.7 +--------+--------+--------+--------+--------+--------+ 8 | 46 | | 1 | 2 | | | 49 | 4.1 | | .8 | 2.5 | | | 2.8 +--------+--------+--------+--------+--------+--------+ 9 | 37 | 4 | 4 | 1 | | | 46 | 3.3 | 1.1 | 3.3 | 1.3 | | | 2.7 +--------+--------+--------+--------+--------+--------+ 10 | 35 | 1 | | 2 | | | 38 | 3.1 | .3 | | 2.5 | | | 2.2 +--------+--------+--------+--------+--------+--------+ 11 | 66 | 20 | 1 | | | | 87 | 5.8 | 5.6 | .8 | | | | 5.0 +--------+--------+--------+--------+--------+--------+ 12 | 56 | 19 | 8 | 7 | | | 90 | 4.9 | 5.3 | 6.6 | 8.9 | | | 5.2 +--------+--------+--------+--------+--------+--------+ 13 | 21 | 3 | | 3 | 1 | 3 | 31 | 1.9 | .8 | | 3.8 | 5.3 | 21.4 | 1.8 +--------+--------+--------+--------+--------+--------+ 14 | 137 | 32 | 7 | 7 | 4 | 5 | 192 | 12.1 | 8.9 | 5.7 | 8.9 | 21.1 | 35.7 | 11.1 +--------+--------+--------+--------+--------+--------+ 15 | 37 | 25 | 11 | | | 2 | 75 | 3.3 | 7.0 | 9.0 | | | 14.3 | 4.3 +--------+--------+--------+--------+--------+--------+ Column 1134 358 122 79 19 14 1726 Total 65.7 20.7 7.1 4.6 1.1 .8 100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 212.87162 65 .00000Likelihood Ratio 233.03936 65 .00000Mantel-Haenszel test for 5.29004 1 .02145 linear association
Minimum Expected Frequency - .251Cells with Expected Frequency < 5 - 43 OF 84 ( 51.2%)
Number of Missing Observations: 66
246
HOTEL hotel code by GRS_SAL current gross salaryCount |$0-5000 $6-10,00 $11-15,0 $16-20,0 $21-25,0 $26-30,0 $31-35,0 $36-40,0 $41-45,0 $46-50,0 over $50 Col Pct | 0 00 00 00 00 00 00 00 00 ,000 Row | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | TotalHOTEL --------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 1 | 16 | 33 | 48 | 76 | 166 | 106 | 41 | 24 | 10 | | 2 | 522 | 20.0 | 35.1 | 30.8 | 32.8 | 31.3 | 31.3 | 28.3 | 45.3 | 45.5 | | 13.3 | 31.3 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 3 | | 4 | 6 | 16 | 46 | 24 | 16 | 3 | 1 | | 1 | 117 | | 4.3 | 3.8 | 6.9 | 8.7 | 7.1 | 11.0 | 5.7 | 4.5 | | 6.7 | 7.0 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 4 | 18 | 20 | 36 | 30 | 72 | 56 | 17 | 6 | 2 | | 4 | 261 | 22.5 | 21.3 | 23.1 | 12.9 | 13.6 | 16.5 | 11.7 | 11.3 | 9.1 | | 26.7 | 15.6 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 5 | 4 | 1 | 3 | 3 | 11 | 14 | 11 | 3 | | | | 50 | 5.0 | 1.1 | 1.9 | 1.3 | 2.1 | 4.1 | 7.6 | 5.7 | | | | 3.0 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 6 | 9 | 4 | 5 | 11 | 32 | 13 | 6 | 1 | | | | 81 | 11.3 | 4.3 | 3.2 | 4.7 | 6.0 | 3.8 | 4.1 | 1.9 | | | | 4.9 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 7 | 4 | 1 | 3 | 7 | 9 | 15 | 6 | | 1 | | | 46 | 5.0 | 1.1 | 1.9 | 3.0 | 1.7 | 4.4 | 4.1 | | 4.5 | | | 2.8 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 8 | 3 | 6 | 2 | 3 | 13 | 13 | 6 | | | | | 46 | 3.8 | 6.4 | 1.3 | 1.3 | 2.4 | 3.8 | 4.1 | | | | | 2.8 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 9 | 7 | 1 | 7 | 7 | 12 | 8 | | 1 | | | 1 | 44 | 8.8 | 1.1 | 4.5 | 3.0 | 2.3 | 2.4 | | 1.9 | | | 6.7 | 2.6 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 10 | 2 | 2 | 2 | 3 | 19 | 6 | 1 | 1 | | | | 36 | 2.5 | 2.1 | 1.3 | 1.3 | 3.6 | 1.8 | .7 | 1.9 | | | | 2.2 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 11 | 8 | 1 | 7 | 17 | 33 | 13 | 5 | 1 | | | 1 | 86 | 10.0 | 1.1 | 4.5 | 7.3 | 6.2 | 3.8 | 3.4 | 1.9 | | | 6.7 | 5.2 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 12 | | 3 | 4 | 11 | 33 | 16 | 15 | 6 | | | | 88 | | 3.2 | 2.6 | 4.7 | 6.2 | 4.7 | 10.3 | 11.3 | | | | 5.3 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 13 | | 1 | | 4 | 8 | 17 | 1 | | | | | 31 | | 1.1 | | 1.7 | 1.5 | 5.0 | .7 | | | | | 1.9 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 14 | 6 | 14 | 24 | 29 | 54 | 30 | 13 | 4 | 8 | 1 | 6 | 189 | 7.5 | 14.9 | 15.4 | 12.5 | 10.2 | 8.8 | 9.0 | 7.5 | 36.4 | 50.0 | 40.0 | 11.3 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 15 | 3 | 3 | 9 | 15 | 23 | 8 | 7 | 3 | | 1 | | 72 | 3.8 | 3.2 | 5.8 | 6.5 | 4.3 | 2.4 | 4.8 | 5.7 | | 50.0 | | 4.3 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ Column 80 94 156 232 531 339 145 53 22 2 15 1669 Total 4.8 5.6 9.3 13.9 31.8 20.3 8.7 3.2 1.3 .1 .9 100.0
247
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 244.13788 130 .00000Likelihood Ratio 245.77581 130 .00000Mantel-Haenszel test for .00172 1 .96692 linear association
Minimum Expected Frequency - .037Cells with Expected Frequency < 5 - 85 OF 154 ( 55.2%)
Number of Missing Observations: 123
248
HOTEL hotel code by HOURS hours worked per week Count | Col Pct |0-5hrs 6-10hrs 11-15hrs 16-20hrs 21-25hrs 26-30hrs 31-35hrs 36-40hrs 41-45hrs 46-50hrs over 50 | hrs Row | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | TotalHOTEL --------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 1 | 1 | 7 | 7 | 35 | 31 | 58 | 54 | 271 | 45 | 22 | 10 | 541 | 12.5 | 21.2 | 18.4 | 35.7 | 27.0 | 34.9 | 40.3 | 35.4 | 22.5 | 25.0 | 12.2 | 31.3 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 3 | | 1 | 2 | 3 | 6 | 18 | 17 | 40 | 14 | 9 | 9 | 119 | | 3.0 | 5.3 | 3.1 | 5.2 | 10.8 | 12.7 | 5.2 | 7.0 | 10.2 | 11.0 | 6.9 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 4 | 3 | 11 | 10 | 27 | 18 | 22 | 16 | 103 | 39 | 18 | 6 | 273 | 37.5 | 33.3 | 26.3 | 27.6 | 15.7 | 13.3 | 11.9 | 13.5 | 19.5 | 20.5 | 7.3 | 15.8 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 5 | 1 | | 2 | 2 | 2 | 3 | | 26 | 12 | 3 | 3 | 54 | 12.5 | | 5.3 | 2.0 | 1.7 | 1.8 | | 3.4 | 6.0 | 3.4 | 3.7 | 3.1 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 6 | | 2 | 1 | 3 | 11 | 6 | 7 | 41 | 11 | 3 | 2 | 87 | | 6.1 | 2.6 | 3.1 | 9.6 | 3.6 | 5.2 | 5.4 | 5.5 | 3.4 | 2.4 | 5.0 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 7 | | 1 | 1 | 1 | 2 | 4 | 1 | 17 | 12 | 5 | 2 | 46 | | 3.0 | 2.6 | 1.0 | 1.7 | 2.4 | .7 | 2.2 | 6.0 | 5.7 | 2.4 | 2.7 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 8 | | | 1 | 1 | 6 | 4 | 3 | 19 | 5 | 5 | 5 | 49 | | | 2.6 | 1.0 | 5.2 | 2.4 | 2.2 | 2.5 | 2.5 | 5.7 | 6.1 | 2.8 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 9 | | 1 | 3 | 1 | 4 | 7 | 2 | 20 | 6 | | 2 | 46 | | 3.0 | 7.9 | 1.0 | 3.5 | 4.2 | 1.5 | 2.6 | 3.0 | | 2.4 | 2.7 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 10 | 1 | 1 | 1 | 4 | | 2 | 1 | 19 | 3 | 1 | 4 | 37 | 12.5 | 3.0 | 2.6 | 4.1 | | 1.2 | .7 | 2.5 | 1.5 | 1.1 | 4.9 | 2.1 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 11 | | 1 | 2 | 5 | 2 | 7 | 5 | 38 | 18 | 3 | 6 | 87 | | 3.0 | 5.3 | 5.1 | 1.7 | 4.2 | 3.7 | 5.0 | 9.0 | 3.4 | 7.3 | 5.0 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 12 | | 2 | 1 | 1 | 7 | 3 | 5 | 52 | 9 | 8 | 2 | 90 | | 6.1 | 2.6 | 1.0 | 6.1 | 1.8 | 3.7 | 6.8 | 4.5 | 9.1 | 2.4 | 5.2 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 13 | | 1 | 1 | 1 | | 1 | 1 | 22 | 5 | | | 32 | | 3.0 | 2.6 | 1.0 | | .6 | .7 | 2.9 | 2.5 | | | 1.9 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 14 | 1 | 5 | 6 | 10 | 19 | 22 | 17 | 66 | 13 | 9 | 23 | 191 | 12.5 | 15.2 | 15.8 | 10.2 | 16.5 | 13.3 | 12.7 | 8.6 | 6.5 | 10.2 | 28.0 | 11.1 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ 15 | 1 | | | 4 | 7 | 9 | 5 | 31 | 8 | 2 | 8 | 75 | 12.5 | | | 4.1 | 6.1 | 5.4 | 3.7 | 4.1 | 4.0 | 2.3 | 9.8 | 4.3 +--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+ Column 8 33 38 98 115 166 134 765 200 88 82 1727 Total .5 1.9 2.2 5.7 6.7 9.6 7.8 44.3 11.6 5.1 4.7 100.0
249
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 244.36246 130 .00000Likelihood Ratio 250.87489 130 .00000Mantel-Haenszel test for 3.72831 1 .05350 linear association
Minimum Expected Frequency - .148Cells with Expected Frequency < 5 - 87 OF 154 ( 56.5%)
Number of Missing Observations: 65
250
HOTEL hotel code by MODEMPL mode of employment
MODEMPL Page 1 of 1 Count | Col Pct |full tim part tim casual |e e Row | 1 | 2 | 3 | TotalHOTEL --------+--------+--------+--------+ 1 | 304 | 48 | 190 | 542 | 29.2 | 26.2 | 38.4 | 31.5 +--------+--------+--------+ 3 | 71 | 46 | 2 | 119 | 6.8 | 25.1 | .4 | 6.9 +--------+--------+--------+ 4 | 151 | 11 | 106 | 268 | 14.5 | 6.0 | 21.4 | 15.6 +--------+--------+--------+ 5 | 44 | | 8 | 52 | 4.2 | | 1.6 | 3.0 +--------+--------+--------+ 6 | 48 | 5 | 33 | 86 | 4.6 | 2.7 | 6.7 | 5.0 +--------+--------+--------+ 7 | 37 | 4 | 5 | 46 | 3.6 | 2.2 | 1.0 | 2.7 +--------+--------+--------+ 8 | 34 | | 15 | 49 | 3.3 | | 3.0 | 2.9 +--------+--------+--------+ 9 | 28 | | 18 | 46 | 2.7 | | 3.6 | 2.7 +--------+--------+--------+ 10 | 27 | 1 | 9 | 37 | 2.6 | .5 | 1.8 | 2.2 +--------+--------+--------+ 11 | 44 | 42 | 1 | 87 | 4.2 | 23.0 | .2 | 5.1 +--------+--------+--------+ 12 | 74 | 3 | 13 | 90 | 7.1 | 1.6 | 2.6 | 5.2 +--------+--------+--------+ 13 | 28 | | 3 | 31 | 2.7 | | .6 | 1.8 +--------+--------+--------+ 14 | 104 | 8 | 80 | 192 | 10.0 | 4.4 | 16.2 | 11.2 +--------+--------+--------+ 15 | 47 | 15 | 12 | 74 | 4.5 | 8.2 | 2.4 | 4.3 +--------+--------+--------+ Column 1041 183 495 1719 Total 60.6 10.6 28.8 100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 394.58509 26 .00000Likelihood Ratio 372.78829 26 .00000Mantel-Haenszel test for 6.83703 1 .00893 linear association
Minimum Expected Frequency - 3.300Cells with Expected Frequency < 5 - 4 OF 42 ( 9.5%)
Number of Missing Observations: 73
251
HOTEL hotel code by TRAINSES last training session
TRAINSES Page 1 of1 Count | Col Pct |0-1yr 1-2yrs 2-3yrs 3-4yrs 4-5yrs 5-6yrs 6-7yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | 7 |TotalHOTEL --------+--------+--------+--------+--------+--------+--------+--------+ 1 | 369 | 73 | 34 | 11 | 8 | 3 | 19 |517 | 30.8 | 30.7 | 34.3 | 27.5 | 27.6 | 27.3 | 40.4 |31.1 +--------+--------+--------+--------+--------+--------+--------+ 3 | 82 | 16 | 10 | 2 | | | 4 |114 | 6.8 | 6.7 | 10.1 | 5.0 | | | 8.5 |6.9 +--------+--------+--------+--------+--------+--------+--------+ 4 | 167 | 51 | 16 | 6 | 6 | 2 | 10 |258 | 13.9 | 21.4 | 16.2 | 15.0 | 20.7 | 18.2 | 21.3 |15.5 +--------+--------+--------+--------+--------+--------+--------+ 5 | 44 | 7 | | 1 | 1 | | 1 |54 | 3.7 | 2.9 | | 2.5 | 3.4 | | 2.1 |3.2 +--------+--------+--------+--------+--------+--------+--------+ 6 | 66 | 7 | 5 | 2 | 1 | 2 | 2 |85 | 5.5 | 2.9 | 5.1 | 5.0 | 3.4 | 18.2 | 4.3 |5.1 +--------+--------+--------+--------+--------+--------+--------+ 7 | 38 | 6 | 1 | | | | |45 | 3.2 | 2.5 | 1.0 | | | | |2.7 +--------+--------+--------+--------+--------+--------+--------+ 8 | 31 | 10 | 2 | 1 | 1 | | 2 |47 | 2.6 | 4.2 | 2.0 | 2.5 | 3.4 | | 4.3 |2.8 +--------+--------+--------+--------+--------+--------+--------+ 9 | 36 | 6 | | 2 | 2 | | |46 | 3.0 | 2.5 | | 5.0 | 6.9 | | |2.8 +--------+--------+--------+--------+--------+--------+--------+ 10 | 30 | 3 | | | 1 | | 1 |35 | 2.5 | 1.3 | | | 3.4 | | 2.1 |2.1 +--------+--------+--------+--------+--------+--------+--------+ 11 | 49 | 10 | 7 | 7 | 2 | 1 | 3 |79 | 4.1 | 4.2 | 7.1 | 17.5 | 6.9 | 9.1 | 6.4 |4.8 +--------+--------+--------+--------+--------+--------+--------+ 12 | 73 | 6 | 4 | 2 | 2 | | 1 |88 | 6.1 | 2.5 | 4.0 | 5.0 | 6.9 | | 2.1 |5.3 +--------+--------+--------+--------+--------+--------+--------+ 13 | 29 | 1 | | | | | 2 |32 | 2.4 | .4 | | | | | 4.3 |1.9 +--------+--------+--------+--------+--------+--------+--------+ 14 | 135 | 28 | 16 | 3 | 2 | 3 | 1 |188 | 11.3 | 11.8 | 16.2 | 7.5 | 6.9 | 27.3 | 2.1 |11.3 +--------+--------+--------+--------+--------+--------+--------+ 15 | 50 | 14 | 4 | 3 | 3 | | 1 |75 | 4.2 | 5.9 | 4.0 | 7.5 | 10.3 | | 2.1 |4.5 +--------+--------+--------+--------+--------+--------+--------+
252
Column 1199 238 99 40 29 11 471663 Total 72.1 14.3 6.0 2.4 1.7 .7 2.8100.0
Chi-Square Value DF Significance-------------------- ----------- ---- ------------
Pearson 94.58367 78 .09742Likelihood Ratio 110.43554 78 .00922Mantel-Haenszel test for 1.72676 1 .18882 linear association
Minimum Expected Frequency - .212Cells with Expected Frequency < 5 - 59 OF 98 ( 60.2%)
Number of Missing Observations: 129
253
Appendix E
Reliability Analysis and
Principal Components Analysis of Employee Organisational Climate
Data
254
1. Variables
1a. Opportunity for independent thought and action exists in your job.1b. your job requires a high level of skill and training.1c. You are required to meet rigid standards of quality in your work.1d. Staff members generally trust their supervisors.1e. The methods of your work are kept up to date.1f. You are required to perform tasks on your job which you consider relatively unimportant orunnecessary.1g. You are able to get the money, supplies, equipment, etc. your work group needs to do its work well.1h. Your supervisor is friendly and easy to approach.1i. Your supervisor offers new ideas for job and related problems.1j. A spirit of cooperation exists in your workgroup.1k. Your job responsibilities are clearly defined.1l. Responsibility is assigned so that individuals have authority within their own area.1m. Dealing with other people is part of your job.1n. Your supervisor encourages the people who work for him or her to exchange ideas and opinions.
2a. Staff members generally trust their managers.2b. You are given advanced information about changes which might affect you.2c. The hotel�s policies are consistently applied to all staff members.2d. You have opportunities to complete the work you start.2e. Procedures are designed so that resources are used efficiently.2f. Your supervisor is attentive to what you say.2g. Your supervisor provides the help you need to schedule your work ahead of time.2h. there is friction in your workgroup.2i. You have opportunities to learn worthwhile skills and knowledge in your job.2j. New staff members get on-the-job training they need.2k. There is variety in your job.2l. Your hours of work are irregular.2m. Everything in this hotel is checked, individual judgement is not trusted.2n. Being liked is important in getting a promotion.
3a. You have good information on where you stand and how your performance is evaluated.3b. Your superior emphasises high standards of performance.3c. The ideas and suggestions of staff members are paid attention to.3d. you have the opportunity to do a number of different things in your job.3e. Your supervisor sets an example by working hard himself or herself.3f. A friendly atmosphere prevails among most of the members of your workgroup.3g. Hotel politics count in getting a promotion.3h. People act as though everyone must be watched or they will slacken off.3i. Supervisors generally know what is going on in their work groups.3j. You are aware of how well your work group is meeting its objectives.3k. Your job demands precision.3l. Members of your work group trust each other.3m. The hotel has a good image to outsiders.3n. Working in this hotel is beneficial to your career.3o. You have opportunities to make full use of your knowledge and skills in your job.
4a. Communication is hindered by following chain of command rules.4b. Your supervisor encourages the people who work for them to work as a team.4c. It is possible to get accurate information on the policies and objectives of this hotel.4d. The hotel strives to do a better job than other hotels of the same type.4e. The hotel emphasises personal growth and development.4f. Managers keep well informed about the needs and problems of employees.4g. Discipline in this hotel is maintained consistently.4h. Your manager is successful in his dealing with higher levels of management.4i. The objectives of the hotel are clearly defined.4j. There is conflict between your department and other departments of the hotel.4k. Your work is important.4l. The way your work group is organised hinders the efficient conduct of work.
255
4m. This hotel is concerned with assisting the local community.4n. Things in this hotel seem to happen contrary to rules and regulations.
5a. In this hotel the only source of information on important matters is the grapevine.5b. In this hotel things are planned so that everyone is getting in each others� way.5c. Under most circumstances I would recommend this hotel to a prospective staff member.5d. Most of the personnel in my department would not want to change to another department.5e. Most members of my work group take pride in their jobs.5f. Generally there are friendly and co-operative relationships between the different departments of thehotel.5g. My department, compared to all other departments would be one of the most productive.5h. Excessive rules and regulations interfere with how well I am able to do my job.5i. Overall I think my immediate supervisor is doing a good job.5j. Compared with other work groups, my work group is under much less pressure to produce.5k. In my job the opportunities to get to know people are limited.5l. Compared to all other similar work groups in this hotel, my work group would be the most productive.5m. Your immediate supervisor is successful in dealing with higher levels of management.
256
2. Reliability AnalysisR E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Mean Std Dev Cases
1. I_1A 4.9550 1.5344 1401.0 2. I_1B 5.0457 1.5673 1401.0 3. I_1C 5.8687 1.1538 1401.0 4. I_1D 5.0435 1.4977 1401.0 5. I_1E 5.2277 1.3110 1401.0 6. I_1F 4.6160 1.6448 1401.0 7. I_1G 4.4668 1.6997 1401.0 8. I_1H 5.6931 1.3623 1401.0 9. I_1I 5.0821 1.4559 1401.0 10. I_1J 5.3419 1.4494 1401.0 11. I_1K 5.4318 1.3958 1401.0 12. I_1L 5.0842 1.5185 1401.0 13. I_1M 6.4097 .9520 1401.0 14. I_1N 5.1734 1.5700 1401.0 15. I_2A 4.7566 1.6195 1401.0 16. I_2B 4.5275 1.6817 1401.0 17. I_2C 4.6395 1.7323 1401.0 18. I_2D 5.4433 1.1987 1401.0 19. I_2E 4.9215 1.4970 1401.0 20. I_2F 5.2655 1.4221 1401.0 21. I_2G 5.0528 1.4112 1401.0 22. I_2H 4.3712 1.8457 1401.0 23. I_2I 4.8223 1.6731 1401.0 24. I_2J 5.0592 1.5848 1401.0 25. I_2K 4.8630 1.7590 1401.0 26. I_2L 3.5289 2.1599 1401.0 27. I_2M 3.9993 1.5886 1401.0 28. I_2N 2.9065 1.6364 1401.0 29. I_3A 4.5860 1.5716 1401.0 30. I_3B 5.5296 1.2573 1401.0 31. I_3C 4.7852 1.4741 1401.0 32. I_3D 5.0214 1.5918 1401.0 33. I_3E 5.0835 1.6855 1401.0 34. I_3F 5.6831 1.2539 1401.0 35. I_3G 3.1370 1.5838 1401.0 36. I_3H 4.0314 1.5813 1401.0 37. I_3I 5.1035 1.3758 1401.0 38. I_3J 5.0542 1.3196 1401.0 39. I_3K 5.5082 1.2683 1401.0 40. I_3L 5.1113 1.3796 1401.0 41. I_3M 5.6538 1.2349 1401.0 42. I_3N 5.3448 1.5925 1401.0 43. I_3O 4.9657 1.7632 1401.0 44. I_4A 3.8494 1.4917 1401.0 45. I_4B 5.5767 1.2490 1401.0 46. I_4C 5.2334 1.3827 1401.0 47. I_4D 5.7652 1.2124 1401.0 48. I_4E 5.0528 1.5311 1401.0
257
R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Mean Std Dev Cases
49. I_4F 4.5303 1.6193 1401.0 50. I_4G 4.7823 1.4872 1401.0 51. I_4H 5.0400 1.4297 1401.0 52. I_4I 5.4711 1.2175 1401.0 53. I_4J 4.3505 1.7207 1401.0 54. I_4K 6.1313 1.0757 1401.0 55. I_4L 4.4140 1.7378 1401.0 56. I_4M 4.7730 1.4471 1401.0 57. I_4N 3.9693 1.5477 1401.0 58. I_5A 4.5903 1.6451 1401.0 59. I_5B 5.2156 1.3242 1401.0 60. I_5C 5.3747 1.3470 1401.0 61. I_5D 4.6146 1.5986 1401.0 62. I_5E 5.2049 1.3475 1401.0 63. I_5F 5.1599 1.2479 1401.0 64. I_5G 5.1485 1.3802 1401.0 65. I_5H 4.6017 1.5037 1401.0 66. I_5I 5.5125 1.3654 1401.0 67. I_5J 5.0578 1.5580 1401.0 68. I_5K 4.9536 1.6579 1401.0 69. I_5L 4.7052 1.3626 1401.0 70. I_5M 5.0757 1.4172 1401.0
N ofStatistics for Mean Variance Std Dev Variables SCALE 346.3498 2883.9276 53.7022 70
258
R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Item-total Statistics
Scale Scale Corrected Mean Variance Item- Alpha if Item if Item Total if Item Deleted Deleted Correlation Deleted
I_1A 341.3947 2783.7034 .6045 .9585I_1B 341.3041 2821.8218 .3582 .9593I_1C 340.4811 2829.8612 .4296 .9591I_1D 341.3062 2776.0812 .6691 .9583I_1E 341.1221 2801.2272 .5835 .9586I_1F 341.7338 2830.5426 .2896 .9596I_1G 341.8829 2815.1163 .3655 .9593I_1H 340.6567 2792.2928 .6236 .9585I_1I 341.2677 2777.3219 .6809 .9583I_1J 341.0079 2783.8878 .6403 .9584I_1K 340.9179 2802.1683 .5401 .9587I_1L 341.2655 2790.6666 .5669 .9586I_1M 339.9400 2861.7935 .2084 .9595I_1N 341.1763 2775.3096 .6417 .9584I_2A 341.5931 2762.4029 .6984 .9582I_2B 341.8223 2773.6562 .6066 .9585I_2C 341.7102 2775.1188 .5797 .9586I_2D 340.9065 2818.9905 .4989 .9589I_2E 341.4283 2779.4079 .6480 .9584I_2F 341.0842 2777.4800 .6967 .9583I_2G 341.2969 2786.4232 .6411 .9584I_2H 341.9786 2792.9653 .4488 .9591I_2I 341.5275 2774.4109 .6055 .9585I_2J 341.2905 2796.0591 .5093 .9588I_2K 341.4868 2795.0043 .4615 .9590I_2L 342.8208 2858.9629 .0879 .9608I_2M 342.3505 2845.3307 .2129 .9598I_2N 343.4433 2824.3884 .3269 .9594I_3A 341.7637 2778.8406 .6193 .9585I_3B 340.8201 2806.6905 .5679 .9587I_3C 341.5646 2769.7889 .7216 .9582I_3D 341.3283 2797.2293 .4999 .9589I_3E 341.2662 2764.7555 .6563 .9583I_3F 340.6667 2810.1452 .5432 .9588I_3G 343.2127 2827.5433 .3199 .9594I_3H 342.3183 2809.4657 .4293 .9591I_3I 341.2463 2791.4229 .6233 .9585I_3J 341.2955 2799.4226 .5927 .9586I_3K 340.8415 2825.6949 .4199 .9591I_3L 341.2384 2801.8917 .5487 .9587I_3M 340.6959 2816.2089 .5050 .9589
259
R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Item-total Statistics
Scale Scale Corrected Mean Variance Item- Alpha if Item if Item Total if Item Deleted Deleted Correlation Deleted
I_3N 341.0050 2785.7478 .5690 .9586I_3O 341.3840 2762.4739 .6385 .9584I_4A 342.5004 2814.8487 .4224 .9591I_4B 340.7730 2797.6556 .6411 .9585I_4C 341.1163 2802.8186 .5409 .9587I_4D 340.5846 2812.8744 .5411 .9588I_4E 341.2969 2777.3875 .6456 .9584I_4F 341.8194 2761.3324 .7050 .9582I_4G 341.5675 2794.0213 .5578 .9587I_4H 341.3098 2789.4611 .6120 .9585I_4I 340.8787 2810.6581 .5561 .9587I_4J 341.9993 2816.6093 .3524 .9594I_4K 340.2184 2833.1994 .4329 .9591I_4L 341.9358 2817.0987 .3459 .9594I_4M 341.5767 2823.7600 .3776 .9592I_4N 342.3804 2812.1473 .4227 .9591I_5A 341.7595 2790.6657 .5210 .9588I_5B 341.1342 2814.4677 .4819 .9589I_5C 340.9750 2800.0172 .5759 .9587I_5D 341.7352 2823.4634 .3409 .9594I_5E 341.1449 2799.0068 .5828 .9586I_5F 341.1899 2815.9096 .5018 .9589I_5G 341.2013 2847.1652 .2366 .9596I_5H 341.7480 2818.9486 .3928 .9592I_5I 340.8373 2789.3178 .6431 .9585I_5J 341.2919 2861.5411 .1197 .9601I_5K 341.3961 2836.2437 .2545 .9597I_5L 341.6445 2862.7878 .1322 .9599I_5M 341.2741 2790.4420 .6109 .9585
Reliability Coefficients
N of Cases = 1401.0 N of Items = 70
Alpha = .9594
260
3. Principal Components Analysis
Analysis number 1 Pairwise deletion of cases with missing values
Mean Std Dev Cases Label
I_1A 4.91572 1.55387 1768I_1B 5.04262 1.57541 1783I_1C 5.85202 1.18007 1784I_1D 5.02302 1.52508 1781I_1E 5.22278 1.32199 1782I_1F 3.38440 1.67313 1782I_1G 4.45465 1.70186 1775I_1H 5.66591 1.37622 1772I_1I 5.09055 1.47296 1767I_1J 5.28708 1.49579 1780I_1K 5.40045 1.42785 1778I_1L 5.07601 1.51588 1776I_1M 6.37535 .98033 1785I_1N 5.16367 1.57916 1778I_2A 4.75518 1.62658 1785I_2B 4.52664 1.68483 1783I_2C 4.64362 1.74339 1779I_2D 5.42817 1.21527 1782I_2E 4.92853 1.50036 1777I_2F 5.21163 1.46050 1772I_2G 5.03180 1.43341 1761I_2H 3.65127 1.84598 1775I_2I 4.82479 1.67522 1775I_2J 5.04730 1.58479 1776I_2K 4.86213 1.75998 1777I_2L 4.42785 2.17874 1774I_2M 4.02201 1.59724 1772I_2N 5.07372 1.65276 1777I_3A 4.57015 1.58756 1782I_3B 5.50703 1.27479 1777I_3C 4.74719 1.49465 1780I_3D 5.00898 1.59833 1782I_3E 5.05697 1.71391 1773I_3F 5.64587 1.27908 1779I_3G 4.87040 1.59475 1767I_3H 3.96901 1.60981 1775I_3I 5.09837 1.39455 1779I_3J 5.01805 1.34664 1773I_3K 5.50929 1.27035 1777I_3L 5.07926 1.41815 1779I_3M 5.64759 1.23324 1782I_3N 5.32697 1.60342 1780I_3O 4.93659 1.77287 1782I_4A 4.16629 1.50733 1756I_4B 5.54972 1.26261 1770I_4C 5.20372 1.39481 1772I_4D 5.71896 1.23291 1772I_4E 5.04084 1.52152 1763I_4F 4.49831 1.63354 1770I_4G 4.79153 1.48291 1770I_4H 5.03333 1.44016 1770I_4I 5.44855 1.23402 1759I_4J 3.65705 1.71003 1767I_4K 6.14813 1.06739 1762I_4L 3.61736 1.74808 1751I_4M 4.75085 1.46036 1762I_4N 4.07548 1.56425 1762I_5A 3.45495 1.67406 1776I_5B 2.83851 1.37734 1771I_5C 5.36266 1.34421 1773
261
Mean Std Dev Cases LabelI_5D 4.57490 1.60181 1769I_5E 5.19255 1.35991 1771I_5F 5.16131 1.26327 1773I_5G 5.10985 1.40793 1766I_5H 3.45000 1.54846 1760I_5I 5.47225 1.41264 1766I_5J 2.97561 1.59844 1763I_5K 3.11205 1.70035 1767I_5L 4.69187 1.39375 1759I_5M 5.06444 1.43499 1769
Correlation Matrix:
I_1A I_1B I_1C I_1D I_1E I_1F I_1GI_1A 1.00000I_1B .36277 1.00000I_1C .29384 .43687 1.00000I_1D .45426 .24380 .31985 1.00000I_1E .36536 .24670 .34006 .48785 1.00000I_1F -.14695 -.03812 -.06707 -.15591 -.15114 1.00000I_1G .23433 .07491 .11461 .23760 .30702 -.05158 1.00000I_1H .42556 .19697 .22424 .57576 .36548 -.13966 .19932I_1I .47173 .26467 .25186 .50856 .40662 -.16232 .25273I_1J .43661 .23724 .27167 .51336 .38267 -.15528 .22858I_1K .26406 .13624 .29414 .35100 .41165 -.18812 .22566I_1L .45847 .26599 .24785 .38921 .34105 -.11332 .25274I_1M .14355 .12299 .14953 .09055 .07850 -.04800 .09792I_1N .47846 .25251 .28036 .46084 .36941 -.15368 .19161I_2A .41224 .20422 .30059 .68438 .45637 -.19368 .28593I_2B .34659 .17695 .21994 .42117 .40761 -.18951 .32802I_2C .31188 .14674 .22571 .43086 .38059 -.19910 .30638I_2D .29671 .18002 .21542 .33626 .37424 -.15766 .24349I_2E .37696 .18990 .27981 .43874 .46632 -.23257 .34671I_2F .46364 .21442 .28993 .59731 .41690 -.20764 .20904I_2G .38834 .17834 .23835 .50817 .41955 -.15579 .22567I_2H -.27040 -.11230 -.15968 -.35549 -.19373 .18657 -.10417I_2I .40107 .31851 .24695 .34663 .36844 -.19400 .25875I_2J .23101 .13392 .24699 .34035 .40597 -.15424 .26223I_2K .40271 .37071 .19607 .26864 .24350 -.10794 .14783I_2L -.03890 .02926 -.01293 -.01485 -.06522 .12174 -.06539I_2M -.16895 -.05100 .02911 -.12267 -.05481 .14720 -.03515I_2N -.16019 -.09018 -.07481 -.20623 -.13177 .10516 -.11878I_3A .35467 .23052 .26932 .39587 .41264 -.14824 .22950I_3B .30408 .19236 .39888 .38936 .36152 -.16250 .17990I_3C .48487 .24819 .24556 .49509 .41073 -.18905 .28775I_3D .46913 .34709 .21000 .30105 .27083 -.10212 .16997I_3E .41136 .24045 .22944 .49824 .34524 -.21492 .14675I_3F .31183 .16092 .25459 .40302 .31212 -.12424 .14496I_3G -.16271 -.09018 -.07610 -.21652 -.15560 .12472 -.09638I_3H -.30925 -.10952 -.07534 -.27853 -.19459 .20855 -.13444I_3I .34404 .15438 .28161 .49946 .36749 -.17106 .19932I_3J .34237 .20075 .26938 .37492 .34783 -.14387 .21451I_3K .27311 .47329 .43556 .24116 .29891 -.09901 .10751I_3L .33781 .23146 .25092 .42193 .32083 -.12490 .13146I_3M .24996 .13589 .26470 .33641 .29424 -.16884 .20989I_3N .39354 .31382 .24580 .35095 .31702 -.15675 .24929I_3O .45316 .41896 .32482 .38011 .40611 -.12666 .26766I_4A -.24797 -.06982 -.08958 -.28834 -.18513 .20846 -.08932I_4B .35847 .17326 .27733 .42811 .37249 -.18261 .16405I_4C .30943 .09495 .20858 .33044 .33869 -.15484 .23630I_4D .29904 .15618 .31706 .32901 .32538 -.17050 .20394I_4E .39369 .22059 .29842 .40690 .38842 -.20658 .28530I_4F .41033 .23555 .26928 .46351 .38916 -.17108 .27527 I_1A I_1B I_1C I_1D I_1E I_1F I_1GI_4G .30032 .18748 .28388 .39405 .37244 -.16517 .25886I_4H .36920 .23417 .26500 .38796 .36987 -.13826 .23772I_4I .28160 .14547 .29026 .31373 .31717 -.18415 .24774I_4J -.17195 -.02982 -.10526 -.22723 -.19658 .18180 -.17379I_4K .28766 .34238 .29810 .27986 .28516 -.15313 .11133I_4L -.19777 -.08266 -.14544 -.21479 -.17741 .20245 -.07751
262
I_4M .20803 .12726 .12639 .20469 .25719 -.12384 .22019I_4N -.24573 -.12223 -.18456 -.27857 -.18764 .20932 -.11518I_5A -.28926 -.09467 -.19631 -.32594 -.25396 .24837 -.13329I_5B -.24995 -.08757 -.20853 -.28347 -.23233 .28478 -.11580I_5C .35050 .11757 .21957 .37755 .31018 -.19960 .24105I_5D .27254 .22977 .12425 .17396 .15889 -.04788 .09670I_5E .36986 .28008 .31404 .37976 .33805 -.14015 .13851I_5F .27044 .12163 .20383 .31712 .28889 -.14841 .20740I_5G .13847 .19201 .21078 .17459 .16395 -.03843 .05213I_5H -.24972 -.05659 -.12929 -.24751 -.20642 .27360 -.09619I_5I .34738 .15026 .23551 .50805 .36768 -.19432 .19320I_5J -.07254 -.10055 -.15176 -.06645 -.03240 .13846 .04366I_5K -.18231 -.04947 -.05427 -.09537 -.08789 .14175 -.06758I_5L .06829 .14989 .15878 .10334 .11902 .02756 .01074I_5M .35954 .19701 .22457 .41667 .40335 -.15587 .19475
I_1H I_1I I_1J I_1K I_1L I_1M I_1NI_1H 1.00000I_1I .66537 1.00000I_1J .51196 .52169 1.00000I_1K .31916 .35773 .40265 1.00000I_1L .34906 .39677 .39279 .42227 1.00000I_1M .16419 .15823 .17418 .11396 .15827 1.00000I_1N .54148 .62645 .49822 .35332 .43502 .20501 1.00000I_2A .49508 .49600 .48031 .39819 .41099 .12225 .46026I_2B .35293 .41658 .37012 .39343 .36819 .12862 .41185I_2C .30310 .33472 .31057 .38085 .37296 .09157 .33814I_2D .30629 .29196 .31592 .40720 .31874 .11089 .30140I_2E .35401 .41713 .42878 .44348 .43322 .12913 .39754I_2F .68519 .62116 .56800 .39887 .38460 .17113 .57530I_2G .55939 .55461 .47816 .42472 .39592 .13837 .49940I_2H -.31931 -.30095 -.46849 -.24438 -.21269 -.03519 -.29247I_2I .31350 .42039 .33549 .29675 .34018 .13484 .43162I_2J .27374 .36331 .34041 .39547 .29969 .05035 .31885I_2K .22227 .29743 .30123 .18016 .29583 .12397 .32714I_2L -.06781 -.03385 -.01692 -.10005 -.06763 .09748 -.00930I_2M -.15368 -.12378 -.14497 -.05471 -.07950 -.00539 -.10475I_2N -.19035 -.20309 -.13472 -.20087 -.13242 .04176 -.16272I_3A .37947 .42840 .35705 .43092 .36986 .09997 .45189I_3B .40442 .45529 .37297 .31126 .27844 .18986 .45709I_3C .47194 .53894 .46428 .33350 .41848 .15706 .57073I_3D .26264 .32650 .31544 .17499 .32010 .13629 .34360I_3E .54541 .55107 .47925 .27734 .34500 .15131 .48682I_3F .39331 .36565 .55119 .27513 .32118 .16454 .37305I_3G -.17532 -.16261 -.15151 -.18462 -.15760 .06638 -.14204I_3H -.23743 -.22358 -.26255 -.19484 -.20375 -.08646 -.22681I_3I .46026 .46556 .45635 .33966 .39180 .11128 .42460I_3J .36531 .41320 .38279 .31521 .33541 .14339 .38038I_3K .17352 .24854 .21826 .21977 .29112 .15619 .27313I_3L .32390 .34864 .54338 .28086 .35345 .14062 .36861I_3M .28304 .27249 .30094 .25008 .27953 .16072 .27344I_3N .31048 .38244 .33873 .27027 .32357 .20153 .36349I_3O .31516 .41231 .36830 .34845 .39776 .11153 .41710I_4A -.26008 -.24198 -.26484 -.17124 -.18156 -.03146 -.21290I_4B .48610 .50861 .42924 .32133 .32144 .19723 .55368I_4C .27259 .32436 .29146 .28717 .29724 .18155 .31109I_4D .28949 .32099 .31925 .28827 .30372 .18937 .31652I_4E .31519 .36780 .36887 .35534 .37308 .13202 .39272I_4F .42168 .48703 .40308 .37740 .39744 .13246 .45432I_4G .27595 .33972 .31201 .33060 .34180 .08410 .32709I_4H .36608 .45979 .35691 .31306 .35544 .12725 .38122I_4I .24906 .31402 .30688 .33538 .27900 .15441 .30353I_4J -.16018 -.16557 -.21359 -.18944 -.13842 -.00295 -.13484I_4K .23535 .24864 .25593 .27217 .27066 .16044 .25176I_4L -.21953 -.21327 -.28059 -.17243 -.16331 -.04408 -.21821I_4M .16136 .19232 .16384 .22418 .25561 .06051 .19303I_4N -.24037 -.23808 -.28046 -.18605 -.19627 -.09272 -.23694I_5A -.28072 -.27342 -.28630 -.26058 -.23176 -.07559 -.26832I_5B -.26933 -.24913 -.26096 -.22784 -.19848 -.10249 -.22775I_5C .34074 .34560 .37615 .30685 .31307 .15475 .36302I_5D .21172 .24614 .25652 .11855 .19305 .12568 .26388
263
I_5E .33630 .39297 .47517 .28790 .34097 .11171 .37700I_5F .23563 .27406 .31550 .25487 .25048 .13653 .25508I_5G .14624 .15128 .18604 .16133 .15391 .01429 .15930I_5H -.25382 -.19703 -.21996 -.18830 -.15945 -.04854 -.18108I_5I .63650 .58882 .46915 .34047 .29920 .14106 .51505I_5J -.07781 -.08522 -.07093 -.04983 -.03862 -.04274 -.06024I_5K -.11731 -.13950 -.14005 -.10462 -.10377 -.25932 -.15763I_5L .09408 .12143 .13607 .12476 .09828 -.01710 .11862I_5M .43677 .54095 .38035 .34219 .35039 .11202 .45306
I_2A I_2B I_2C I_2D I_2E I_2F I_2GI_2A 1.00000I_2B .51781 1.00000I_2C .49000 .52580 1.00000I_2D .37578 .38909 .37340 1.00000I_2E .49681 .46848 .48633 .47721 1.00000I_2F .56180 .43236 .37260 .38273 .46758 1.00000I_2G .50237 .44047 .40203 .42320 .49488 .68371 1.00000I_2H -.31158 -.21261 -.20514 -.21188 -.23536 -.35216 -.28746I_2I .40342 .39810 .35003 .29135 .38055 .37270 .34121I_2J .38095 .38818 .35255 .30972 .45973 .37884 .39721I_2K .28675 .27533 .23822 .21948 .29193 .30205 .28727I_2L -.01884 .00726 -.02679 -.07012 -.08267 -.03278 -.06194I_2M -.09469 -.08369 -.03750 -.04866 -.04387 -.10612 -.09425I_2N -.25251 -.18651 -.22732 -.17648 -.15621 -.21094 -.18308I_3A .48830 .46908 .39347 .34105 .43939 .46660 .46435I_3B .41853 .35226 .32389 .31052 .40547 .48942 .45038I_3C .54603 .46515 .43347 .33948 .49849 .58127 .51946I_3D .33034 .29430 .24955 .20980 .27448 .33921 .28311I_3E .46624 .36435 .32256 .28872 .38416 .60994 .52484I_3F .40265 .28596 .27436 .28295 .30273 .42980 .35742I_3G -.26460 -.18833 -.26325 -.15307 -.16576 -.19181 -.18512I_3H -.29950 -.22270 -.18231 -.22073 -.22860 -.25991 -.24330I_3I .49004 .37535 .39323 .27203 .43647 .52790 .48615I_3J .42516 .41058 .34121 .29822 .37804 .44182 .41969I_3K .26107 .21984 .22007 .23552 .25883 .24385 .21515I_3L .42561 .29772 .28733 .26920 .29612 .39499 .33871I_3M .37748 .28390 .31576 .23743 .32717 .30728 .27516I_3N .39910 .31207 .34368 .24372 .34897 .33356 .31006I_3O .42298 .34890 .36292 .33603 .41797 .39903 .36682I_4A -.28252 -.22846 -.17692 -.16117 -.24318 -.28238 -.23664I_4B .43314 .38894 .36037 .30735 .40974 .53709 .49448I_4C .40008 .38335 .39252 .28264 .40241 .34057 .32766I_4D .39287 .31690 .35310 .27132 .36510 .33171 .31099I_4E .46489 .41821 .45725 .33946 .48811 .39089 .37879I_4F .55802 .48255 .46077 .33998 .49388 .49371 .46373I_4G .44467 .39837 .52165 .28812 .45830 .35287 .35083I_4H .50663 .40301 .34087 .30823 .40532 .44563 .39634I_4I .40266 .37543 .39984 .28726 .39006 .31418 .30222I_4J -.22558 -.23191 -.29469 -.15250 -.26740 -.18153 -.15892I_4K .27356 .20999 .23960 .25291 .26670 .27115 .24277I_4L -.20158 -.17056 -.10136 -.17950 -.21735 -.26853 -.21728I_4M .27147 .25503 .33385 .18174 .24668 .20249 .23966I_4N -.28067 -.24909 -.24968 -.25961 -.26218 -.29402 -.22374I_5A -.37556 -.37736 -.34793 -.25968 -.34262 -.33726 -.25979I_5B -.29937 -.24148 -.23918 -.24186 -.33304 -.31407 -.23327I_5C .42764 .30456 .34592 .26632 .37056 .36506 .31304I_5D .21698 .20429 .09957 .18735 .14920 .26653 .22520I_5E .40071 .29917 .24960 .28345 .31695 .41879 .35085I_5F .36523 .27857 .37351 .21035 .33560 .29744 .26583I_5G .14154 .08491 .10705 .12180 .16850 .15495 .14192I_5H -.24346 -.21482 -.17182 -.19605 -.24522 -.26489 -.20358I_5I .46289 .34370 .31546 .28724 .37409 .64330 .54225I_5J -.03370 -.00911 .02249 -.01312 -.02779 -.08020 .04162I_5K -.10970 -.12583 -.08546 -.06624 -.10437 -.13862 -.13448I_5L .09491 .05128 .10206 .09062 .09811 .09977 .11425I_5M .45032 .37571 .33273 .29908 .39966 .49985 .45171
I_2H I_2I I_2J I_2K I_2L I_2M I_2NI_2H 1.00000I_2I -.14741 1.00000
264
I_2J -.19595 .36878 1.00000I_2K -.11325 .48874 .22678 1.00000I_2L .11250 -.02967 -.07046 .03812 1.00000I_2M .16553 -.13288 -.00599 -.08322 .10977 1.00000I_2N .19131 -.15084 -.16924 -.10416 .09733 .17859 1.00000I_3A -.23326 .39298 .41777 .30067 -.04388 -.01687 -.20593I_3B -.23799 .31320 .35366 .21933 -.01787 .01430 -.10870I_3C -.29547 .48874 .39131 .34154 -.04872 -.11820 -.20002I_3D -.13937 .48627 .18775 .71500 .03990 -.09248 -.11769I_3E -.34824 .33186 .31152 .30052 -.02384 -.10317 -.19161I_3F -.37941 .27077 .25349 .21749 .00004 -.10475 -.12624I_3G .24166 -.15393 -.20737 -.11705 .07659 .15084 .43662I_3H .31205 -.18551 -.13633 -.16863 .13096 .30542 .26652I_3I -.31225 .35639 .36482 .25077 -.04883 -.05691 -.14826I_3J -.23365 .34506 .37670 .30284 -.04297 -.03473 -.14246I_3K -.13697 .28834 .18623 .31841 .02224 -.02335 -.04498I_3L -.44152 .28792 .26389 .25362 -.03021 -.08972 -.15622I_3M -.19617 .28452 .23274 .17398 -.00979 -.07913 -.13381I_3N -.16434 .51931 .27484 .39186 -.01226 -.09850 -.10102I_3O -.21221 .53604 .31763 .46903 -.03992 -.10086 -.22016I_4A .29942 -.20139 -.19950 -.17600 .06642 .21586 .20089I_4B -.27344 .36202 .34238 .27397 .00286 -.09904 -.14996I_4C -.16652 .34181 .29355 .22305 .03274 -.08213 -.11976I_4D -.19224 .30926 .31512 .21427 .00475 -.07636 -.12616I_4E -.22324 .49440 .38847 .33141 -.03246 -.09941 -.20774I_4F -.26116 .49225 .38529 .33757 -.05735 -.09730 -.24520I_4G -.22888 .31211 .34785 .22025 -.06851 -.02062 -.17671I_4H -.26150 .36718 .33039 .27813 .01507 -.11207 -.18237I_4I -.18787 .34831 .35255 .23225 -.03352 -.07641 -.12126I_4J .28293 -.12226 -.15698 -.09237 .12841 .17958 .17355I_4K -.15343 .30592 .18523 .30217 -.04095 -.05794 -.13490I_4L .29732 -.12249 -.18834 -.11015 .11139 .11349 .13290I_4M -.12441 .24843 .22923 .16525 -.04139 -.07128 -.12449I_4N .29294 -.19104 -.20758 -.14380 .02766 .16342 .22694I_5A .27944 -.24260 -.27608 -.13985 .02913 .20432 .26193I_5B .28847 -.21294 -.25144 -.13669 .07445 .19115 .15174I_5C -.24825 .33976 .30556 .24218 -.05260 -.15373 -.17339I_5D -.18078 .22154 .10689 .27884 -.01001 -.07316 -.06870I_5E -.34391 .28483 .27014 .30804 -.06890 -.06531 -.16005I_5F -.19361 .25491 .24195 .17549 -.05094 -.05463 -.15491I_5G -.02400 .07992 .14644 .12630 .01870 -.02150 -.01958I_5H .26615 -.14728 -.17843 -.08399 .10492 .23616 .14953I_5I -.33618 .30522 .31546 .23597 -.04270 -.08862 -.17373I_5J .08542 -.04684 -.03701 .00960 .00466 .06366 .01106I_5K .08295 -.20400 -.08943 -.19678 .00641 .14622 .05297I_5L .00864 .02494 .08338 .07209 .02302 .04445 -.02634I_5M -.25547 .36199 .30675 .26252 -.02070 -.08852 -.16680
I_3A I_3B I_3C I_3D I_3E I_3F I_3GI_3A 1.00000I_3B .44296 1.00000I_3C .50788 .47019 1.00000I_3D .32748 .27206 .40424 1.00000I_3E .40406 .48493 .50431 .34374 1.00000I_3F .32537 .32405 .39510 .27283 .40415 1.00000I_3G -.22479 -.13043 -.16427 -.09097 -.17235 -.11877 1.00000I_3H -.18773 -.13408 -.26523 -.18080 -.24214 -.23200 .32679I_3I .42657 .40565 .47534 .27663 .50738 .42669 -.15651I_3J .44065 .41107 .48425 .32710 .40732 .36196 -.11003I_3K .28008 .29958 .25407 .31042 .26666 .21447 -.03699I_3L .32358 .33113 .38229 .30817 .38457 .56180 -.13534I_3M .29971 .31886 .37612 .19448 .27562 .31270 -.10415I_3N .29916 .28969 .42877 .38427 .33687 .30045 -.11943I_3O .42774 .33384 .46649 .47877 .37611 .33302 -.20670I_4A -.18286 -.17755 -.26075 -.17642 -.26678 -.18840 .24883I_4B .41497 .50911 .49472 .31022 .49757 .40815 -.13218I_4C .35553 .34641 .38756 .25423 .30488 .30461 -.12847I_4D .33370 .39184 .38602 .24694 .31771 .33129 -.11015I_4E .43398 .39635 .47672 .34260 .35632 .31289 -.21103I_4F .49438 .38752 .57197 .37289 .44138 .33918 -.24078I_4G .37784 .38524 .40977 .25027 .33963 .30860 -.20891
265
I_4H .42646 .36783 .48741 .33886 .42523 .28206 -.21282I_4I .36686 .36384 .39600 .27437 .29557 .30069 -.16382I_4J -.18638 -.14251 -.19710 -.07817 -.16909 -.21428 .20429I_4K .25776 .23234 .26582 .31409 .25016 .24640 -.10678I_4L -.18185 -.20022 -.21701 -.12366 -.26308 -.23090 .13168I_4M .24069 .18209 .25356 .17726 .17556 .12149 -.18010I_4N -.24203 -.22556 -.28440 -.15864 -.26923 -.23789 .23801I_5A -.31649 -.26659 -.33932 -.15518 -.31895 -.29412 .28247I_5B -.22818 -.24031 -.29526 -.14085 -.31740 -.25134 .17584I_5C .32054 .29669 .40729 .25240 .32451 .32083 -.19242I_5D .19325 .16158 .26771 .30147 .27642 .24428 -.03356I_5E .32196 .34297 .36545 .32928 .41158 .43396 -.14285I_5F .29453 .24760 .35341 .20762 .28508 .32128 -.15770I_5G .14226 .17096 .14007 .12051 .14636 .18037 -.02708I_5H -.17987 -.15664 -.24173 -.07934 -.23363 -.21727 .19122I_5I .37643 .45677 .48996 .26283 .61233 .38396 -.16324I_5J -.02369 -.09794 -.02715 -.02201 -.10498 -.09078 .02044I_5K -.10893 -.10631 -.16206 -.19598 -.11996 -.15110 .04121I_5L .10781 .11059 .08035 .08867 .09907 .13729 -.00540I_5M .41161 .40805 .46921 .30626 .48418 .29144 -.17671
I_3H I_3I I_3J I_3K I_3L I_3M I_3NI_3H 1.00000I_3I -.19962 1.00000I_3J -.14981 .45778 1.00000I_3K -.09339 .23070 .32070 1.00000I_3L -.25642 .38041 .38249 .32621 1.00000I_3M -.17341 .30563 .29489 .20516 .31620 1.00000I_3N -.18293 .27974 .31872 .29575 .31275 .42630 1.00000I_3O -.23215 .37504 .38700 .37520 .34999 .32036 .56332I_4A .34633 -.26610 -.18878 -.10808 -.19121 -.15290 -.15564I_4B -.20153 .48800 .39581 .22933 .36217 .31856 .35113I_4C -.21137 .31518 .35867 .19108 .23661 .38215 .37461I_4D -.17554 .32067 .33006 .30433 .27999 .48798 .37190I_4E -.25081 .39428 .36578 .23813 .29289 .41099 .47993I_4F -.24452 .49935 .45262 .25751 .34092 .36813 .42557I_4G -.18603 .36940 .33390 .26555 .29983 .33843 .32260I_4H -.22366 .40667 .38617 .27569 .30206 .35055 .32846I_4I -.18989 .35024 .37188 .25587 .27970 .40540 .34402I_4J .25523 -.17927 -.19476 -.07269 -.20854 -.18656 -.16676I_4K -.14642 .23107 .23831 .39299 .25799 .24881 .35371I_4L .27665 -.22416 -.19344 -.10388 -.24434 -.12806 -.06467I_4M -.16179 .22519 .19139 .17797 .15392 .27166 .28632I_4N .32557 -.23896 -.21713 -.11738 -.25298 -.24844 -.16866I_5A .36454 -.29782 -.25584 -.11890 -.22784 -.29691 -.27562I_5B .30927 -.28574 -.23438 -.13519 -.20466 -.28905 -.23558I_5C -.25525 .34467 .29919 .19075 .32452 .44009 .47652I_5D -.12093 .18490 .24551 .22914 .29400 .14317 .22168I_5E -.26435 .37510 .36905 .32035 .49267 .31498 .29239I_5F -.19066 .30540 .28650 .17359 .30139 .31368 .30400I_5G -.03648 .18680 .16517 .21743 .18389 .11031 .07662I_5H .32381 -.21766 -.18848 -.10794 -.17489 -.23977 -.14021I_5I -.23528 .49330 .38374 .18695 .36416 .27509 .29421I_5J .07842 -.05520 -.04400 -.14922 -.07386 -.03976 -.02599I_5K .16835 -.08750 -.14356 -.05362 -.12307 -.10308 -.21714I_5L .01839 .13274 .11443 .16779 .14445 .03109 .01978I_5M -.22795 .42458 .37474 .24222 .30600 .28024 .32265
I_3O I_4A I_4B I_4C I_4D I_4E I_4FI_3O 1.00000I_4A -.19520 1.00000I_4B .39448 -.20063 1.00000I_4C .33151 -.22249 .40933 1.00000I_4D .38549 -.17088 .38704 .48664 1.00000I_4E .50422 -.22539 .42288 .52718 .56760 1.00000I_4F .47271 -.26588 .45747 .46542 .44762 .59387 1.00000I_4G .36277 -.18481 .39780 .38332 .42063 .48672 .50908I_4H .40742 -.22561 .41192 .37233 .38975 .44581 .53125I_4I .36532 -.20750 .38501 .51287 .52199 .51243 .47766I_4J -.19055 .25025 -.13505 -.18924 -.14883 -.21915 -.21058
266
I_4K .35935 -.13601 .28258 .19261 .25386 .28736 .27996I_4L -.13550 .39222 -.18793 -.12012 -.16022 -.15349 -.19445I_4M .25467 -.11489 .20411 .27901 .28804 .40706 .33135I_4N -.20510 .33320 -.22944 -.22317 -.23474 -.25987 -.27702I_5A -.31675 .32450 -.29766 -.35468 -.33918 -.38346 -.37524I_5B -.24942 .31808 -.26098 -.27107 -.33403 -.31322 -.29360I_5C .35720 -.24954 .34341 .37263 .43034 .49318 .43494I_5D .24806 -.14590 .20209 .16337 .14648 .16034 .20269I_5E .37481 -.21946 .34554 .26540 .31024 .32253 .33389I_5F .31162 -.15677 .29349 .35966 .33224 .37365 .37806I_5G .15633 -.05985 .16201 .08564 .15405 .09952 .14123I_5H -.16867 .35418 -.20656 -.22423 -.23210 -.22239 -.23835I_5I .32655 -.22930 .54818 .28442 .31331 .34750 .43504I_5J -.01724 .11675 -.05753 -.06052 -.07949 -.00589 -.00173I_5K -.15863 .13617 -.13343 -.13692 -.14579 -.14693 -.11844I_5L .10024 .00707 .08761 .02443 .06975 .04407 .07561I_5M .36081 -.23859 .43838 .33589 .31924 .39860 .46148
I_4G I_4H I_4I I_4J I_4K I_4L I_4MI_4G 1.00000I_4H .42495 1.00000I_4I .43346 .43706 1.00000I_4J -.21741 -.19094 -.17080 1.00000I_4K .26575 .24305 .24613 -.12538 1.00000I_4L -.14997 -.17688 -.17505 .23665 -.05244 1.00000I_4M .29282 .29280 .31881 -.11894 .21611 .00761 1.00000I_4N -.24984 -.22166 -.23344 .32541 -.10549 .38440 -.07120I_5A -.31335 -.29697 -.31082 .32523 -.15250 .33115 -.16973I_5B -.24672 -.26218 -.28933 .35345 -.16581 .38867 -.13741I_5C .32712 .35370 .39709 -.21143 .25691 -.21200 .32778I_5D .12111 .21615 .12786 -.08380 .18682 -.17287 .10573I_5E .32570 .35651 .26709 -.17940 .33206 -.25882 .18055I_5F .32736 .34328 .33752 -.45117 .20874 -.17322 .20144I_5G .13130 .15809 .11051 .02571 .24539 -.02166 .11451I_5H -.16493 -.22496 -.24305 .26594 -.11962 .38075 -.12928I_5I .32353 .43645 .30047 -.14094 .25491 -.22238 .16462I_5J -.05174 -.04898 -.07848 .03899 -.13970 .19437 .01205I_5K -.06139 -.10357 -.09490 .15626 -.11399 .12734 -.08317I_5L .08975 .07374 .05076 .07163 .19551 .05130 .08941I_5M .33189 .64388 .34419 -.16545 .22866 -.20954 .25041
I_4N I_5A I_5B I_5C I_5D I_5E I_5FI_4N 1.00000I_5A .42877 1.00000I_5B .40739 .53734 1.00000I_5C -.24627 -.33653 -.32756 1.00000I_5D -.14093 -.15324 -.09981 .23499 1.00000I_5E -.23159 -.27843 -.24840 .31303 .38408 1.00000I_5F -.19747 -.27364 -.25806 .38653 .21011 .35320 1.00000I_5G -.04708 .00376 -.05153 .12621 .13649 .27101 .10615I_5H .35305 .39717 .49997 -.27971 -.07782 -.18378 -.17809I_5I -.24183 -.26063 -.29618 .34607 .20997 .36908 .27325I_5J .18130 .15952 .22211 -.03879 .01163 -.11275 .00258I_5K .17619 .20972 .24350 -.15304 -.14269 -.08949 -.13989I_5L .01136 .03591 .06042 .04787 .07561 .18607 .02511I_5M -.19526 -.25513 -.25703 .33521 .22977 .32856 .32398
I_5G I_5H I_5I I_5J I_5K I_5L I_5MI_5G 1.00000I_5H .02648 1.00000I_5I .20412 -.23303 1.00000I_5J -.14194 .22237 -.08653 1.00000I_5K .00318 .19113 -.11632 .14192 1.00000I_5L .64248 .07544 .13499 -.11540 .10187 1.00000I_5M .20314 -.22987 .60255 -.05890 -.13410 .16243 1.00000
Extraction 1 for analysis 1, Principal Components Analysis (PC)
267
- - - - - - - - - - - F A C T O R A N A L Y S I S - - - - - - - - - - -
268
Initial Statistics:
Variable Communality * Factor Eigenvalue Pct of Var Cum Pct *I_1A 1.00000 * 1 20.28428 29.0 29.0I_1B 1.00000 * 2 3.16968 4.5 33.5I_1C 1.00000 * 3 2.53426 3.6 37.1I_1D 1.00000 * 4 2.27544 3.3 40.4I_1E 1.00000 * 5 1.90237 2.7 43.1I_1F 1.00000 * 6 1.73948 2.5 45.6I_1G 1.00000 * 7 1.45607 2.1 47.7I_1H 1.00000 * 8 1.38525 2.0 49.6I_1I 1.00000 * 9 1.22485 1.7 51.4I_1J 1.00000 * 10 1.16759 1.7 53.1I_1K 1.00000 * 11 1.10980 1.6 54.6I_1L 1.00000 * 12 1.02730 1.5 56.1I_1M 1.00000 * 13 1.01748 1.5 57.6
I_1N 1.00000 * 14 .98009 1.4 59.0I_2A 1.00000 * 15 .90769 1.3 60.3I_2B 1.00000 * 16 .88659 1.3 61.5I_2C 1.00000 * 17 .85652 1.2 62.7I_2D 1.00000 * 18 .82640 1.2 63.9I_2E 1.00000 * 19 .80714 1.2 65.1I_2F 1.00000 * 20 .79285 1.1 66.2I_2G 1.00000 * 21 .77782 1.1 67.3I_2H 1.00000 * 22 .75530 1.1 68.4I_2I 1.00000 * 23 .73690 1.1 69.5I_2J 1.00000 * 24 .72842 1.0 70.5I_2K 1.00000 * 25 .70838 1.0 71.5I_2L 1.00000 * 26 .68002 1.0 72.5I_2M 1.00000 * 27 .66246 .9 73.4I_2N 1.00000 * 28 .65611 .9 74.4I_3A 1.00000 * 29 .62698 .9 75.3I_3B 1.00000 * 30 .61318 .9 76.1I_3C 1.00000 * 31 .60861 .9 77.0I_3D 1.00000 * 32 .60485 .9 77.9I_3E 1.00000 * 33 .58232 .8 78.7I_3F 1.00000 * 34 .58119 .8 79.5I_3G 1.00000 * 35 .57369 .8 80.4I_3H 1.00000 * 36 .56269 .8 81.2I_3I 1.00000 * 37 .54876 .8 81.9I_3J 1.00000 * 38 .53654 .8 82.7I_3K 1.00000 * 39 .53226 .8 83.5I_3L 1.00000 * 40 .50707 .7 84.2I_3M 1.00000 * 41 .50414 .7 84.9I_3N 1.00000 * 42 .48984 .7 85.6I_3O 1.00000 * 43 .47811 .7 86.3I_4A 1.00000 * 44 .46842 .7 87.0I_4B 1.00000 * 45 .46368 .7 87.6I_4C 1.00000 * 46 .45928 .7 88.3I_4D 1.00000 * 47 .45121 .6 88.9I_4E 1.00000 * 48 .43999 .6 89.6I_4F 1.00000 * 49 .43111 .6 90.2I_4G 1.00000 * 50 .42662 .6 90.8I_4H 1.00000 * 51 .41395 .6 91.4I_4I 1.00000 * 52 .39888 .6 91.9I_4J 1.00000 * 53 .39134 .6 92.5I_4K 1.00000 * 54 .38656 .6 93.1I_4L 1.00000 * 55 .37164 .5 93.6I_4M 1.00000 * 56 .36512 .5 94.1I_4N 1.00000 * 57 .35079 .5 94.6I_5A 1.00000 * 58 .34896 .5 95.1I_5B 1.00000 * 59 .34087 .5 95.6Variable Communality * Factor Eigenvalue Pct of Var Cum Pct *I_5C 1.00000 * 60 .32509 .5 96.1I_5D 1.00000 * 61 .32433 .5 96.5I_5E 1.00000 * 62 .31249 .4 97.0I_5F 1.00000 * 63 .30561 .4 97.4I_5G 1.00000 * 64 .30072 .4 97.8
269
I_5H 1.00000 * 65 .28702 .4 98.2I_5I 1.00000 * 66 .27311 .4 98.6I_5J 1.00000 * 67 .25843 .4 99.0I_5K 1.00000 * 68 .24295 .3 99.3I_5L 1.00000 * 69 .23557 .3 99.7I_5M 1.00000 * 70 .21953 .3 100.0
- - - - - - - - - - - F A C T O R A N A L Y S I S - - - - - - - - - - -
PC extracted 13 factors.
VARIMAX rotation 1 for extraction 1 in analysis 1 - KaiserNormalization.
VARIMAX converged in 10 iterations.
270
Rotated Factor Matrix:
Factor 1 Factor 2 Factor 3 Factor 4 Factor5
I_5I .78513I_1H .76147I_2F .74750I_1I .74048I_3E .68318I_5M .64066I_2G .64029 .38232I_1N .63083I_4B .60019 .32269I_1D .54827 .32482I_3C .54264 .30491I_3I .53171I_3B .52722I_4H .48633 .38638I_2A .48012 .31882 .37099I_3A .40572 .36489I_3J .38169
I_4D .69007I_4E .65698I_4I .65156I_3M .61499I_4C .61013I_5C .54845I_4M .50018I_3N .48021 .34605I_4F .42562 .48015I_4G .46372 .30484
I_2E .31405 .59566I_2D .56499I_1K .55832I_1G .54268I_2B .31935 .53658I_2C .36634 .51323I_1E .33491 .49122I_2J .46440I_1L .44849
I_5H .66098I_4L .66025I_5B .65429I_4N .58890I_4A .55544I_5A .54966I_5J .46421I_3H .38905
I_2K .76994I_3D .75628I_2I .32013 .54086I_3O .32755 .45948I_5D .44143I_1A .37311 .37497
I_3LI_3F .30155I_2HI_1J .45882I_5E Factor 1 Factor 2 Factor 3 Factor 4 Factor5
I_1B .34856I_1CI_3K
271
I_4K
I_3GI_2N
I_5GI_5L
I_4J .31232I_5F .33482
I_1MI_5K
I_2M
I_2L
I_1F .34721
272
Factor 6 Factor 7 Factor 8 Factor 9 Factor10
I_5II_1HI_2FI_1II_3EI_5MI_2GI_1NI_4BI_1DI_3CI_3II_3BI_4HI_2AI_3AI_3J
I_4DI_4EI_4II_3MI_4CI_5CI_4MI_3NI_4FI_4G
I_2EI_2DI_1KI_1GI_2BI_2CI_1EI_2JI_1L
I_5HI_4L
I_5BI_4NI_4AI_5AI_5JI_3H .34251I_2KI_3DI_2II_3O .32306I_5D .31648I_1A
I_3L .69406I_3F .63847I_2H -.54682I_1J .54244I_5E .49822
Factor 6 Factor 7 Factor 8 Factor 9 Factor10
I_1B .70626I_1C .69224I_3K .65464
273
I_4K .46327
I_3G .75557I_2N .74610
I_5G .85196I_5L .84870
I_4J -.67927I_5F .67340
I_1MI_5K
I_2M
I_2LI_1F
274
Factor 11 Factor 12 Factor 13
I_5II_1HI_2FI_1II_3EI_5MI_2GI_1NI_4BI_1DI_3CI_3II_3B .31582I_4HI_2AI_3AI_3J
I_4DI_4EI_4II_3MI_4CI_5CI_4MI_3NI_4FI_4G
I_2EI_2DI_1KI_1GI_2BI_2CI_1EI_2JI_1L
I_5HI_4LI_5BI_4NI_4AI_5AI_5JI_3H .34559I_2KI_3DI_2II_3OI_5DI_1A -.30983
I_3LI_3FI_2HI_1JI_5E
I_1BI_1C Factor 11 Factor 12 Factor 13
I_3KI_4K
I_3GI_2N
275
I_5GI_5L
I_4JI_5F
I_1M .68779I_5K -.67369
I_2M .53476
I_2L .77626I_1F .44449
276
Factor Transformation Matrix:
Factor 1 Factor 2 Factor 3 Factor 4 Factor5
Factor 1 .60134 .43884 .38733 -.26018.27352Factor 2 .12255 .02222 .06034 .73231.30310Factor 3 -.53483 .65106 .27332 .10909.09796Factor 4 .38622 .07990 .32272 .30506 -.51511Factor 5 -.21974 .15054 .06232 -.18677 -.52261Factor 6 .13955 .33848 -.34766 -.13860 -.19583Factor 7 -.19429 .13769 -.07790 .33972 -.02891Factor 8 -.17696 -.38416 .53507 -.09210 -.01163Factor 9 -.07835 -.10380 .38318 .06014 -.03440Factor 10 -.09604 -.10554 .06905 -.24997.45203Factor 11 -.08251 -.11893 .23775 .02387 -.16258Factor 12 -.16120 .18406 .16941 -.16124.08321Factor 13 .05646 -.05431 .11638 -.14289 -.08411
Factor 6 Factor 7 Factor 8 Factor 9 Factor10
Factor 1 .25227 .20571 -.13910 .06648.12773Factor 2 -.05606 .33069 .28276 .28183 -.16610Factor 3 -.35255 .00690 -.09089 -.19387.14023Factor 4 -.17425 -.48044 -.02040 -.10691 -.01451Factor 5 .16477 .32975 .04178 .61336.04268Factor 6 .04770 -.05194 .59987 -.19831 -.04271Factor 7 .74795 -.23381 .00220 -.06436.41142Factor 8 .21128 .26905 .26236 -.44593.06297Factor 9 -.04275 -.26000 .33720 .32053.01394Factor 10 -.05833 -.50426 .14198 .36600.21020Factor 11 .04053 -.00675 -.35744 .10741 -.12324Factor 12 .28522 -.17663 .19572 -.01832 -.74719Factor 13 -.23842 .16081 .40208 .00536.37614
Factor 11 Factor 12 Factor 13
Factor 1 .07185 -.02196 -.03992Factor 2 -.01843 .16912 .15586Factor 3 .02983 .05440 -.01326Factor 4 -.23830 .22626 .02626
277
Factor 5 -.14529 .27393 -.07319Factor 6 .45010 .21654 .17909Factor 7 -.07518 -.14309 .08568Factor 8 .03526 .36598 -.04824Factor 9 .43916 -.42741 -.41213Factor 10 -.07957 .44274 .22298Factor 11 .50526 -.07917 .69122Factor 12 -.33252 -.18696 .15333Factor 13 -.37572 -.46957 .45308
278
Factor Loadings
Factor 1 Leader facilitation and support loadingItem# Item5i. Overall I think my immediate supervisor is doing a good job. .791h. Your supervisor is friendly and easy to approach. .762f. Your supervisor is attentive to what you say. .751i. Your supervisor offers new ideas for job and related problems. .743e. Your supervisor sets an example by working hard himself or herself. .685m. Your immediate supervisor is successful in dealing with higher levels of management .642g. Your supervisor provides the help you need to schedule your work ahead of time. .641n. Your supervisor encourages the people who work for him or her to exchange ideas and
opinions. .634b. Your supervisor encourages the people who work for them to work as a team. .601d. Staff members generally trust their supervisors. .553c. The ideas and suggestions of staff members are paid attention to. .543i. Supervisors generally know what is going on in their work groups. .533b. Your superior emphasises high standards of performance. .534h. Your manager is successful in his dealing with higher levels of management. .492a. Staff members generally trust their managers. .483a. You have good information on where you stand and how your performance is evaluated. .413j. You are aware of how well your work group is meeting its objectives. .38
Factor 2 Professional and organisational esprit4d. The hotel strives to do a better job than other hotels of the same type. .694e. The hotel emphasises personal growth and development. .664i. The objectives of the hotel are clearly defined. .653m. The hotel has a good image to outsiders. .624c. It is possible to get accurate information on the policies and objectives of this hotel. .615c. Under most circumstances I would recommend this hotel to a prospective staff member. .554m. This hotel is concerned with assisting the local community. .503n. Working in this hotel is beneficial to your career. .484f. Managers keep well informed about the needs and problems of employees. .484g. Discipline in this hotel is maintained consistently. .46
Factor 3 Conflict and ambiguity2e. Procedures are designed so that resources are used efficiently. .602d. You have opportunities to complete the work you start. .571k. Your job responsibilities are clearly defined. .561g. You are able to get the money, supplies, equipment, etc. your work group needs
to do its work well. .542b. You are given advanced information about changes which might affect you. .542c. The hotel�s policies are consistently applied to all staff members. .511e. The methods of your work are kept up to date. .492j. New staff members get on-the-job training they need. .461l. Responsibility is assigned so that individuals have authority within their own area. .45
279
Factor 4 Regulations, organisation and pressure5h. Excessive rules and regulations interfere with how well I am able to do my job. .664l. The way your work group is organised hinders the efficient conduct of work. .665b. In this hotel things are planned so that everyone is getting in each others� way. .654n. Things in this hotel seem to happen contrary to rules and regulations. .594a. Communication is hindered by following chain of command rules. .565a. In this hotel the only source of information on important matters is the grapevine. .555j. Compared with other work groups, my work group is under much less
pressure to produce. .463h. People act as though everyone must be watched or they will slacken off. .39
Factor 5 Job variety, challenge and autonomy2k. There is variety in your job. .773d. You have the opportunity to do a number of different things in your job. .762i. You have opportunities to learn worthwhile skills and knowledge in your job. .543o. You have opportunities to make full use of your knowledge and skills in your job. .465d. Most of the personnel in my department would not want to change to another
department. .441a. Opportunity for independent thought and action exists in your job. .37
Factor 6 Workgroup co-operation, friendliness and warmth3l. Members of your work group trust each other. .693f. A friendly atmosphere prevails among most of the members of your workgroup. .642h. There is friction in your workgroup. -.551j. A spirit of cooperation exists in your workgroup. .545e. Most members of my work group take pride in their jobs. .50
Factor 7 Job standards1b. Your job requires a high level of skill and training. .711c. You are required to meet rigid standards of quality in your work. .693k. Your job demands precision. .664k. Your work is important. .46
280
4. Oneway ANOVAs of new Organisational Climate Dimensions and Composite Measure of OrganisationalClimate
- - - - - O N E W A Y - - - - -
Variable F1 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 133.4169 11.1181 11.0268 .0000Within Groups 1566 1578.9603 1.0083Total 1578 1712.3772
Variable F2 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 130.8890 10.9074 12.7682 .0000Within Groups 1612 1377.0711 .8543Total 1624 1507.9600
Variable F3 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 75.6003 6.3000 6.3853 .0000Within Groups 1626 1604.2743 .9866Total 1638 1679.8745
Variable F4 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 133.4651 11.1221 11.8983 .0000Within Groups 1596 1491.8868 .9348Total 1608 1625.3519
281
- - - - - O N E W A Y - - - - -
Variable F5 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 111.4105 9.2842 6.9393 .0000Within Groups 1632 2183.4695 1.3379Total 1644 2294.8800
Variable F6 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 67.5928 5.6327 10.5933 .0000Within Groups 1641 872.5589 .5317Total 1653 940.1517
- - - - - O N E W A Y - - - - -
Variable F7 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 79.7824 6.6485 7.6118 .0000Within Groups 1650 1441.1894 .8734Total 1662 1520.9718
Variable CLIMATE By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 33.0528 2.7544 7.5921 .0000Within Groups 1367 495.9426 .3628Total 1379 528.9954
282
Appendix F
Model Testing
283
Part 1 Structural Equation Model A
284
Multiple Linear Regression
- - Correlation Coefficients - -
CLIMATE GENDER AGE EDUCAT LENGTH_S LENGTH_J GRS_SAL HOURS MODEMPL TRAINSES
CLIMATE 1.0000 .0207 -.0193 -.0399 -.0214 -.0338 .1074 .1475 -.0904 -.1198 ( 1443) ( 1418) ( 1410) ( 1397) ( 1417) ( 1408) ( 1367) ( 1405) ( 1398) ( 1360) P= . P= .436 P= .469 P= .136 P= .421 P= .204 P= .000 P= .000 P= .001 P= .000
GENDER .0207 1.0000 -.0439 -.0328 -.0725 -.0837 -.1829 -.1361 .1111 -.0915 ( 1418) ( 1741) ( 1723) ( 1701) ( 1730) ( 1714) ( 1656) ( 1708) ( 1706) ( 1647) P= .436 P= . P= .068 P= .176 P= .003 P= .001 P= .000 P= .000 P= .000 P= .000
AGE -.0193 -.0439 1.0000 -.2350 .4440 .4114 .2147 .0758 -.2307 .1537 ( 1410) ( 1723) ( 1731) ( 1695) ( 1724) ( 1708) ( 1649) ( 1701) ( 1695) ( 1640) P= .469 P= .068 P= . P= .000 P= .000 P= .000 P= .000 P= .002 P= .000 P= .000
EDUCAT -.0399 -.0328 -.2350 1.0000 -.1501 -.1367 .0513 -.0048 .0657 -.0629 ( 1397) ( 1701) ( 1695) ( 1711) ( 1704) ( 1687) ( 1635) ( 1684) ( 1677) ( 1623) P= .136 P= .176 P= .000 P= . P= .000 P= .000 P= .038 P= .844 P= .007 P= .011
LENGTH_S -.0214 -.0725 .4440 -.1501 1.0000 .7285 .2902 .1847 -.3123 .1194 ( 1417) ( 1730) ( 1724) ( 1704) ( 1739) ( 1720) ( 1657) ( 1711) ( 1706) ( 1648) P= .421 P= .003 P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000 P= .000
LENGTH_J -.0338 -.0837 .4114 -.1367 .7285 1.0000 .2158 .0894 -.2423 .1610 ( 1408) ( 1714) ( 1708) ( 1687) ( 1720) ( 1726) ( 1658) ( 1706) ( 1701) ( 1644) P= .204 P= .001 P= .000 P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000
GRS_SAL .1074 -.1829 .2147 .0513 .2902 .2158 1.0000 .6509 -.5637 .0399 ( 1367) ( 1656) ( 1649) ( 1635) ( 1657) ( 1658) ( 1669) ( 1651) ( 1639) ( 1595) P= .000 P= .000 P= .000 P= .038 P= .000 P= .000 P= . P= .000 P= .000 P= .111
HOURS .1475 -.1361 .0758 -.0048 .1847 .0894 .6509 1.0000 -.6566 .0029 ( 1405) ( 1708) ( 1701) ( 1684) ( 1711) ( 1706) ( 1651) ( 1727) ( 1710) ( 1645) P= .000 P= .000 P= .002 P= .844 P= .000 P= .000 P= .000 P= . P= .000 P= .907
MODEMPL -.0904 .1111 -.2307 .0657 -.3123 -.2423 -.5637 -.6566 1.0000 -.0619 ( 1398) ( 1706) ( 1695) ( 1677) ( 1706) ( 1701) ( 1639) ( 1710) ( 1719) ( 1638) P= .001 P= .000 P= .000 P= .007 P= .000 P= .000 P= .000 P= .000 P= . P= .012
TRAINSES -.1198 -.0915 .1537 -.0629 .1194 .1610 .0399 .0029 -.0619 1.0000 ( 1360) ( 1647) ( 1640) ( 1623) ( 1648) ( 1644) ( 1595) ( 1645) ( 1638) ( 1663) P= .000 P= .000 P= .000 P= .011 P= .000 P= .000 P= .111 P= .907 P= .012 P= .
(Coefficient / (Cases) / 2-tailed Significance) ‘ . ‘ is printed if a coefficient cannot be computed
285
* * * * M U L T I P L E R E G R E S S I O N * * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CLIMATE
Block Number 1. Method: Enter GENDER AGE EDUCAT LENGTH_S LENGTH_J GRS_SAL HOURS MODEMPL TRAINSES
Variable(s) Entered on Step Number 1.. TRAINSES last training session 2.. GRS_SAL current gross salary 3.. EDUCAT education level 4.. GENDER gender 5.. LENGTH_J length of job 6.. AGE age 7.. MODEMPL mode of employment 8.. LENGTH_S length of service 9.. HOURS hours worked per week
Multiple R .21227 Analysis of VarianceR Square .04506 DF Sum of Squares Mean SquareAdjusted R Square .03821 Regression 9 20.92228 2.32470Standard Error .59464 Residual 1254 443.40578 .35359
F = 6.57450 Signif F = .0000
------------------ Variables in the Equation ------------------
Variable B SE B Beta T Sig T
GENDER .047572 .034458 .039192 1.381 .1677AGE -.006936 .018501 -.012163 -.375 .7078EDUCAT -.017371 .010754 -.046347 -1.615 .1065LENGTH_S -.013752 .024284 -.023310 -.566 .5713LENGTH_J -.008069 .025006 -.012781 -.323 .7470GRS_SAL .021161 .013775 .061422 1.536 .1248HOURS .041617 .013458 .132591 3.092 .0020MODEMPL -8.69224E-04 .026909 -.001276 -.032 .9742TRAINSES -.050640 .013198 -.108827 -3.837 .0001(Constant) 4.605277 .156497 29.427 .0000
End Block Number 1 All requested variables entered.
286
Thu Mar 16 15:53:40 2000
Amos Version 3.61 (w32)
by James L. Arbuckle
Copyright 1994-1997 SmallWaters Corporation 1507 E. 53rd Street - #452 Chicago, IL 60615 USA 773-667-8635 Fax: 773-955-6252 http://www.smallwaters.com
******************************************** * Structural Model A * *------------------------------------------* * * ********************************************
Serial number 55501773
Structural Model A Page 1
User-selected options
287
---------------------
Output:
Maximum Likelihood
Output format options:
Compressed output
Minimization options:
Technical output Machine-readable output file
Sample size: 1207
Your model contains the following variables
climate observed endogenous cs_over observed endogenous
gender observed exogenous age observed exogenous educat observed exogenous length_s observed exogenous length_j observed exogenous grs_sal observed exogenous hours observed exogenous modempl observed exogenous trainses observed exogenous
other2 unobserved exogenous other unobserved exogenous
Number of variables in your model: 13 Number of observed variables: 11 Number of unobserved variables: 2 Number of exogenous variables: 11 Number of endogenous variables: 2
Summary of Parameters
Weights Covariances Variances Means Intercepts Total ------- ----------- --------- ----- ---------- ----- Fixed: 2 0 0 0 0 2 Labeled: 0 0 0 0 0 0 Unlabeled: 10 36 11 0 0 57 ------- ----------- --------- ----- ---------- ----- Total: 12 36 11 0 0 59
The model is recursive.
Model: Your_modelComputation of Degrees of Freedom
Number of distinct sample moments: 66 Number of distinct parameters to be estimated: 57 ------------------------- Degrees of freedom: 9
288
Minimization History
0e 8 0.0e+00 -4.1849e-01 1.00e+04 3.36432295221e+03 0 1.00e+04 1e 0 1.9e+01 0.0000e+00 8.71e-01 8.91386526368e+02 18 1.02e+00 2e 0 1.2e+02 0.0000e+00 4.47e-01 5.93916156150e+02 2 0.00e+00 3e 0 5.3e+01 0.0000e+00 4.47e-01 1.91502709126e+02 1 1.24e+00 4e 0 7.3e+01 0.0000e+00 3.75e-01 7.69821486886e+01 1 1.20e+00 5e 0 1.1e+02 0.0000e+00 2.25e-01 5.91740289912e+01 1 1.12e+00 6e 0 1.2e+02 0.0000e+00 6.10e-02 5.84641179855e+01 1 1.03e+00 7e 0 1.2e+02 0.0000e+00 3.53e-03 5.84623686521e+01 1 1.00e+00 8e 0 1.2e+02 0.0000e+00 1.10e-05 5.84623686370e+01 1 1.00e+00
Minimum was achieved
Chi-square = 58.462Degrees of freedom = 9Probability level = 0.000
Maximum Likelihood Estimates----------------------------
Regression Weights: Estimate S.E. C.R. Label------------------- -------- ------- ------- -------
climate <------- gender 0.055 0.035 1.585 climate <---------- age -0.006 0.019 -0.336 climate <------- educat -0.016 0.011 -1.448 climate <----- length_s -0.013 0.024 -0.545 climate <----- length_j -0.011 0.025 -0.416 climate <------ grs_sal 0.021 0.014 1.477 climate <-------- hours 0.039 0.013 2.854 climate <------ modempl -0.012 0.027 -0.433 climate <----- trainses -0.051 0.014 -3.778 cs_over <------ climate 0.478 0.030 16.088
Covariances: Estimate S.E. C.R. Label------------ -------- ------- ------- -------
modempl <----> trainses -0.042 0.033 -1.275 hours <------> trainses -0.063 0.072 -0.866 grs_sal <----> trainses 0.023 0.065 0.353 length_j <---> trainses 0.229 0.035 6.549 length_s <---> trainses 0.174 0.037 4.681 educat <-----> trainses -0.165 0.060 -2.735 age <--------> trainses 0.219 0.040 5.542 gender <-----> trainses -0.039 0.019 -2.129 hours <-------> modempl -1.173 0.061 -19.393 grs_sal <-----> modempl -0.918 0.052 -17.492 length_j <----> modempl -0.184 0.024 -7.511 length_s <----> modempl -0.278 0.027 -10.357 educat <------> modempl 0.094 0.042 2.250 age <---------> modempl -0.227 0.028 -8.129 gender <------> modempl 0.052 0.013 4.006 grs_sal <-------> hours 2.322 0.119 19.458 length_j <------> hours 0.227 0.052 4.333 length_s <------> hours 0.396 0.057 6.952 educat <--------> hours -0.015 0.091 -0.167 age <-----------> hours 0.226 0.059 3.800
289
gender <--------> hours -0.144 0.028 -5.101 length_j <----> grs_sal 0.387 0.048 8.010 length_s <----> grs_sal 0.561 0.053 10.590 educat <------> grs_sal 0.129 0.082 1.572 age <---------> grs_sal 0.418 0.055 7.644 gender <------> grs_sal -0.190 0.026 -7.320 length_s <---> length_j 0.625 0.032 19.449 educat <-----> length_j -0.167 0.044 -3.835 age <--------> length_j 0.386 0.030 12.760 gender <-----> length_j -0.049 0.013 -3.641 educat <-----> length_s -0.225 0.047 -4.807 age <--------> length_s 0.460 0.033 13.947 gender <-----> length_s -0.044 0.014 -3.096 age <----------> educat -0.368 0.050 -7.320 gender <-------> educat -0.010 0.023 -0.426 gender <----------> age -0.038 0.015 -2.524
Variances: Estimate S.E. C.R. Label---------- -------- ------- ------- -------
gender 0.249 0.010 24.556 age 1.108 0.045 24.556 educat 2.626 0.107 24.556 length_s 0.991 0.040 24.556 length_j 0.861 0.035 24.556 grs_sal 3.102 0.126 24.556 hours 3.800 0.155 24.556 modempl 0.800 0.033 24.556 trainses 1.655 0.067 24.556 other2 0.345 0.014 24.556 other 0.385 0.016 24.556
Summary of models-----------------
Model NPAR CMIN DF P CMIN/DF ---------------- ---- --------- -- --------- --------- Your_model 57 58.462 9 0.000 6.496 Saturated model 66 0.000 0 Independence model 11 3332.678 55 0.000 60.594
Model RMR GFI AGFI PGFI ---------------- ---------- ---------- ---------- ---------- Your_model 0.039 0.991 0.937 0.135 Saturated model 0.000 1.000 Independence model 0.390 0.637 0.564 0.531
DELTA1 RHO1 DELTA2 RHO2 Model NFI RFI IFI TLI CFI ---------------- ---------- ---------- ---------- ---------- ---------- Your_model 0.982 0.893 0.985 0.908 0.985 Saturated model 1.000 1.000 1.000 Independence model 0.000 0.000 0.000 0.000 0.000
Model PRATIO PNFI PCFI ---------------- ---------- ---------- ---------- Your_model 0.164 0.161 0.161
290
Saturated model 0.000 0.000 0.000 Independence model 1.000 0.000 0.000
Model NCP LO 90 HI 90 ---------------- ---------- ---------- ---------- Your_model 49.462 28.937 77.483 Saturated model 0.000 0.000 0.000 Independence model 3277.678 3092.130 3470.521
Model FMIN F0 LO 90 HI 90 ---------------- ---------- ---------- ---------- ---------- Your_model 0.048 0.041 0.024 0.064 Saturated model 0.000 0.000 0.000 0.000 Independence model 2.763 2.718 2.564 2.878
Model RMSEA LO 90 HI 90 PCLOSE ---------------- ---------- ---------- ---------- ---------- Your_model 0.068 0.052 0.084 0.035 Independence model 0.222 0.216 0.229 0.000
Model AIC BCC BIC CAIC ---------------- ---------- ---------- ---------- ---------- Your_model 172.462 173.608 599.608 519.928 Saturated model 132.000 133.327 626.590 534.329 Independence model 3354.678 3354.899 3437.109 3421.733
Model ECVI LO 90 HI 90 MECVI ---------------- ---------- ---------- ---------- ---------- Your_model 0.143 0.126 0.166 0.144 Saturated model 0.109 0.109 0.109 0.111 Independence model 2.782 2.628 2.942 2.782
HOELTER HOELTER Model .05 .01 ---------------- ---------- ---------- Your_model 350 447 Independence model 27 30
Execution time summary:
Minimization: 0.380 Miscellaneous: 0.149 Bootstrap: 0.000 Total: 0.529
291
Part 2 Structural Equation Model B
292
- - Correlation Coefficients - -
CS_OVER F1 F2 F3 F4 F5 F6 F7
CS_OVER 1.0000 .3859 .5337 .4116 -.3088 .2928 .2940 .2467 ( 1686) ( 1565) ( 1609) ( 1619) ( 1597) ( 1634) ( 1636) ( 1647) P= . P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= .000
F1 .3859 1.0000 .7021 .7407 -.4852 .6388 .6066 .4491 ( 1565) ( 1650) ( 1602) ( 1598) ( 1585) ( 1612) ( 1621) ( 1626) P= .000 P= . P= .000 P= .000 P= .000 P= .000 P= .000 P= .000
F2 .5337 .7021 1.0000 .7096 -.4526 .6077 .4904 .4372 ( 1609) ( 1602) ( 1700) ( 1636) ( 1632) ( 1653) ( 1658) ( 1673) P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000 P= .000
F3 .4116 .7407 .7096 1.0000 -.4342 .5743 .5002 .4268 ( 1619) ( 1598) ( 1636) ( 1711) ( 1623) ( 1660) ( 1664) ( 1673) P= .000 P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000
F4 -.3088 -.4852 -.4526 -.4342 1.0000 -.3449 -.3048 -.2506 ( 1597) ( 1585) ( 1632) ( 1623) ( 1682) ( 1638) ( 1644) ( 1655) P= .000 P= .000 P= .000 P= .000 P= . P= .000 P= .000 P= .000
F5 .2928 .6388 .6077 .5743 -.3449 1.0000 .5265 .5571 ( 1634) ( 1612) ( 1653) ( 1660) ( 1638) ( 1717) ( 1680) ( 1686) P= .000 P= .000 P= .000 P= .000 P= .000 P= . P= .000 P= .000
F6 .2940 .6066 .4904 .5002 -.3048 .5265 1.0000 .4176 ( 1636) ( 1621) ( 1658) ( 1664) ( 1644) ( 1680) ( 1728) ( 1693) P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= . P= .000
F7 .2467 .4491 .4372 .4268 -.2506 .5571 .4176 1.0000 ( 1647) ( 1626) ( 1673) ( 1673) ( 1655) ( 1686) ( 1693) ( 1740) P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= .
(Coefficient / (Cases) / 2-tailed Significance) ‘ . ‘ is printed if a coefficient cannot becomputed
293
* * * * M U L T I P L E R E G R E S S I O N * * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CS_OVER cust satis - overall
Block Number 1. Method: Enter F1 F2 F3 F4 F5 F6 F7
Variable(s) Entered on Step Number 1.. F7 2.. F4 3.. F6 4.. F3 5.. F5 6.. F2 7.. F1
Multiple R .54734 Analysis of VarianceR Square .29958 DF Sum of Squares Mean SquareAdjusted R Square .29601 Regression 7 200.14226 28.59175Standard Error .58358 Residual 1374 467.94240 .34057
F = 83.95278 Signif F = .0000
------------------ Variables in the Equation ------------------
Variable B SE B Beta T Sig T
F1 -.009840 .027004 -.014670 -.364 .7156F2 .350973 .025594 .489812 13.713 .0000F3 .045969 .024968 .066625 1.841 .0658F4 -.050000 .019042 -.070336 -2.626 .0087F5 -.057323 .019459 -.098170 -2.946 .0033F6 .044920 .027861 .047276 1.612 .1071F7 .022011 .020563 .029941 1.070 .2846(Constant) 2.024319 .176928 11.441 .0000
End Block Number 1 All requested variables entered.
294
Wed Dec 15 14:29:181999
Amos Version 3.61 (w32)
by James L. Arbuckle
Copyright 1994-1997 SmallWaters Corporation 1507 E. 53rd Street - #452 Chicago, IL 60615 USA 773-667-8635 Fax: 773-955-6252 http://www.smallwaters.com
******************************************** * Structural Model B * *------------------------------------------* * * ********************************************
Serial number 55501773
Structural Model B Page 1
User-selected options---------------------
Output:
295
Maximum Likelihood
Output format options:
Compressed output
Minimization options:
Technical output Standardized estimates Squared multiple correlations Machine-readable output file
Sample size: 1443
Your model contains the following variables
cs_over observed endogenous revpari observed endogenous
f1 observed exogenous f2 observed exogenous f3 observed exogenous f4 observed exogenous f5 observed exogenous f6 observed exogenous f7 observed exogenous
other1 unobserved exogenous other2 unobserved exogenous
Number of variables in your model: 11 Number of observed variables: 9 Number of unobserved variables: 2 Number of exogenous variables: 9 Number of endogenous variables: 2
Summary of Parameters
Weights Covariances Variances Means InterceptsTotal ------- ----------- --------- ----- ---------- ----- Fixed: 2 0 0 0 0 2 Labeled: 0 0 0 0 0 0 Unlabeled: 8 21 9 7 2 47 ------- ----------- --------- ----- ---------- ----- Total: 10 21 9 7 2 49
The model is recursive.
Model: Your_modelComputation of Degrees of Freedom
Number of distinct sample moments: 54 Number of distinct parameters to be estimated: 47 ------------------------- Degrees of freedom: 7
Minimization History
0e 12 0.0e+00 -4.4127e-01 1.00e+04 2.96540527289e+05 0 1.00e+04 1e 10 0.0e+00 -5.0243e-01 2.34e+00 1.53851730515e+05 15 1.07e+00 2e* 1 0.0e+00 -2.7700e-03 3.28e+00 6.70092425028e+04 6 1.09e+00
296
3e 0 2.5e+04 0.0000e+00 5.82e+00 1.07828142652e+04 5 9.40e-01 4e 0 2.6e+04 0.0000e+00 1.02e+00 8.78426152151e+03 6 0.00e+00 5e 0 2.4e+04 0.0000e+00 2.88e+00 3.07830203044e+03 2 0.00e+00 6e 0 2.4e+04 0.0000e+00 1.19e+00 1.29238805525e+03 1 1.26e+00 7e 0 2.4e+04 0.0000e+00 4.72e-01 5.36309430307e+02 1 1.28e+00 8e 0 2.4e+04 0.0000e+00 4.99e-01 2.02279336772e+02 1 1.27e+00 9e 0 2.4e+04 0.0000e+00 5.03e-01 7.93102165909e+01 1 1.23e+00 10e 0 2.4e+04 0.0000e+00 3.73e-01 5.13381868729e+01 1 1.16e+00 11e 0 2.4e+04 0.0000e+00 1.47e-01 4.90271902153e+01 1 1.06e+00 12e 0 2.4e+04 0.0000e+00 1.68e-02 4.90036264275e+01 1 1.01e+00 13e 0 2.4e+04 0.0000e+00 1.93e-04 4.90036234268e+01 1 1.00e+00
Minimum was achieved
Chi-square = 49.004Degrees of freedom = 7Probability level = 0.000
Maximum Likelihood Estimates----------------------------
Regression Weights: Estimate S.E. C.R. Label------------------- -------- ------- ------- -------
cs_over <--------- f1 -0.068 0.053 -1.288 cs_over <--------- f2 0.350 0.051 6.883 cs_over <--------- f3 0.090 0.049 1.820 cs_over <--------- f4 -0.017 0.037 -0.468 cs_over <--------- f5 -0.047 0.038 -1.235 cs_over <--------- f6 -0.035 0.055 -0.637 cs_over <--------- f7 0.049 0.041 1.196 revpari <---- cs_over 2.393 0.455 5.257
Standardized Regression Weights: Estimate-------------------------------- --------
cs_over <--------- f1 -0.058 cs_over <--------- f2 0.279 cs_over <--------- f3 0.075 cs_over <--------- f4 -0.014 cs_over <--------- f5 -0.046 cs_over <--------- f6 -0.021 cs_over <--------- f7 0.038 revpari <---- cs_over 0.137Means: Estimate S.E. C.R. Label------ -------- ------- ------- -------
f1 5.131 0.028 186.311 f2 5.193 0.026 201.990 f3 4.976 0.027 185.788 f4 3.537 0.026 137.134 f5 4.866 0.032 154.356 f6 4.992 0.019 256.837 f7 5.641 0.025 225.760
Intercepts: Estimate S.E. C.R. Label----------- -------- ------- ------- -------
cs_over 2.410 0.344 7.007 revpari 80.307 1.963 40.908
297
Covariances: Estimate S.E. C.R. Label------------ -------- ------- ------- -------
f6 <-------------> f7 0.295 0.020 14.723 f5 <-------------> f7 0.645 0.034 18.757 f4 <-------------> f7 -0.241 0.025 -9.530 f3 <-------------> f7 0.401 0.028 14.566 f2 <-------------> f7 0.405 0.027 15.214 f1 <-------------> f7 0.453 0.029 15.756 f5 <-------------> f6 0.465 0.026 17.691 f4 <-------------> f6 -0.232 0.020 -11.616 f3 <-------------> f6 0.371 0.022 16.813 f2 <-------------> f6 0.358 0.021 16.900 f1 <-------------> f6 0.469 0.024 19.713 f4 <-------------> f5 -0.425 0.033 -12.951 f3 <-------------> f5 0.691 0.037 18.749 f2 <-------------> f5 0.710 0.036 19.706 f1 <-------------> f5 0.802 0.039 20.479 f3 <-------------> f4 -0.439 0.029 -15.328 f2 <-------------> f4 -0.451 0.028 -16.185 f1 <-------------> f4 -0.504 0.030 -16.775 f2 <-------------> f3 0.705 0.032 21.992 f1 <-------------> f3 0.782 0.035 22.494 f1 <-------------> f2 0.717 0.033 21.820
Correlations: Estimate------------- --------
f6 <-------------> f7 0.421 f5 <-------------> f7 0.568 f4 <-------------> f7 -0.259 f3 <-------------> f7 0.415 f2 <-------------> f7 0.437 f1 <-------------> f7 0.456 f5 <-------------> f6 0.527 f4 <-------------> f6 -0.321 f3 <-------------> f6 0.494 f2 <-------------> f6 0.497 f1 <-------------> f6 0.607 f4 <-------------> f5 -0.363 f3 <-------------> f5 0.568 f2 <-------------> f5 0.607 f1 <-------------> f5 0.640 f3 <-------------> f4 -0.441 f2 <-------------> f4 -0.471 f1 <-------------> f4 -0.492 f2 <-------------> f3 0.710 f1 <-------------> f3 0.735 f1 <-------------> f2 0.702
Variances: Estimate S.E. C.R. Label---------- -------- ------- ------- -------
f1 1.094 0.041 26.851 f2 0.953 0.036 26.851 f3 1.035 0.039 26.851 f4 0.959 0.036 26.851 f5 1.433 0.053 26.851 f6 0.545 0.020 26.851 f7 0.900 0.034 26.851 other1 1.387 0.052 26.851 other2 450.482 16.777 26.851
Squared Multiple Correlations: Estimate------------------------------ --------
298
cs_over 0.080 revpari 0.019
Summary of models-----------------
Model NPAR CMIN DF P CMIN/DF ---------------- ---- --------- -- --------- --------- Your_model 47 49.004 7 0.000 7.001 Saturated model 54 0.000 0 Independence model 9 45950.334 45 0.000 1021.119
DELTA1 RHO1 DELTA2 RHO2 Model NFI RFI IFI TLICFI ---------------- ---------- ---------- ---------- ---------- ---------- Your_model 0.999 0.993 0.999 0.9940.999 Saturated model 1.000 1.0001.000 Independence model 0.000 0.000 0.000 0.0000.000
Model PRATIO PNFI PCFI ---------------- ---------- ---------- ---------- Your_model 0.156 0.155 0.155 Saturated model 0.000 0.000 0.000 Independence model 1.000 0.000 0.000
Model NCP LO 90 HI 90 ---------------- ---------- ---------- ---------- Your_model 42.004 23.438 68.055 Saturated model 0.000 0.000 0.000 Independence model 45905.334 45203.467 46613.479
Model FMIN F0 LO 90 HI 90 ---------------- ---------- ---------- ---------- ---------- Your_model 0.034 0.029 0.016 0.047 Saturated model 0.000 0.000 0.000 0.000 Independence model 31.866 31.834 31.348 32.326
Model RMSEA LO 90 HI 90 PCLOSE ---------------- ---------- ---------- ---------- ---------- Your_model 0.065 0.048 0.082 0.071 Independence model 0.841 0.835 0.848 0.000
Model AIC BCC BIC CAIC ---------------- ---------- ---------- ---------- ---------- Your_model 143.004 143.660 Saturated model 108.000 108.754 Independence model 45968.334 45968.460
Model ECVI LO 90 HI 90 MECVI ---------------- ---------- ---------- ---------- ----------
299
Your_model 0.099 0.086 0.117 0.100 Saturated model 0.075 0.075 0.075 0.075 Independence model 31.878 31.391 32.369 31.878
HOELTER HOELTER Model .05 .01 ---------------- ---------- ---------- Your_model 414 544 Independence model 2 3
Execution time summary:
Minimization: 0.385 Miscellaneous: 0.122 Bootstrap: 0.000 Total: 0.507
300
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