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AFBE JOURNAL
Special Issue of Selected Papers from AFBE-UNITEN Conference, 2012
Volume 5, No. 3, December, 2012
ISSN 2071-7873
I
TABLE OF CONTENTS
ACADEMIC PAPERS
Bhushan Chandra Das, K.S.Chakraborty, Raveesh Krishnankutty,
―Industrial Sickness in Micro and Small Manufacturing Enterprises in
Backward Regions of India: A Case Study of Tripura‖
240
Fahmi Zaidi Bin Abdul Razak, Noor Azizah Binti Noorashid, Hussin Bin
Salleh, Fairus Bin Ahmad, ―Examining UCSA Student Portal Success
From The Perspective of Modified De Lone Mclean Success Model‖
252
Saripah Basar, ―Factors Influencing Continuance Intention to Use Student
Portal Among University College Shahputra‘s Students‖
275
Victor Egan, ―The Breakdown of Global Capitalism: The Future of Business
Under a Collapsitarian Scenario‖ 292
K.S.Chakraborty, Raveesh Krishnankutty, B.B.Sarkar, Bhushan Chandra
Das, ―Liquidity Aspects of Large Corporate Business : A Study With
Refrence to Listed Companies In India‖
319
Marthin Nanere, Phil Trebilcock, Apollo Nsubuga-Kyobe, ―Understanding
Student Tutorial Attendance Beliefs‖
335
Saripah Basar, ―The Effects of Teaching Quality on Student Satisfaction
And Behavioural Intentions from Viewpoint of University Students.‖
347
Chris Perryer, Brenda Scott-Ladd, Catherine Leighton, ―Gamification:
Implications for Workplace Intrinsic Motivation in The 21st Century‖
371
240
INDUSTRIAL SICKNESS IN MICRO AND SMALL MANUFACTURING
ENTERPRISES IN BACKWARD REGIONS OF INDIA: A CASE STUDY OF TRIPURA
Bhushan Chandra Das, Associate Professor, Department of Commerce, M.B.B. College,
Agartala, Tripura-799004, India, Mobile: +919436453660
Email: [email protected]
K.S.Chakraborty, Regional Director, Indira Gandhi National Open University, Agartala Regional
Centre, Tripura-799004, India and Guest Faculty , School of Management, Tripura University,
India.(email id : [email protected]) Tele: (Office) +91 3812516715, (Residence) +91
3812517711,Fax: +91 3812516266, Mobile +91 9436123605,
Email:[email protected]
Raveesh Krishnankutty, Research Scholar, ICFAI University, Tripura, Fax: +91 3812516266,
Mobile +91 8974109325, Email: [email protected]
ABSTRACT
Industrial sickness among the micro and small sector of Tripura, one of the most backward
regions of India, has assumed a serious proportion and become a cause of anxiety for the policy
makers of the state. In this background present study is designed with the object to examine the
causes of industrial sickness in micro and small manufacturing enterprises in Tripura. A detailed
survey among the sample units has been conducted. The analyses have been done with the help
of statistical tools for identifying the factors affecting reasons for sickness. It is found that market
demand, management issues, obsolete technology, diversion of funds, inadequate working
capital, poor realization of debts, etc. are the major causes of sickness in micro and small sector
of Tripura.
Keywords:Industrial sickness, micro and small sector, causes of sickness, industrial development
INTRODUCTION
The micro, small and medium enterprises (MSME) sector contributes significantly to the
manufacturing output, employment and exports of India. It is estimated that in terms of value, the
sector accounts for about 45 percent of the manufacturing output and 40 percent of the total
exports of the country. The sector is estimated to employ about 59 million persons in over 26
million units throughout the country. The sector contributes nearly 8.00 per cent of GDP in the
year 2009-10. Further, this sector has consistently registered a higher growth rate than the rest of
the industrial sector. There are over 6000 products ranging from traditional to high-tech items,
which are being manufactured by the MSMEs in India. Constant support to MSME sector by the
government in terms of infrastructure mechanism, fiscal and monetary policies have helped this
241
sector to emerge as a dynamic and vibrant sector of Indian economy. It is well known that the
MSME sector provides the maximum opportunities for both self-employment and jobs after
agriculture sector in India (Government of India, 2010).
The necessity of industrialisation as a means of achieving sustainable growth and prosperity has
been therefore, recognised in the development strategy of any region in India. And Tripura is no
exception. The state is yet to emerge out of industrial backwardness. This is reflected by the
fact that the Central Government has declared the entire state as 'A' category backward area for
the purpose of giving central investment subsidy. The overall backwardness of the state is
evident from the composite infrastructural index evolved by the Center for Monitoring Indian
Economy (CMIE). As per the CMIE index, Tripura is one of the most backward states in the
country. The process of setting up large and medium scale industries in the state is not
satisfactory due to several constraints. At present there are only five medium scale industrial
units and no large scale industry in the state. Development of sustainable micro and small
enterprise is therefore the only alternative to the problems that need immediate attentions viz.,
utilisation of local resources, unemployment and dispersal of industrial activity (Chakraborty,
2006). Beside growing significance of MSME sector to state's economy, sickness among the
micro and small units also seems to have become a feature of the state's economy in recent
times. It has assumed a serious proportion and become a cause of anxiety for the policy makers
of the state.
LITERATURE REVIEW
Most the research studies on sickness are pertaining to failed and non-failed firms. A few related
studies without empirical analysis have been conducted to identify the general problems of small
scale industry. The related studies are discussed below in brief.
Vepa (1971) has observed one of the main problems confronting the growth of small industry in
most of the developing countries is lack of adequate finance. State Bank of India (1975)
constituted a study team under the chairmanship of J. S. Varshneya to find out the reasons for
sickness in small scale units, taking preventive steps and revival of sick units to restore them to
health for the overall economic development. A high powered committee was also constituted in
1978 under the chairmanship of the Union Finance Minister to discuss the problem of sick
industrial units and the role of banks and financial institutions. Bidani and Mitra (1982) have
stated that industrial sickness develops gradually and is not an overnight phenomenon. If the
financial institutions are taken into confidence at the initial stage, when the problem arises, the
diagnosis and treatment would certainly be much easier. The study is intended for bankers and
persons working in term lending financial institutions and consultants undertaking studies for
revival of sick units.
Bala (1984) has critically evaluated the Government policies and their implementation in the
perspective of the benefits and problems of the small entrepreneur. The study is a comprehensive
one covering loans, subsidies and assistance related to marketing; raw materials, machinery,
consultancy and training of entrepreneurs and workers, power and labour policy. Khandelwal
242
(1985) has made a study of 40 small scale units working in Jodhpur Industrial Estates. The study
is based on information collected through personal visits and questionnaire. His work throws
light on working capital management practices adopted by Small Scale Industry units. The
analysis of aggregated working capital management is followed by an intensive analysis of
individual components of Working Capital, viz., management of cash, accounts receivables and
inventories. Hasib Committee constituted by Reserve Bank of India (1986) considered various
aspects of SSI units including identification of incipient sickness in SSI units.
Dave (1987) has examined the strengths and weaknesses of management practices in textile units
against the norms laid down by various authorities of Management Science. She has attempted to
evaluate the performance of the units from various aspects of management such as general
management, planning, marketing, technologically, labour-management relations and finance.
The most significant contribution of her study is to examine the linkage between quality of
management practices and the problems of industrial sickness. Banerjee (1990) mentioned that in
India, some industrial units are born sick, sickness is thrust upon some while others become sick
due to a number of causes. Lacks of planning and imperfect project formulation give birth to a
sick unit. Choices of a product without analyzing the market, improper site selection, tardy
implantation of the project, etc., are other causes.
The industrial sickness in SSI sector has been caused by several factors like mismanagement,
faulty or defective planning, imperfect project formulation, market recession, problems of
marketing, non availability of quality raw-materials, shortage of power, scarcity of funds, poor
collection of bad & doubtful debts, old plant & machinery, delay in sanctioning loan by
commercial banks and financial institutions, government policy etc. (Khan, 1990). The financial
problems and industrial sickness in small scale industry of Punjab has been studied by Bansal
(1992). He has shown in his study how the industrial sickness hindering the economic
development of the state. Due to miscalculations, the industrial units face the industrial sickness
and are ultimately forced to close their shutters permanently creating several severe problems
like unemployment and wastage of resources (Agarwal, 1997).
Junejo, Rohra and Maitlo (2007) found that the most of entrepreneurs are completely unaware
about requirements for making better feasibility reports. Many projects were sick by birth
because of inadequate feasibility reports regarding the demand of product in various markets,
wrong choice of technology, improper forecasting of financial requirements, delayed in supply of
plant and machinery or in their installation or release of funds by financiers.
The increasing incidence of sickness in the SSI units of Tripura is a woeful phenomenon, which
is causing a severe strain on the banks and financial institutions (Das, 2006). Sickness in an
industrial unit, however, cannot be attributed to a single factor alone. It is a cumulative effect of
many factors, which may be inter-related or independent of each other. It would, therefore, be
difficult to recognize a particular factor responsible for a particular sick unit. There are several
factors responsible for sickness in the small scale sector of Tripura like frequent changes in the
policies of state government, banks and financial institutions, inadequate working capital,
243
shortage of skilled workers, limited market support by the government, outdated technology etc.
(Chakraborty, 2002)
In the Indian context or in the context of other states of India, there are lots of studies have been
done on industrial sickness. The survey of the existing research work indicates that there is no
study on industrial sickness in Tripura as yet. In regards to other aspect of socio-economic
problems of the state, highly worthy studies have been undertaken by different scholars.
Unfortunately the problem of industrial sickness in Tripura has not been able to draw the
attention of researchers to any noticeable extent. At present there are only a few articles on the
issue of sickness in the state. Other articles and industrial potential survey have mentioned the
problem as only a passing reference. Hence there is the necessity of a study incorporating the
issues associated with sickness in Tripura. The present study is an attempt to provide a complete
picture of industrial sickness of the state with reference to micro and small units.
RATIONALE AND OBJECTIVE
The magnitude of the problem is evident from the fact that 42.70 per cent of the registered micro
and small units in the state are closed, which is fairly high compared to other states of the North-
East Region (NEC, 2006). According to the opinion of officials of Department of Industries &
Commerce (DIC) there are some more working units which are either sick or on the verge of
getting so. Tripura has the highest percentage of closed units followed by Nagaland (31.20
percent), Assam (28.11 percent) and Mizoram (25.02 percent). So far as the number of closed
units are concerned, Assam has the highest (1732) followed by Tripura (603), Mizoram (306)
and Manipur (169) respectively.
According to an estimate, out of total 6914 sick registered and unregistered units in the state,
there are only 549 potentially viable sick units and 5693 units are non-viable sick units and
viability of 672 units is yet to be decided. In comparison to other states of north eastern region
Tripura has the highest number of sick registered and unregistered units after Assam. According
to an estimate made by Department of Industries & Commerce, Govt. of Tripura, non-viable sick
micro and small units accounts more than 90 per cent of the sick units.
This study is motivated by the concern for the sick industries of the state and thus to draw policy
prescription leading to economic development because a backward state like Tripura with limited
investible resources and huge population, cannot afford the loss of production, income and
employment involved. It may be aptly argued that the pathology and the therapeutic aspects of
industrial sickness should be as much a subject of academic study as the anatomy and physiology
of normal healthy industrial organisation which is the conventional field of study in universities
and academic institutions. In this background present study is designed with the object to
examine the causes of industrial sickness in micro and small manufacturing enterprises in
Tripura.
METHOD OF ENQUIRY
The study has explored the necessary data sources and adopted the appropriate method to deal
244
with the issues. In the course of analyzing the issue of industrial sickness, a number of text and
reference books, Government publications, notifications, reports, publications of Development
Commissioner (Ministry of SSI, Govt. of India), publications of North East Council (NEC) and
other published and unpublished documents relating to the study has been consulted. An
industrial potential survey, techno-economic survey, all India censuses of micro and small scale
industries has also been consulted. The data regarding the micro and small units of the state are
collected from the Department of Industries and Commerce, Govt. of Tripura.
Analysing the issue of industrial sickness a survey of micro and small units of the state is
conducted. A detailed questionnaire is prepared, and personal interviews and survey of selected
respondent units under study are conducted. As per data provided by the Department of
Industries, Government of Tripura, there are 2,934 registered MSME in the state, out of which
2361 units(80.47 percent) is in manufacturing sector and 573 units(19.53 percent) is in service
sector. The study deals with manufacturing units only. Thus the population is 2361manufacturing
units of the state, consisting of 2205 micro, 152 small and 4 medium units. As the study deals
with the micro and small manufacturing units of the state the population size is reduced to 2357
from 2361. The Department of Industries, Government of Tripura, has classified the above
mentioned 2357 micro and small manufacturing units into 19 categories including one ‗others‘
category. 455 micro and 32 small units, thus total of 487 units are selected on the basis of
stratified random sampling from the population as sample for the study. The selected sample
represents 20.66 percent of the population.
To understand the issue of reasons of sickness or closure, factor analysis has been carried out
using SPSS 17 software for extracting the factors. To avoid the cross loading among the factors
of the variables Eigen value criteria (greater than one) and Varimax Rotation criteria has been
used. Before conducting the factor analysis the reliability of the questionnaire has also been
checked. Sample adequacy has been checked using KMO and Bartlett‘s test which is satisfactory
it came as more than 0.6. This shows that number of sample collected is enough for the study.
It is found that the definition of sickness has been changing over time in India. The RBI
appointed committees look into this issue from time to time. The latest definition of sickness
given by the Working Group on Rehabilitation of Sick Units set up by the RBI (Kohli
Committee) is that i) if any of the borrowed accounts of the unit remains substandard for more
than six months; or ii) there is erosion in the net worth due to accumulated losses to the extent of
50 per cent of its net worth during the previous accounting year, and iii) the unit has been in
commercial production for at least two years. In the present study above definition has been
considered to understand the situation in Tripura.
RESULTS AND DISCUSSION
It is found in the survey that out of 487 selected sample units, only 237 units (48.67 percent) are
working, while remaining 250 units (51.33 percent) are either sick or closed in Tripura. High rate
of incidence of sickness and closure is observed in the units of wood products, jute based
products, paper & paper products, tea & allied products and electrical products. On the other
245
hand low rate of incidence of sickness and closure is found in the units of beverage & tobacco,
metal products, handloom, hosiery & garments, handicrafts, incense sticks & other bamboo &
cane products. It is observed that 85 percent sick and closed units of the sample are situated in
rural areas. So far as ages of the sample units are concerned it is found that one third of the
unsuccessful units belongs to the category of 20 to 30 years.
It is also found that 84 percent of the closed and sick units, i.e. 209 closed or sick units, are sole
proprietorship. It appears that the sole proprietorship business not only plays an important role in
the development of small sector in the state but at the same time they are equally responsible for
alarming rate of industrial sickness in the state. The data of the owners of the sample
unsuccessful (sick or closed) sole proprietorship units pertaining to their age, educational
qualification, cast category and religion are analysed. It is observed that educational
qualifications of the majority of the owners of unsuccessful sole proprietorship sample are only
upto 12th
standard or H.S. passed. The data reveals that educational qualification of the owners in
case of sole proprietorship small business in Tripura is closely associated with the success of the
business. The age, cast category and religion of the owners of the sample sole proprietorship
units are found insignificant. However the presence of Scheduled Tribe in the sample of sole
proprietorship units indicates less participation of Scheduled Tribe (ST) community in the
industrial activities of the state.
Many factors are responsible for sickness in industrial units. These may be external and internal
factors. In the sample selected for the present study more than 50 percent of the sample units are
sick or closed. In the study an attempt is made to look into the reasons for sickness. In order to
get a better understanding of the reason of industrial sickness the opinion of the owners of the
sick units in the sample is sought. From their view it can more or less be understood that sickness
or closure is a result of various factors. After talking with them around nineteen (19) factors were
identified that resulted in sickness or closure. Not all agreed on these factors. Some agreed, some
strongly agreed, some disagreed, some strongly disagreed and some kept undecided in each of
these factors. But more or less these nineteen factors have some impact in the sickness or closure
of 250 units. This list is not an exhaustive and purely based on the perception of the respondents.
There may be other more technical and critical reasons which may be more crucial for sickness
but not mentioned here as they may have escaped the realization of the respondents.
On scrutiny of the opinion of the respondents it is seen that most of the factors do not have the
same degree of influence in the sickness of the enterprises. The views of the respondents are
expressed in a 5-point Likert scale. The two extremes are ‗strongly agreed‘ and ‗strongly
disagreed‘. The midpoint is ‗undecided‘. Between the midpoint and the two extremes are two
points on both sides. These ‗agreed‘ and ‗disagreed‘. The reasons of sickness as revealed through
the opinion survey are shown below.
TABLE 1 : REASONS OF SICKNESS INCLUDING CLOSURE OF THE SAMPLE &
CLOSED UNIT
Sl.
No. Reasons
No. of Respondent Total
1 2 3 4 5
246
1 Mismanagement & Inadequate Planning 158 37 9 27 19 250
2 Lack of Basic Industrial Infrastructure 93 52 14 56 35 250
3 Lack of Advanced Technology 78 43 6 49 74 250
4 Inadequate Working Capital 176 52 1 12 9 250
5 Inadequate Support from Banks and other
Financial Institutions 172 44 5 16 13 250
6 Poor Maintenance of Records and
Accounts 68 52 9 46 75 250
7 Lack of Market Demand 188 31 4 19 8 250
8 Poor Collection of Bad and Doubtful
Debts and Marketing Problems 119 47 6 37 41 250
9 Slow Turnover of Inventory 105 47 9 48 41 250
10 High Cost of Production 168 49 3 17 13 250
11 Labour Problem 17 29 12 98 104 250
12 Lack of Availability of Skilled & Semi-
Skilled Labour 179 38 4 21 8 250
13 Shortage of Power 28 62 5 67 88 250
14 Delayed Payment of Government
Purchase 171 57 2 14 6 250
15 Unattractive rate of Taxes 34 23 7 75 111 250
16 Delays in Rehabilitation of Sick Units 127 52 9 27 35 250
17 Extremist & Insurgency Problem 21 33 17 83 96 250
18 Government Interference 31 32 27 83 77 250
19 Diversion of Funds from the Business 77 43 21 30 79 250
Strongly Agreed (1), Agreed (2), Undecided (3), Disagreed (4), Strongly Disagreed (5)
Source: Field Survey
It is interesting to note from the above table that lack of market demand was identified by the
respondents as one of the main contributor to sickness. Shortage of working funds, inadequate
support from the banks and financial institutions, mismanagement & inadequate planning, lack
of availability of skilled & semi-skilled labour, high cost of production etc. are the other major
contributors. The information received from the respondents reveals that some of the major
challenges faced by the micro and small enterprises are limited budget for marketing, lack of
market intelligence on the demand, delayed payments from the government , information gap
regarding market, non-availability of raw materials at reasonable prices, lack of adequate
infrastructure like all weather roads, resistance to technological upgradation, lack of adequate
number of entrepreneurship development institutes, complex government regulations,
unstructured incentive systems, multiplicity of laws and regulators, limited financial resources,
lack of organisational, financial and management skills and expertise, diversion of funds,
obsolete technology etc.
It reveals from the observation of the officials of banks and financial institutions that while
market demand, management issues, willful default in payment of loan and diversion of funds
247
were found to be major contributors to sickness in micro and small sector of Tripura by the
majority of the respondents from financial institutions, the majority of respondents from banks
attributed high weightage to market factors and management factors as contributors to sickness.
If the views of all respondents are taken together market demand, management issues, diversion
of funds, inadequate working capital, and poor realisation of debts were the major causes of
sickness in micro and small sector of Tripura.
For identifying the factors affecting reasons for sickness we have used the factor analysis. Before
conducting factor analysis we have checked the reliability of the questions asked. The reliability
statistic Cronbach's Alpha for the parts came to be 0.625 shows it quite satisfactory for the
further proceedings.
TABLE 2: RELIABILITY OF QUESTIONNAIRE RELIABILITY STATISTICS OF QUESTINAIRE
FRAMEWORK FOR THE PILOT STUDY
Cronbach's Alpha 0.625
No. of Items 19
After that we have done factor analysis, it has been carried out using SPSS 17 software for
extracting the factors. For avoiding the cross loading among the factors of the variables Eigen
value criteria (greater than one) and Varimax Rotation criteria has been used respectively. First
of all we will present sample adequacy result. Sample adequacy has been checked using KMO
and Bartlett‘s test which is satisfactory it came as more than 0.6. This shows that number of
sample collected is enough for study. Table 3 shows the summary results of the sample
adequacy.
TABLE 3: RESULT OF KMO AND BARTLETT’s TEST
KMO measure of sampling adequacy 0.621
Chi-square 383.606
DF 136
Sig. .000
In the second step, summary of the extracted factors and the total variance explained by total
number of extracted factors has been presented. Because of cross loading we have omitted two
variables (extremist and insurgency problem, shortage of power) and then run the analysis. It is
notice that these extracted factors are obtained after avoiding the cross loadings. We found that
six factors are loaded and it explains 52.053%.variance. This shows that the variables included in
the questionnaire are able to explain only 52.053%.of the total variation and reset in unexplained.
Table 4 shows the result of the total variance explained.
TABLE 4 : TOTAL VARIANCE EXPLAINED
No. of Factor loaded 6
Factor 1 12.170%
248
Factor 2 8.510%
Factor 3 8.293%
Factor 4 8.082%
Factor 5 7.823%
Factor 6 7.176%
Total variance explained 52.053%
Four variables are loaded under factor 1 and it is explaining 12.170% of variance out of 52.053%
total variance explained. Factor 2, factor 3 and factor 4 are explaining more than 8% of variance
out of 52.053% total variance explained. And factor 5 and factor 6 are explaining more than 7%
of variance out of 52.053% total variance explained. Table 5 showing the summary result of the
factors loaded under each factors.
TABLE 5: VARIMAX ROTATED LOADING FOR REASONS OF SICKNESS
Variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Mismanagement and
inadequate planning .704
Lack of basic industrial
infrastructure .669
Lack of advanced
technology .634
Unattractive rate of Taxes .526
Slow Turnover of Inventory .675
Inadequate Support from
Banks and other Financial
Institutions
.646
Inadequate Working Capital .560
Delays in Rehabilitation of
Sick Units .667
Diversion of Funds from the
Business .616
Labour problem .495
Government Interference .801
Poor Maintenance of
Records and Accounts .501
Lack of Market Demand .695
Delayed Payment of
Government Purchase .551
249
Lack of Availability of
Skilled & Semi-Skilled
Labour
.471
High Cost of Production .797
Poor Collection of Bad and
Doubtful Debts and
Marketing Problems
.535
CONCLUDING REMARKS
There are different perspectives in defining industrial sickness in India. At the political level, by
and large, sickness gets forced recognition only when an enterprise closes its doors or is on the
brink of closure, creating a serious employment problem. The lending banks and financial
institutions look the problem from the view point of recovering their money and they regard an
enterprise as sick if the recovery of their dues seems uncertain. The proprietors look the issue
from the view point of return on their investment. Thus the industrial sickness has been defined
by different authorities in different ways. The industrial sickness is a situation where the rate of
return on investment in a unit is insignificantly and continuously less than the prevailing rates on
similar investments. There is no one definition which suits all the purposes of defining a sick
unit. The analysis of the different perspectives of definition of sickness reveals that the
identification of a unit as sick is so late in India that the possibilities of its revival recede.
Therefore, there is a need to hasten the process of identification of a unit as sick by way of
change in the definition of sickness.
The micro and small entrepreneurs at the time of project planning should evolve a sound equity
based capital structure. Financial institutions too should strive to make the entrepreneurs aware
of risk inherent in addition of excessive debt into the capital structure. Higher the amount of
debt, higher obviously would be the interest and amortization. Only in exceptional cases, where
profitability is relatively high, and adequate owner‘s capital is not being available, the upper
limit of debt to net worth ratio be utilized. Under adverse conditions with unstable and
unpredictable cash flows, the risk of debt financing is obviously to be considered with due
caution. Similarly banks and financial institutions should not allow the micro and small scale
units to fully utilize their debt rising capacity in the beginning itself. The unused part of debt
rising capacity should always be reserved for contingent situations. It is observed that in a
number of situations cost of the project over-runs during gestation period itself. It could either be
due to inflationary trends or under estimation of costs due to lack of experience and knowledge.
To meet the over-run costs of the project additional term loan be sanctioned. To take stock of
such situations debt-equity ratio be monitored.
One of the problems faced by micro and small enterprises in Tripura is obsolete technology.
These units are not able to compete with the large industries and cheap imports. There is little
availability of funds with the promoters for technological upgradation. Adaptation of technology
developed in other parts of the country for micro and small sector also needs to be considered for
250
making them more cost effective and dovetailed to the requirements of the customer. Sickness
from technological obsolescence can be prevented through modernization. Therefore, large scale
modernization should be introduced in the micro and small industrial units of Tripura. Some sick
units should be allowed to be amalgamated with viable units. And the management of such
amalgamated units should be left free to decide how it can rehabilitate and improve its working.
Inadequate working capital is one of the main reasons of sickness in micro and small enterprises
of Tripura. The banks and financial institutions should ensure that working capital needs of the
business units have been adequately estimated at the time of preparation of project report. The
working capital needs should be adequately financed out of the owner‘s capital and long term
sources of funds besides financing by commercial banks. The institution granting term finance
should bridge the gap existing between the total needs and sources of working capital so that the
unit runs smoothly. The ratios of net sales to gross working capital and bank finance to working
capital gap should be made use of in determining the adequate working capital requirements and
the gap. The banks and financial institutions before sanctioning of loan should also ensure about
the future prospects of the product and services. The market grid is of considerable for capital
structure planning. If the situation is congenial for stable sales and growth prospects, it is
obviously a favorable condition for higher debt financing of project.
REFERENCES
Agarwal A.N. (1997) Indian Economy, New Delhi: Wishwa Prakashani.
Bala Shashi (1984) Management of Small Scale Industries – Problems, Government Policy and
Assistance, Finance, Production, Marketing, Labour Management, New Delhi: Deep and Deep
Publications.
Banerjee B. (1990) Financial Policy and Management Accounting, Calcutta: The World Press
Ltd., Calcutta,
Bansal S. K. (1992) Financial Problems of Small Scale Industries, New Delhi: Anmol
Publications.
Bidani, S. S. and Mitra, P. K. (1982) Industrial Sickness- Identification and Rehabilitation, New
Delhi: Vision Books.
Chakraborty, K. S., (2002), Sickness in Small Scale Industries of Tripura, Vidyasagar
University Journal of Commerce, pp
Chakraborty, K. S. (2006) A Glimpse into the Development of SSI in Tripura, Entrepreneurship
and Small Business Development, edited by Dr. Kiran Sankar Chakraborty, New Delhi: Mittal
Publications,
251
Das, B. C.(2006), Industrial Sickness in SSI of Tripura, in Entrepreneurship and Small Business
Development edited by Kiran Sankar Chakraborty, New Delhi : Mittal Publications
Dave Nalini V. (1987), Industrial Sickness and Key Areas of Management, New Delhi: Deep
and Deep Publications.
Government of India, Ministry of MSME (2010), Annual Report-2009-10, New Delhi.
Government of India, Ministry of MSME (2010), Fourth All India Census, New Delhi.
Junejo, Mumtaz A., Rohra, Chandan Lal and Maitlo, Ghulam Murtaza, (2007), Sickness in
Small-Scale Industries of Sindh: Causes & Remedies. A Case Study of Larkana Estate Area,
Australian Journal of Basic and Applied Sciences, INSI net Publication, visited on 08.08.2010.
Khan, N. A. (1990), Sickness in Industrial Units, Anmol Publication, New Delhi.
Khandelwal N. M. (1985), Working Capital Management in Small Scale Industries, Ashish
Publishing House, New Delhi.
North Eastern Council (2006), Basic Statistics of North Eastern Region, Shillong.
Vepa, R. K., (1971) Small Industry in the Seventies, Vikash Publications, London.
252
EXAMINING UCSA STUDENT PORTAL SUCCESS FROM THE PERSPECTIVE OF
MODIFIED De LONE McLEAN SUCCESS MODEL
Fahmi Zaidi Bin Abdul Razak
Noor Azizah Binti Noorashid
Hussin Bin Salleh
Fairus Bin Ahmad
Faculty of Education & Social Science University College ShahPutra (UCSA)
BIM Point, Bandar Indera Mahkota, 25200 Kuantan, Pahang, Malaysia
Tel.: 609-5737777
Fax: 609-5738899
ABSTRACT
UCSA’s student portal has been implemented since 2005. Due to the fact that UCSA’s student
portal is only for registration purposes and time-table viewing, the hit account throughout the
year was not so high. Based on limited IT budgets and the need to justify investment in student
portals, assessing the benefits of these is an important field in research and practice .This study
uses the modified De Lone McLean success model (Delone & McLean, 2003) in the context of a
student portal. The hypothesized model is validated empirically using a sample collected from
279 students of UCSA. The results demonstrate that satisfaction was found to be positively
related to users’ continuance intention explaining a total of 67% variance. The implications of
these findings for e-learning practitioners are discussed at the end of this work.
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Keywords: continuance intention, service quality, satisfaction, structural equation modeling,
student portal
INTRODUCTION
The measurement of information systems (IS) success or effectiveness has been widely
investigated by the IS research community. However there is a lack of studies on applying IS
success model in the context of continuance intention. Previously, numerous studies only
focusing on initial acceptance e.g.; (Lin, (2007); Masrek, (2007); Chen & Cheng, (2009) whereas
eventual success depend on its continued use rather than first-time use (Bhattacherjee,( 2001).
The importance of continuance is clear, that is customer turnover will lead to acquiring new
customers that may cost as much as five times more than retaining existing ones (Bhattacherjee,
(2001).
The success of retaining customers will help organizations by reducing the cost of and
increasing availability of training. Present study desired to explore individuals‘ intentions to
continue using student portal system. De Lone & Mc Lean IS Success model were used to
measure student portal success and thus obtain an understanding of individuals‘ continuance
intention towards using portal system. From the perspective of continuance intention, previous
studies utilized numerous of determinants and IS model e:g Self-Determination Theory (Roca &
Gagne, (2008); Expectation-Confirmation Model (Bhattacherjee, (2001); Personal
Innovativeness (Shih-Wei Chou, (2009); UTAUT-Unified Theory of Acceptance and Use of
Technology (Chiu & Wang, (2008); Technology Acceptance Model-TAM (Wangpipatwong &
Wichian Chutimaskul, (2008); Terzis & Economides, (2011); Roca, Chiu, & Martı´nez, (2006);
user e-learning experience (Lin K.-M. , (2011); subjective norm (Lee, 2010); contribution
intention (He & Wei, (2007); habit (Limayem, Moez, Hirt, & Cheung, (2007).
Previous research has shown that satisfaction have a relationship between satisfaction and
continuance intention (Bhattacherjee, (2001; Yu-Hui Tao, (2009); Kang, Hong, & Lee, (2008);
Chen, Yen, & Hwang, (2012); Wen-Shan Lin, (2011); Shih-Wei Chou, (2009).Oliver (1980)
demonstrated that satisfaction will lead to intention to use. Consequently, we also argued that
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satisfaction influenced student portal system continuance intention through these variables.
Antecedent of the satisfaction was also included in this study as proven in previous study:
information quality (Chen & Cheng, (2009); system quality (Wang, 2008) and service quality
(Wang, (2008). Therefore, we considered De Lone & Mc Lean Success Model as a determinant
of continuance intention
THEORY AND LITERATURE REVIEW
Student portal system
The definition of portal is still not clearly defined (Masrek, (2007). However, in general, it is
defined as a single, personalized interface through which users access all information resources
and services in a secure, consistent and customizable manner (Masrek, (2007). The portal is
resource-based as the members can download and upload all kinds of information such as
documents, articles, websites, software, exercises, video upload, links to interesting events
(Pynooet et al.(2012). In the context of UCSA, the new implemented portal information system
helps students for registration purposes and timetable viewing. However since its inception, no
studies have been conducted to assess measures of the adoption of the portal
De Lone Mc Lean Success model
There are several measures of Information system success. De Lone & McLean (1992)
reviewed comprehensively the different information system success measures and proposed a
six-factor IS success model as a taxonomy and framework for measuring the complex dependent
variables in IS research (Wang, 2008). They are System Quality, Information Quality, IS Use,
User Satisfaction, Individual Impact and Organizational Impact. However, Delone & Mclean
(1992) did not provide an empirical validation of the model and suggested that further
development and validation was needed for their model as well as it was not well accepted by the
management IS community (Chen & Cheng, (2009) as it ignores the emergence of new
economic activities. Because of the criticisms suffered from other studies, Delone & McLean
(2003) proposed the updated version of the IS success model in 2003.
The objective of this new model was to update the old one and evaluate its usefulness in
light of dramatic change in information technology (IT) evolution, especially the emerging
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growth of e-commerce. The major alteration that has been made to the model was the addition of
a ‗service quality‘ construct and a partial division of the ‗use‘ construct into ‗intention to use‘
and ‗actual use‘. Delone & McLean (2003) also combined the individual and organizational
impacts of use into a single factor called ‗net benefits‘. Although the model has been revised, it
still needs further validation before it can serve as a basis for the selection of appropriate IS
measures (Wu & Wang, (2006). Wang (2008) extended the model to explain e-commerce
success in terms of reuse as a dependant variable. Therefore, it seems reasonable to assume that
De Lone & Mc Lean IS Success model can be used to study the continuance.
RESEARCH MODEL AND HYPOTHESES
The main focus of IS research is to know why and how individuals choose to adopt new
technologies (Schauppa & Lemuria Carter, (2010). Numerous studies ( Venkatesh, Morris, &
Davis (2003); Davis, (1989); Su-Chao Chang, (2008); Park, (2009); Abdulhameed Rakan
Alenezi, (2010); I-Fan Liu & Yeali Sun, (2010); Il Im & Kang, (2011); Boštjan Šumak, (2011);
Vachiraporn Khayun, (2011); Shu-ming Wang,( 2011) have shown that all those variables
involved in the studies affected individuals to adopt new technologies at initial stages but not at
eventually success. The study on long-term viability of an information system is crucial.
Therefore, the proposed model uses satisfaction, information quality, service quality and system
quality to explain students‘ continuance intention to use student portal system in UCSA (Fig. 1)
FIGURE 1 PROPOSED RESEARCH MODEL
Continuance
Intention Satisfaction
Information
Quality
System
Quality
Service
Quality
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Satisfaction
Satisfaction is defined as emotional reactions to the transaction of business (Oliver, (1980).
Satisfaction is considered as an important determinant of continuance intention (Bhattacherjee,
(2001) and critical to the survival of an organization (Kettinger & Sung-Hee ‗‗Sunny‘‘ Park,
(2009). If the customer has good experiences of using MIM (mobile instant message) over time,
then he will have cumulative customer satisfaction (Zhaohua Deng, (2010). Wang (2008)
reported that customers‘ satisfaction with e-commerce was significantly associated with their
continuance intentions. Therefore, the following hypothesis is proposed.
H1 Satisfaction is positively related to student‘s continuance intention to use UCSA‘s student
portal
Service Quality
Service quality has been widely studied since the early work of Zeithaml & Leonard L.
Berry (1996). Oliver (1980) argued that, service quality is a performance perception which
influences customer satisfaction through two mechanisms, directly via customer observation of
good or bad service quality and indirectly via an input to the disconfirmation comparison (i.e.
discrepancy between performance and expectation). Service quality was a late addition to the De
lone and McLean model (Trkman & Trkman, (2009). Parasuraman & Zeithaml (1985) proposed
that higher level of service quality result in increased customer satisfaction. In recognition of the
expanded role of the IS department and the importance of IS and e-commerce (EC), researchers
have begun to include service quality as a measure of IS satisfaction/success in recent years.
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Prior studies, Kettinger & Sung-Hee ‗‗Sunny‘‘ Park (2009) indicated that service quality was
significantly related to customer satisfaction. Therefore, the following hypothesis is proposed.
Therefore, we proposed
H2. Service quality is positively related to student‘s satisfaction with UCSA‘s student portal.
Information Quality
According to Gorla & Toni M. Somers (2010), information quality is a concept that is related to
the quality of information system output, can be described in terms of outputs that are useful for
business users. As stressed by Salaun & Flores (2001), good quality information is becoming a
necessary prerequisite for the setting-up of an active partnership between supplier and consumer
(in this case-the student portal system and the students). Delone & Mclean (1992) IS success
model suggests that higher level of information quality result in increased user satisfaction. Chen
C.-W. (2010) and Landrum, Prybutok, & Zhang (2010), indicated that information quality had a
significant effect on user satisfaction. Accordingly, the following hypothesis was proposed.
H3. Information Quality is positively related to student‘s satisfaction with UCSA‘s student
portal.
System Quality
The concept of system quality, first introduced by Delone & Mclean (1992), was defined as
quality manifested in a system‘s overall performance and measured by individuals‘ perceptions
(Delone & McLean, (3). Somers &Somers (2010) defined system quality as quality of
information processing itself, which is characterized by employment of state-of-the-art
technology, a system offering key functions and features (which is denoted as IS excellence, and
software) that is user friendly, easy to learn, and easily maintainable (which is denoted as IS
value). Cheung & Lee (2011) examined the users‘ satisfaction on e-learning portal. They found
that system quality affected overall satisfaction and was the best predictor of satisfaction. Prior
studies on IS success (Wang, (2008); Wu & Wang, (2006); Chen & Cheng, (2009) have also
provided support for the notion that system quality positively affected user satisfaction.
Accordingly, the following hypothesis was proposed.
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H4. System Quality is positively related to student‘s satisfaction with UCSA‘s student portal.
METHOD
Sample
The participants for current studies comprised 279 students from UCSA who use UCSA‘s
student portal for course registration. Of the 300 questionnaires distributed, 279 were completely
filled. Regarding gender, female samples were the majority of total samples; the percentage of
females was around 76%. For the semester currently studied, semester 4 is the majority of the
sample. Concerning course taken, 44.4% was from Nursing (UCSA) program. The rest are
Pharmacy 7.5%, Medical Lab Technology 2.5%, Art & Design (UiTM) 2%, Diploma in Science
(UiTM) 0.7%, Nursing UiTM 6.1%, Office Management UiTM 5%, BA Business (UPM) 1.1%,
Diploma in Business (UPM) 5.4%, Property management UTM 3.6%, Quantitative Surveying
(UTM) 11.5%, Architecture (UTM) 6.1% Computer Science UTM 1.8%, Medical Assistance
(UCSA) 1.8%
Instrument development
Our research model includes five constructs, each of which was measured with 28 items. All
items were obtained from previously validated instruments. After the questionnaire was
formulated, it was tested among several students. Based on their comments, some were revised
to improve the readability. Each item was measured with a seven point Likert scale, whose
answer choices range from ‗‗strongly disagree‖ (1) to ‗‗strongly agree‖ (7). Continuance
intention was measured with three items and adapted from Bhattacherjee (2001). While the
measures of satisfaction (eight items), information quality (six items), service quality (four
items) and system quality (seven items) were adapted from Delone & McLean (2003) and Chao-
Min Chiu & Chang (2007). All of the items used were modified to the context of student portal.
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Data Analysis
This study employs a two-step structural equation modeling (Anderson & Gerbing, (1988). It
performs confirmatory factor analysis (CFA) analysis on the items corresponding to the
constructs. The reason of adopting SEM for analyze the relationship between variables is due to
general theoretic of social science and behavioral science, which is usually constructed by some
unobservable or unmeasured variance (Pai & Tu, 2011)
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RESULTS
Psychometric properties measures
Psychometric properties measurement involves assessing internal consistency and construct
validity. The traditional criterion for assessing internal consistency is Cronbach alpha (Table 1)
while the recent one is uses composite reliability (Urbach, Smolnik, & Riempp, (2010). The CA
values in our model are exceeding recommended value of 0.5 (Fornell & Larcker, 1981) while
CR values indicated above the generally recommended minimum of 0.7 (Nunally & Bernstein,
(1994). All of the variables in this study were adapted from relevant literature thus exhibited
strong content validity.
Convergent validity was evaluated for the measurement scales using two criteria suggested by
(Fornell & Larcker, (1981): (1) all indicator factor loadings should be significant and exceed
0.70 and (2) average variance extracted (AVE) for each construct should exceed the variance due
to measurement error for that construct (i.e., should exceed 0.50). As shown in Table 1, most
items exhibited loading higher than 0.7 on their respective constructs, providing evidence of
acceptable item convergence on the intended constructs. Therefore, all conditions for convergent
validity were met.
TABLE 1 RESULT OF CONVERGENT AND RELIABILITY TEST
Construct items Std. loading Composite reliability AVE
Cronbach
Alpha
SATISFACTION 0.89 0.73 0.89
Satis4 0.80
Satis5 0.88
Satis6 0.88
SYSTEM 0.88 0.65 0.88
System 3 0.80
System 6 0.83
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System 5 0.80
System 4 0.80
INFORMATION
IQ 4 0.86 0.9 0.7 0.9
IQ 3 0.84
IQ 2 0.84
IQ 1 0.81
SERVICE
SQ1 0.79 0.88 0.7 0.87
SQ2 0.86
SQ3 0.86
CONTI
Continuance 1 0.86 0.92 0.79 0.91
Continuance2 0.94
Continuance3 0.86
Discriminant validity assesses the extent to which a concept and its indicators differ from another
concept and its indicators (Bagozzi, Yi, & Phillips, (1991). The discriminant validity of items
and variables were examined using factor and correlation analyses. As we can see from the factor
analysis in (Table 2), all items, had cross loading coefficients that are at least 0.10 lower than the
factor loading on their respective assigned latent variables (Gefen & Straub, (2005). Overall, the
measurement model demonstrated adequate reliability, convergent validity and discriminant
validity.
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TABLE 2 ITEM-CONSTRUCT CORRELATION
Evaluation of the measurement model
The measurement model for the construct is measured using confirmatory factor analysis. This
procedure is done using AMOS 18. To demonstrate a reasonable fit for the model, a number of
fit indices were computed including Chi-square/degrees of freedom, Goodness-of-fit index
(GFI),Adjusted Goodness-of-fit Index (AGFI , Adjusted Goodness-of-fit Index (AGFI),
Comparative Fit Index (CFI), and Root Mean Square of Approximation(RMSEA). A very good
fit is normally deemed to exist when GFI and CFI are greater than 0.90, Root Mean Square of
ITEMS SYSTEM INFORMATION SERVICE SATIS CONTI
Systm3 0.80 0.65 0.66 0.59 0.49
Systm6 0.83 0.67 0.68 0.62 0.51
Systm5 0.80 0.65 0.66 0.59 0.49
Systm4 0.80 0.65 0.66 0.60 0.49
IQ4 0.70 0.86 0.64 0.67 0.52
IQ3 0.68 0.84 0.62 0.66 0.51
IQ2 0.69 0.84 0.63 0.66 0.51
IQ1 0.65 0.81 0.60 0.63 0.49
SQ1 0.65 0.59 0.79 0.55 0.43
SQ2 0.71 0.64 0.86 0.60 0.47
SQ3 0.71 0.64 0.86 0.60 0.47
satis4 0.59 0.62 0.55 0.80 0.53
satis5 0.66 0.69 0.61 0.88 0.59
satis6 0.66 0.69 0.61 0.88 0.59
Continuance1 0.53 0.52 0.46 0.58 0.86
Continuance2 0.58 0.57 0.51 0.63 0.94
Continuance3 0.53 0.52 0.46 0.57 0.86
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Approximation (RMSEA) is around 0.10 (Hair & Black , (2006), and AGFI is greater than 0.80.
The chi-square was not used because it is sensitive to sample size. Thus, the use of relative (chi-
square/df) seemed appropriate; it is assumed that value less than 3 is indicative of an acceptable
fit (Bagozzi & Yi, (1988).
The indices for the measurement model 1 with all 28 items showed that the data did not fit well
(see Table 3). Some of the indices, such as GFI (0.79), and AGFI (0.75) were below acceptable
levels. Therefore, the measurement model was reevaluated. Anderson & Gerbing (1988)
suggested four methods to improve model fit: (1) relate the indicator to a different factor, (2)
delete the indicator from the model, (3) relate the indicator to a multiple factor, or (4) use
correlated measurement error. The researchers stated that the first two methods are preferred
because they preserve unidimensional measurement (Cho, Johanson, & Guchait, (2009), whereas
the second two methods do not. Therefore, we chose to delete the indicators instead of relating
them to a different factor because we could not find a theoretical support for the approach. This
process resulted in the deletion of 10 items to improve the model fit, A respecification of Model
1 without these items was necessary to improve it. In the deleting procedure, each item must be
deleted one at a time (Kim, (2008) and Model 1 was reevaluated. In order to make sure that
deleting those items did not worsen the reliability and validity of the constructs, we conducted a
composite reliability and validity test for the first measurement model (before deleting the items)
and the modified measurement model (after deleting the items).
Table 3 shows the results of the composite reliabilities and validity for the two models. The
composite reliabilities were satisfactory for both models which exceeding the minimum criterion,
.50 (Fornell & Larcker, (1981). After discarding those items, the measurement model, Model 2,
was reevaluated; its indices indicated a good fit which is chi-square/degree of freedom = 2.24,
GFI = .91 and AGFI = .873.
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TABLE 3: RESULT OF COMPOSITE RELIABILITY AND VALIDITY
CONSTRUCT Number of items
Composite reliability
convergent validity
(AVE)
1st model 2nd model
1st model 2nd model
1st model 2nd model
SYSTEM 7 4
0.91 0.89
0.60 0.65
INFORMATION 6 4
0.92 0.90
0.64 0.70
SERVICE 4 3
0.90 0.88
0.68 0.70
SATIS 8 3
0.94 0.89
0.67 0.73
CONTI 3 3
0.92 0.92
0.79 0.79
Evaluation of the structural model
For the purpose of examining the structural model, we use a similar set of model-fit indices.
(Table 4) shows the estimation from the structural modeling.
TABLE 4 SUMMARY OF THE OVERALL FIT INDICES FOR MEASUREMENT MODEL 1
AND 2
Model Chi-Square/df GFI AGFI TLI/NNFI CFI RMSEA
Measurement model 1 2.85 0.79 0.75 0.90 0.91 0.08
Measurement model 2 2.24 0.91 0.87 0.96 0.96 0.07
Structural model 2.26 0.91 0.87 0.95 0.96 0.07
Suggested value ≤3 >0.95 ≥0.80 ≥0.90 ≥0.90 <0.08
The overall fit of model is satisfactory, with all of the relevant goodness of fit indices greater
than 0.90. The GFI is 0.91 (Bagozzi & Yi, (1988), the AGFI is 0.87, which is above the
acceptable level of 0.8 (Etezadi-Amoli & Farhoomand, 1996) and the TLI/NNFI is 0.95, which is
considered to be acceptable as recommended by (Hair et al. 2010) and RMSEA showed a very
265
satisfactory level of 0.07 (Hair et al., 2010). Another statistic test to assess model fit is normed
chi-square value (a chi-square divided by degrees of freedom). Our model shows satisfactory
level of 2.26, a value that is appropriately below the benchmark of three, to indicate good overall
model performance (Shin, (2009; Bagozzi & Yi, (1988) . Hence we conclude that our model
demonstrates good model ft.
Hypotheses testing
The four hypotheses presented above were tested collectively using the structural equation
modeling (SEM) approach and performed using AMOS 18. The path significance of each
hypothesized association in the research model and variance explained (R2 value) by each path
were examined. Table 5 shows the standardized path coefficients and path significances. Three
hypothesized associations were strongly significant at p < 0.05, except for the links between
service quality and continuance intention. The continuance intention to use student portal in this
study was predicted by satisfaction (b = 0.79, < 0.01), While satisfaction was jointly predicted by
information quality (b = 0.72, < 0.01) and system quality (b = 0.40, p < 0.05). Service quality did
not significantly influence satisfaction. These variables together explained 67% of the variance
of satisfaction (R2= 0.67, coefficient of determination). In addition satisfaction explained 48% of
the variance of continuance intention (R2= 0.48, coefficient of determination). (Fig.2)
TABLE 5: THE RESULT OF TESTED HYPOTHESES
Hypothesis Effects
Path
coefficient C.R Result
H1
Satisfaction → student‘s continuance
intention 0.79
10.9** Supported
H2 Service quality → student‘s satisfaction 0.17
1.286 Not supported
H3 Information Quality → student‘s satisfaction 0.72
5.209** Supported
H3 System Quality → student‘s satisfaction 0.4
2.266* Supported *values are critical ratios exceeding 1.96, at the 0.05 level of significance
**values are critical ratios exceeding 2.32, at the 0.01 level of significance
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FIGURE.2. STRUCTURAL MODEL
DISCUSSION
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This study attempts to examine the validity of modified De Lone & Mc Lean (Delone &
McLean, (2003) in educational context and continuance intention as dependent variable. The
study‘s research question is focused on empirically validating service quality, information
quality, systems quality on student satisfaction while continuance intention affected by
satisfaction.
Implication for research
This study empirically validates De Lone & Mc Lean Success Model in the context of education.
Research on De Lone & Mc Lean Success Model on continuance intention is still in early stages.
This study introduces De Lone & Mc Lean Success Model as a predictor of student‘s
continuance intention to use UCSA student portal. De Lone & Mc Lean Success Model helps to
explain why students are willing to continue to use student portal. The proposed model can serve
as a spring board for future research in the context of education and continuance as a dependant
variable. Future research should look to investigate the significance of demographic difference
(e.g., age, gender, culture).
Implication for practice
The implications of this study are twofold. There are several implications for the organization
(UCSA) and students. The study highlights the fact that students continuance intention to use
UCSA portal is significantly influenced by their satisfaction towards the system which they are
using to register their subjects and view timetable. Satisfaction has shown to explain students‘
satisfaction toward the system. This highlights to the organization (UCSA) the fact that
satisfaction towards the system which are information quality, system quality and service quality
is absolutely critical.
As suggested by Bhattacherjee (2001), IS continuance intention is determined primarily by their
satisfaction with prior IS use. It‘s essential for organization to ensure that each student is
satisfied with the system provided by the organization. When satisfaction is not met, continuance
intention is impossible because the whole system has lost its credibility from the eyes of the
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individual user. Prior research has shown that satisfaction plays a significant role on continuance
intention (Bhattacherjee, (2001; Yu-Hui Tao, (2009); Kang, Hong, & Lee, (2008); Chen, Yen, &
Hwang, (2012); Wen-Shan Lin, (2011); Shih-Wei Chou,( 2009). Thus, satisfaction is vital to
ensure the retention of the students in continuing their intention to use the portal. However, in
order to ensure the students are satisfied, the system provided shall meet the reasonable quality.
By revealing the factors affecting satisfaction, the present study contributes to organization by
the fact that providing the IT infrastructure that meets the needs of the students is vital. Factors
that affect customer satisfaction have been proven through this study.
The relationship between information quality and satisfaction was found significant, consistent
with previous studies (Wang, (2008); Lin, (2007). System quality was also found to be
significant. This is also consistent with prior studies (e.g., Chen & Cheng, 2009; Lin, 2007).
Organization should maintain the quality system and quality of information in the student portal
system. However, service quality, need to be improved as the relationship between service
quality and satisfaction was found not to be significant. This is contrary to Lin (2007) and Chen
& Cheng (2009) who found that service quality affected user‘s satisfaction. However, this
finding is consistent with Chao- Min Chiu & Chang (2007). A possible explanation for the
insignificant relationship could be that the service provided in the context of student portal is still
inadequate. This may be due to a lack of staff experienced in dealing with system. Therefore, the
organization must provide adequate training to the IT staff members.
Limitations
There are some limitations to this study that should be noted. First, the results are not
generalizable because current study examined only one student portal system. Second, the data
are cross-sectional while individual‘s intention to use student portal system is continuous
process. Third, although the model explains 48% of the variance in continuance intention and
67% of the variance in satisfaction, it does not include several others construct existed in the
literature. Further studies could include educational compatibilities and technological expectancy
(Chen , (2011) on continuance intention
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CONCLUSION
Meeting customer expectations of IS quality is accomplished by offering appealing, user-friendly
interfaces, entertaining user requests for changes, and satisfying the stakeholders of the IS
(Somers & Somers, (2010). The above statement shows the importance of protecting the quality
of the IS system. As has been proven in this study, satisfaction of the students towards portal
system is the key to the success of the implementation of student portal system. In order to gain
students satisfaction, factors that lead to students‘ satisfaction must be well explored.
Particularly, to satisfy student expectancy on service quality, organization should focus on
enhancing staff abilities in dealing with the portal system by providing better enhancement
courses. Student can therefore receive better service quality.
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FACTORS INFLUENCING CONTINUANCE INTENTION TO USE STUDENT
PORTAL AMONG UNIVERSITY COLLEGE SHAHPUTRA’S STUDENTS
Saripah Basar
Research and Innovation Department
University College Shahputra, Malaysia
ABSTRACT
With the advent of information system technology, University College ShahPutra (UCSA) is
putting in effort to introduce a student portal system for use by students. However, continued
usage is considered as a measurement of success in information system implementation. The
objective of this study is to propose an integrated research framework that investigates the
factors that can motivate students to continue utilizing the UCSA student portal system. Four
streams of research provide the basis for this integrated framework namely Unified theory of
acceptance and use of technology (UTAUT) and Self-Determination theory. 279 students from
UCSA (University College ShahPutra) responded to 20-item questionnaires containing 6
constructs: continuance intention to use, performance expectancy, effort expectancy, social
influence, facilitating condition and intrinsic motivation. Covariance-based SEM was employed
as the main method of analysis in this study. Results revealed that the performance expectancy
and intrinsic motivation do not have any statistically significant effect on continuance intention
to use the UCSA student portal. However, effort expectancy, social influence and facilitating
conditions were shown to significantly influence continuance intention. The model explained 53
% of variance of student portal continuance intention.
INTRODUCTION
The latest internet technology in education such as a learning management system, student portal
and a virtual learning system has brought e-learning to the new era. With the development of
ICT, students are able to capitalize on the strengths of ICT for the purpose of learning activities.
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There are a variety of e-learning applications including student portals that have been integrated
in many university programs (Selim, 2007). This study will focus on the use of the student portal
as one of the e-learning applications. There are many advantages in using e-learning, including
helping organizations by reducing the cost of and increasing availability of training. However,
the development and maintenance of such technology is expensive and time-consuming
(Bringula & Basa, 2011) If the technology is not used optimally, then it would cause waste
(Chen , 2011; Chiu & Wang, 2008). Therefore, the study on the continuance intention to use the
portal is vital. The success depends mainly on students‘ loyalty, i.e., continued use. The
importance of continuance is obvious where customer turnover can be costly as the cost of
acquiring new customers is higher than that of retaining existing ones (Chiu & Wang, 2008).
Two models were used to assess the technological and motivation issues and thus obtain an
understanding of individuals‘ actions: The Unified Theory of Acceptance and Use of Technology
and Self-Determination Theory. As Schauppa & Lemuria Carter, (2010) said, a researcher can
gain more comprehensive understanding of an adoption by integrating models. This study
outlines only one component of self-determination theory which is intrinsic motivation. Yi-Shun
Wang & Liao, (2010); Chen & Jang, (2010); Lee & Chen, (2005); Liu, Han, & Li, (2010), all
found that on-line learning e.g. a portal, web-based and m-learning, can be explained by self-
determination theory. Consequently, we also argue that self-determination theory influences
student portal continuance intention through this variable. According to Bostjan Sumak &
Hericko (2010), Islam (2011), Jong (2009), I Gusti Nyoman Sedana (2010), and Chen (2011),
e-learning acceptance and usage can be explained by the Unified Theory of Acceptance and Use
of Technology (UTAUT), which is a parsimonious and robust model of individual acceptance of
new IT. While it initially focuses on user acceptance and usage of IT in the workplace, it has
recently been used in understanding the acceptance and use of e-learning, tablet personal
computer, e-mail and student portals (Mohamed Yamin, (2010); El-Gayar, (2006); Ismail,
(2009); Chen, Wu, & Yang, (2008) . Therefore, we consider the major UTAUT constructs in
determining continuance intention: performance expectancy, effort expectancy, social influence,
and facilitating conditions.
THEORY AND LITERATURE REVIEW
Student Portal System
A student portal system is a portal website developed for colleges or institutes which provides
facility to their students & faculties for creating & maintaining their own web pages (profiles)
which can be viewed online by anyone who visits the website (Santronix Computers, 2012). The
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features-rich portal system offers a variety of facilities for students, faculties and administrator
such as uploading of self photograph, images(jpeg file) and articles(doc file) with which
everyone can enjoy the thrill of the world wide web and experience the feel of globalizing their
identity (Santronix Computers, 2012). This helps institutes in achieving the best communication
levels and avail global exposure to their students (Santronix Computers, 2012). The new
implemented portal information system in ShahPutra University College (UCSA) will help
students in registration purposes and timetable viewing. However since its inception, no studies
had been conducted to assess to what extent the adoption of the portal is regarded as successful..
Students have shown some reluctance to use the system, and eventually it was not used as hoped.
This has led to questions about the factors that influence the student use of the portal system.
UNIFIED THEORY OF ACCEPTANCE AND USE
The need to know how and why individuals adopt new technologies is the most important thing
in IS research (Schauppa & Lemuria Carter, 2010). Within the wide area of IS research, there is a
literature that only focuses on intention to use. One of the latest models that is focusing on
intention is the Unified Theory of Acceptance and Use of Technology (UTAUT) which
synthesizes elements across eight well known technology acceptance models: the Theory of
Reasoned Action (TRA), the Technology Acceptance Model (TAM), the Motivational Model
(MM), the Theory of Planned Behavior (TPB), the combined TAM and TPB, the Model of PC
Utilization (MPTU), the Innovation Diffusion Theory (IDT) and the Social Cognitive Theory
(SCT). The objective of the UTAUT is to achieve a unified view of user acceptance (Venkatesh
et al.., 2003). The resulting unified model consists of four core components or determinants of
intention and usage. The model is claimed to be a useful tool for managers to assess the
likelihood of acceptance of a new technology within an organization. It also helps in
understanding factors that drive acceptance of a new technology, so that appropriate features can
be designed to facilitate acceptance of a new technology by users.
Self-Determination Theory
SDT proposes two overarching types of motivation. Intrinsic motivation refers to doing an
activity for its own sake, because one enjoys the process (Ryan & Deci, 2000). Extrinsic
motivation refers to doing an activity for a consequence separate from the activity itself, such as
the pursuit of a reward or the avoidance of a punishment (Ryan & Deci, 2000). Numerous IS
researchers have urged the need to include intrinsic motivation to explain IT adoption and usage
(Matthew Lee & Chen, 2005). Therefore the present study extended UTAUT by including
intrinsic motivation as a postulated predictor of continuance intention. Intrinsic motivation is
also identified as a factor influencing continuance intention (Chiu & Wang, 2008). The proposed
model integrated intrinsic motivation and UTAUT variable to explain UCSA‘s students‘
continuance intention to use student portal.
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RESEARCH MODEL AND HYPOTHESES
The student portal is an emerging application of the WWW and is different from IS used in the
workplace. Existing variables of UTAUT do not explained learners‘ motives. Roca & Gagne
(2008) argued that intrinsic motivation will predict engagement in interesting activities. Intrinsic
motivation plays such a central role, providing a basis for understanding human behavior in and
across culture (Chiu & Wang, 2008). Therefore we extended UTAUT by adding intrinsic
motivation into the model. In our study, the dependent variable was student portal continuance
intention to use, which refers to the subjective probability that an individual would continue
using the student portal. According to Venkatesh, Morris, & Davis (2003), constructs theorized
not to be direct determinants for intention are attitudes, self-efficacy, and anxiety. The intrinsic
value component of our Self-determination theory was measured in the same manner as Davis
(1992) and Chiu & Wang (2008). Figure 1 shows our model; in addition to the four core
constructs of UTAUT, intrinsic motivation is assumed to affect students‘ intentions to continue
using UCSA student portal.
FIGURE 1: RESEARCH MODEL FOR STUDENT PORTAL CONTINUANCE
INTENTION
Continuance
Intention
EE
PE
FC
SI IV
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PERFORMANCE EXPECTANCY
Performance expectancy is the extent to which a person believes that a system enhances his or
her performance (Venkatesh, Morris, & Davis, 2003). Literature has shown that there are
similarities between performance expectancy and perceived usefulness (Venkatesh, Morris, &
Davis, 2003) Performance expectancy has been found to be the strongest predictor of intention in
previous model tests (Venkatesh, Morris, & Davis, 2003; Ritu Agarwal, 1999; Venkatesh, 2000).
(Venkatesh et al. 2003) found that performance expectancy is a strong predictor of an
individual‘s intention to use a new technology in the workplace. (Ismail, 2009; Chen, Wu, &
Yang, 2008) provided empirical support for the relationship between perceived usefulness and
behavioral intention in the context of e-learning and student portal. Accordingly, the following
hypothesis was proposed.
H1. Performance expectancy is positively related to UCSA‘s student portal continuance
intention.
EFFORT EXPECTANCY
Effort expectancy is to the extent to which a learner believes that using a system is free of effort
(Venkatesh, Morris, & Davis, 2003). Effort expectancy pertains to perceived ease of use in
TAM, which assumes that a system perceived to be easier to use is more likely to induce
perception of usefulness and behavioral intention (Chiu & Wang, 2008). (Ismail, 2009; Islam,
2011; I Gusti Nyoman Sedana, 2010) indicated that effort expectancy is positively associated
with intention to use in the context of e-learning and student portal. Therefore, we proposed.
H2. Effort expectancy is positively related to UCSA‘s student portal continuance intention.
Social influence
Social influence is to the degree to which an individual perceives that important others believe he
or she should use a technology (Venkatesh, et al. 2003). The concept is similar to subjective
norm in the theory of planned behavior (TPB) which argues that the more favorable the social
influence of a behavior, the stronger would be an individual‘s intention to perform it (Chiu &
Wang, 2008). According to innovation diffusion theory (Rogers, 1995), users tend to interact
with each other to interpret their IT adoption. Such increased interactions can influence adoption
decision. Studies from Schauppa & Lemuria Carter, (2010); I Gusti Nyoman Sedana, (2010);
Venkatesh, Morris, & Davis, (2003); and Dasgupta, Haddad, Weiss, & Bermudez, (2007)
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showed that social influence is a significant predictor of intention to use a system. Accordingly,
the following hypothesis was proposed.
H3. Social influence is positively related to UCSA‘s student portal continuance intention.
FACILITATING CONDITION
Factors and resources that an individual believes exist to support his or her activities are termed
facilitating conditions (Venkatesh, Morris, & Davis, 2003). In our study, they included both
technical and non-technical support. Facilitating condition consists of three root construct:
perceived behavioral control, facilitating condition and compatibility (Venkatesh, Morris, &
Davis, 2003). In their studies, Gusti Nyoman Sedana, (2010); Dasgupta et al. (2007) found that
facilitating condition is positively and significantly related on intention to use a system.
Accordingly, the following hypothesis was proposed.
H4. Facilitating conditions is positively related to UCSA‘s student portal continuance intention.
Intrinsic motivation
Intrinsic motivation is the extent to which an activity is perceived to be personally pleasing (Chiu
& Wang, 2008). It is also defined as the extent to which the activity of using the computer is
perceived to be enjoyable in its own right, apart from any performance consequences that may be
anticipated Lee & Chen, (2005) and according to self-determination theory, learners are self-
determining and intrinsically motivated in using student portal when they are interested in or
enjoying doing it. Chiu & Wang, (2008) found that individuals who were intrinsically interested
in using a system will continue using it. Therefore, the following hypothesis was postulated.
H5. Intrinsic motivation is positively related to UCSA‘s student portal continuance intention.
METHOD
Participants and procedures
The participants for the current study comprised of 279 students from UCSA who use the
UCSA‘s student portal for course registration. Of the 300 questionnaires distributed, 279 were
completely filled. Regarding gender, female samples were the majority of the total samples; the
percentage of females was approximately 76%. For the semester currently studied, semester 4 is
the majority of the sample. Concerning course taken, 44.4% were from the Nursing (UCSA)
program. The rest were from Pharmacy 7.5%, Medical Laboratory Technology 2.5%, Art &
Design (UiTM) 2%, Diploma in Science (UiTM) 0.7%, Nursing UiTM 6.1%, Office
Management UiTM 5%, BA Business (UPM) 1.1%, Diploma in Business (UPM) 5.4%, Property
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Management UTM 3.6%, Quantitative Surveying (UTM) 11.5%, Architecture (UTM) 6.1%
Computer Science UTM 1.8%, and Medical Assistance (UCSA) 1.8%
Measures
Current studies used previously validated scales to measure all the constructs in the model.
Performance expectancy and effort expectancy were measured with four items and adapted from
Venkatesh et al. (2003), while items for social influence and facilitating condition were
measured with three items and each that was also adapted from Venkatesh et al., (2003). Intrinsic
motivation were measured with three items and adapted from Chiu & Wang, (2008).
Continuance intention was measured with three items and adapted from Bhattacherjee, (2001)
All of the items used were modified to the context of the student portal.
DATA ANALYSIS
The research model was tested using covariance-based SEM techniques using AMOS 18.
SEM is Structural equation modeling (SEM), a type of statistic method, which is normally used
to examine the accuracy of constructive relationships, and exploring relationships between the
observable variance and potential variance, as well as defining the interactive relationship
between each other. The reason for adopting SEM to analyze the relationship between variables
is due to the general theoretic of social science and behavioral science, which is usually
constructed by some unobservable or unmeasured variance (Pai & Tu, 2011)
RESULTS
Evaluation of measurement model
Prior to conducting path analysis for the overall research model, it is important to determine how
to evaluate potential variance, due to the fact that only potential variance can be effectively
evaluated. Statistics can precisely predict the path coefficient in the evaluation model. The
confirmatory analysis is the examining action in evaluating the numbers. In this research, both
UTAUT and Intrinsic motivation (Self-Determination Theory) were used in conducting
confirmatory factor analysis (CFA). This is to examine and test whether accuracy and fitness
evaluation of the variance get firm support from the theory. CFA involves specification and
estimation of one or more hypothesized factor structure(s), each of which proposes a set of latent
variables to account for covariance among a set of observed variables. If CFA does not get an
appropriate fit, then by deleting inappropriate questions or by amending the modification indices
(M.I.), one can enhance the fit level (Pai & Tu, 2011) The measurement model was assessed
using confirmatory factor analysis (CFA). This was conducted with AMOS 18 using the
maximum likelihood estimation (MLE) procedure. There was an acceptable level of model fit for
the measurement model as suggested by Hu & Bentler, (1999) and Kline, (2011). The ratio of χ2
to degrees-of freedom (df), should not exceed 3, adjusted to the goodness of fit index (AGFI)
which should exceed 0.8, non- normed fit index (NNFI) and comparative fit index (CFI) should
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exceed 0.9, and the root mean square error of approximation (RMSEA) should not exceed 0.08.
For our CFA model, value of 430.605 with 1 degree of freedom and a probability value of
less than 0.05.A significant p-value indicates the absolute fit of the model is less than desirable.
However, the test of absolute model fit is sensitive to sample size and non-normality
(Schauppa & Lemuria Carter, 2010). A better measure of fit is chi-square over degrees of
freedom / df) (Schauppa & Lemuria Carter, 2010). This ratio for the proposed model in this
study is 2.778, which is within the suggested value while AGFI is 0.823, NNFI is 0.925, CFI is
0.908 and RAMSEA is 0.080. As shown in Table 1, all the model-fit indices exceed their
respective common acceptance levels suggested by previous research, thus demonstrating that
the measurement model exhibited a fairly good fit with the data collected. After examining the
model fit, we could therefore proceed to evaluating the psychometric properties of the
measurement model in terms of internal consistency reliability, convergent validity, and
discriminant validity. The reliability of the survey instrument was established by calculating
Cronbach‘s alpha for the purpose of measuring internal consistency. Most of the scores are
above the acceptable level, that is, above 0.70 as suggested by Schmitt, (1996) while convergent
validity of the factors is estimated by composite reliability and average variance extracted (see
Table 2). Composite reliability for all the factors in the measurement model is above 0.7 as
suggested by Segars (1997). The average extracted variances are all above the recommended
0.50 level (Fornell & Larcker, 1981). Convergent validity can also be evaluated by examining
the factor loadings from the confirmatory factor analysis (Table 2). Following the
recommendation made by Hair et al. (2006),a factor loading greater than 0.50 is considered to be
very significant. All of the factor loadings of the items in the measurement model are greater
than 0.50. Thus, all factors in the measurement model have adequate reliability and convergent
validity. To examine discriminant validity, this study compares the shared variance between
factors with the average variance extracted of the individual factors (Fornell & Larcker, 1981)
This analysis shows that the shared variances between factors are lower than the average
variance extracted of the individual factors, thus confirming discriminant validity (see Table 3).
The item-construct correlation by Anderson & Gerbin (1988) also can be used to examine
discriminant validity. As we can see from table 4 the correlation pattern shows that an item
posited to form a given sub-construct has a stronger correlation with the intended construct than
another construct. Hence this indicates there is appropriate discriminant validity. In summary,
the measurement model demonstrated adequate reliability, convergent validity, and discriminant
validity.
TABLE 1: FIT INDICES FOR MEASUREMENT AND STRUCTURAL MODELS Model fit indices
Measurement Structural
Recommended value
Chi-Square statistics / df 2.778 2.778 ≤3
AGFI .823 .823 ≥0.80
CFI .908 .908 ≥0.90
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NNFI/TLI .925 .925 ≥0.90
RMSEA .080 .080 <0.08
TABLE 2: ANALYSIS OF MEASUREMENT MODEL
Construct items Std. loading
Composite
reliability AVE
Cronbach
Alpha
Performance expectancy 0.9 0.68 0.89
Pe4 0.77
Pe3 0.84
Pe2 0.85
Pe1 0.83
Effort expectancy 0.85 0.6 0.85
Ee4 0.81
Ee3 0.77
Ee2 0.75
Ee1 0.74
Social influence 0.82 0.61 0.79
SI3 0.55
SI2 0.90
SI1 0.86
facilitating condition 0.75 0.51 0.74
Fc3 0.81
Fc2 0.68
Fc1 0.64
Intrinsic motivation 0.88 0.7 0.87
Iv3 0.84
Iv2 0.90
Iv1 0.77
Continuance intention 0.92 0.78 0.91
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Continuance3 0.86
Continuance2 0.94
Continuance1 0.86
TABLE 3: DISCRIMINANT VALIDITY FOR THE MEASUREMENT
MODEL
IV FC SI EE PE CONT
INTRINSIC 0.83
FACILITATING 0.61 0.71
SOCIAL 0.65 0.53 0.79
EFFORT 0.55 0.77 0.44 0.76
PERFORMANCE 0.75 0.55 0.69 0.60 0.82
CONT 0.52 0.70 0.49 0.64 0.46 0.88
Diagonal in bold : square root of AVE and off diagonal : correlation between construct
TABLE 4: ITEM –CONSTRUCT CORRELATION (DISRIMINANT VALIDITY)
ITEMS PE EE SI FC INTR CONT
Pe1 0.83 0.50 0.57 0.46 0.63 0.39
Pe2 0.85 0.51 0.59 0.47 0.64 0.40
Pe3 0.84 0.50 0.58 0.46 0.65 0.39
Pe4 0.77 0.46 0.54 0.43 0.58 0.36
Ee1 0.44 0.74 0.33 0.57 0.41 0.47
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Ee2 0.45 0.75 0.33 0.58 0.41 0.48
Ee3 0.46 0.77 0.34 0.59 0.42 0.49
Ee4 0.48 0.88 0.36 0.63 0.44 0.52
Si1 0.59 0.38 0.86 0.45 0.55 0.42
Si2 0.62 0.37 0.90 0.47 0.58 0.44
Si3 0.38 0.24 0.55 0.29 0.35 0.27
Fc1 0.35 0.49 0.33 0.64 0.39 0.44
Fc2 0.38 0.53 0.36 0.68 0.42 0.47
Fc3 0.45 0.62 0.43 0.81 0.49 0.56
Intr1 0.58 0.42 0.50 0.47 0.77 0.40
Intr2 0.68 0.49 0.58 0.55 0.90 0.47
Intr3 0.63 0.46 0.54 0.51 0.84 0.47
Continuance 1 0.40 0.55 0.43 0.60 0.45 0.86
Continuance 2 0.44 0.60 0.46 0.65 0.49 0.94
Continuance3 0.40 0.55 0.42 0.60 0.45 0.86
EVALUATION OF THE STRUCTURAL MODEL AND HYPOTHESIS TESTING
A similar set of model-fit indices is used to examine the structural model (see Table 1). A
comparison of all fit indices with their corresponding recommended values provided evidence of
a good model fit. Thus, we could proceed to investigate the predictors in the continuance
intention of UCSA student portal. Having established the adequacy of the model‘s fit, it is
appropriate to examine individual path coefficients. This analysis is presented in Table 5. Three
of the five hypotheses are supported. Effort expectancy (H2), social influence (H3) and
facilitating conditions (H4) increased continuance intentions. . However, H4 is found to be
positive and significantly related on continuance intention. Two hypotheses are not supported:
Performance expectancy did not significantly influence students‘ continuance intentions to use
the UCSA student portal (H1) and intrinsic motivation did not give any impact to continuance
intentions to use the UCSA student portal (H5). The R2 value shows that performance
expectancy, effort expectancy, social influence, facilitating conditions and intrinsic motivation
together account for 53% of the variance of student portal continuance intention.
DISCUSSION
The present study found support for three out of five hypotheses in our proposed model. The
obtained results suggest that the variables from the UTAUT and Self-Determination theory are
important in explaining students‘ continuance intention to use the student portal. The main
theoretical implication of the present study is that an integration of UTAUT and Self-
Determination theory, Effort expectancy, social influence and facilitating condition increased
286
continuance intentions. The relationship between Effort expectancy and social influence on
continuance intention are the same result derived from the original UTAUT (Venkatesh et al.
2003). Facilitating condition is found to be positive and significantly related on continuance
intention and it is contrary to the previous studies (e.g. Venkatesh et al.. (2003), Chiu & Wang,
(2008), Schauppa & Lemuria Carter, (2010). It is possible that because of the current research
being conducted in an academic setting, while the original facilitating condition was carried out
in an organizational setting Gusti Nyoman Sedana (2010) that performance expectancy did not
significantly influence students‘ continuance intentions to use the UCSA student portal. This is
contrary to the research of Chiu & Wang, (2008) who found that performance expectancy
significantly influenced continuance intention to use web-based learning. We found that the
UCSA student portal only has limited applications.
IMPLICATIONS FOR RESEARCH
This study develops and empirically validates a research model that extends UTAUT by
integrating intrinsic motivation in an academic setting. Research on the effects of performance
expectancy, effort expectancy, social influence, facilitating condition and intrinsic motivation on
e-learning especially in student portal is still not well explored. This study introduces an
integrated model consisting of an UTAUT variable and a Self-Determination variable as a salient
predictors of student portal continuance intention. This construct helps to explain why students
are willing to continue using the student portal.
TABLE 5: HYPOTHESES TESTING RESULT Hypotheses Path Path
coefficient
t-value Result
H1 Performance expectancy →continuance
intention
-0.12 -1.147 Not
supported
H2 Effort expectancy→ continuance intention 0.26 2.334** Supported
H3 Social influence → continuance intention. 0.18 2.170** Supported
H4 Facilitating conditions →continuance
intention
0.40 3.234** Supported
H5 Intrinsic motivation → continuance
intention
0.11 1.144 Not
supported
Path coefficient *significant at p<.05;** significant at p<.01;*** significant at p<.001
287
IMPLICATIONS FOR PRACTICE
The applications in the UCSA student portal are only the registration form and class time table
viewing. This may explain why performance expectancy is not positive and significantly related
on continuance intention to use the student portal. Therefore, the organization (UCSA) should
add more applications into the portal in order to increase performance expectancy towards it. Our
finding that intrinsic motivation did not affect the continuance intentions to use the UCSA
student portal is inconsistent with several other comparable studies (Lee & Chen, (2005); Roca
& Gagne, (2008); Øystein Sørebø & Vebjørn Flaata Gulli, (2009). Our explanation for this is
based on assuming that the design and the implementation of the portal needs to be improved. As
proposed by Lee & Chen (2005), one should use a creative approach and design to improve the
student portal such as making full use of the rich multimedia capability of the internet to create
images, sounds and text in order to facilitate student understanding and enjoyment when using
the portal, hence cultivating hedonic pleasure when using the portal (Liua & Arnett, 2000).
LIMITATIONS
There are limitations in this study. Although the present model explains 53% of the variance in
continuance intention, it does not include several continuance intention constructs explored in
the literature. There is also the possibility of the emergence of common method bias due to
measuring users‘ subjective psychological variables ( Liu & Sun, 2010). Therefore, future
research could improve the questionnaire design by mixing the order of the question or using
different types of scales (Chang, Witteloostuijn, & Eden (2010). Finally, only one variable of
Self-Determination theory was included which is intrinsic motivation. Further research could add
the antecedent of the variable and should look to investigate the role of UTAUT moderators (e.g.
age, gender) in explaining the relationship between the UTAUT variable and continuance
intention
CONCLUSIONS
This study reveals that effort expectancy, social influence and facilitating condition have impact
on the continuance intention to use the portal system. However, performance expectancy and
intrinsic motivation was found not to be a significant predictor of continuance intention. With
such results, this study depicts the importance of the organization (UCSA) in understanding the
psychological factors in determining the continuation of the use of the portal system. The
organization should realize that performance expectancy is an important factor in explaining the
continuation of the use of the portal system (Chiu & Wang, 2008). In addition, intrinsic factors
also noteworthy, because the students who are excited in the use of a system will lead to a
continuation of usage.
288
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THE BREAKDOWN OF GLOBAL CAPITALISM:
THE FUTURE OF BUSINESS UNDER A COLLAPSITARIAN SCENARIO
Victor Egan
Asian Forum on Business Education
Western Australia
email: [email protected]
ABSTRACT
The world is currently confronting some extraordinary environmental problems for which
available evidence suggests potentially catastrophic outcomes under a 'business-as-usual'
economic regime. By 2020, we may be facing a tipping-point leading to irreversible and
catastrophic consequences on a global scale. Such anticipated, and inter-related, issues as
climate change, degraded soil fertility, peak oil, water scarcity, food shortages, and population
growth point to a global economic system that has severely overshot ecological sustainability.
There has been considerable heated debate over the likely global outcomes ranging from climate
change sceptics who view the science as fraudulent and perceive no adverse consequences, to
'collapsitarians' who anticipate by 2050, the plenary collapse of global capitalism, and
subsequent violent conflict and death on an apocalyptic scale. This paper reviews the literature
and data underlying the collapsitarian scenario, and considers the consequences for business in
the coming decades, if this view proves to be correct.
INTRODUCTION A convergence of two ecological phenomena appears to be extant and concurrent; firstly, rapid increase in
the size and affluence of human population with consequential demands for food, water, energy, and
consumer goods; and secondly, rapid decrease in the Earth‘s biocapacity to supply those demands.
Science is backing the anecdotal evidence that we are being accosted by a recondite conundrum involving
climate change and resource scarcity that threaten to overwhelm governments, institutions, and societies.
Only in very recent years has the climate science began to crystallise pointing to the perils that confront
the global community, the urgency of requisite action, and the potentially dire consequences of
insufficient mitigation and inadequate adaptation.
With nearly half a billion people already living in drier, often over-populated and economically repressed
regions, the effects of climate change and resource depletion pose severe risks to political, economic, and
social stability to many countries. In less developed countries, where there is a general lack of resources
and capabilities required for rapid adaptation, climate change effects are likely to be exacerbated. For
many countries, climate change and resource scarcity is promising to morph into such a challenge that
violence and mass migration may be the denouement for desperate people seeking survival.
Even skeptics generally accept that the Earth‘s climate is changing, but remain doubtful of the cause (see,
for example, http://scienceandpublicpolicy.org). The most high-profile skeptic in recent times has been
Viscount Monckton of Brenchley, who has opined that ―‗global warming‘ is unlikely to be dangerous and
extremely unlikely to be catastrophic‖ (Monckton 2007, p.15). In the public arena, there is widespread
consensus that climate change is occurring (about 80 percent of Americans, Europeans, and Australians
believe that climate is changing), but there is also considerable polarization as to the cause (40-50 percent
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of people believe climate change is being caused by human activity, while another 30-40 percent believe it
is part of the Earth‘s natural cycle) (Leiserowitz et al. 2011; Leviston & Walker 2011; Park et al. 2011).
Anxiety among politicians and policy makers is accruing over the projected scenarios of gradual global
warming, and as such, initial attempts are in progress to limit detrimental human influences. However,
these efforts may be insufficient, or may not be implemented in sufficient time. Rather than gradual
warming over decades, recent evidence exhorts the possibility that a far more dire climate scenario may
actually be unfolding. Within this context, it is noticeable that much of the current dialogue on climate
change is focused on mitigation (i.e., what needs to be done to circumvent catastrophic outcomes). Too
little debate, however, has been concentrated on adaptation (i.e., dealing with the consequences) (Lovins
& Cohen 2011).
This paper does not provide recommendations to return the world to sustainability. Rather it attempts to
understand the ‗collapsitarian‘ perspective, which champions the lucid possibility that climate change and
resource depletion will likely result in catabolic global economic collapse because of malfeasant
influential forces, ineffective institutions and political leadership, emasculated academia, corporations
focused on short-term profits, and communities mired in unsustainable lifestyles. Under the collapsitarian
scenario, regionalized degeneration of the social order, devolving into widespread conflict and violence, is
the imagined outcome. Consequently, this paper seeks answers to three fundamental collapsitarian
questions: (1) When might we expect the collapse of global capitalism?; (2) What are the symptoms of
global collapse?; and, (3) What is the future for business under a collapsitarian scenario?
GLOBAL CAPITALISM
Market capitalism is an economic system in which business is conducted by independently managed
enterprises that set their own strategies, objectives, and policies so that they choose the products or
services to supply, where to market them, and at what price to sell (Bower et al. 2011). When this system
is supported by a platform of open-border trading, then market capitalism becomes wedded to globalism
in a world ―characterized by networks of connections that span multi-continental distances‖ (Nye 2002,
p.1); global capitalism is then the outcome of the marriage between markets and free trade.
Global capitalism probably had its origins in the pre-BCE religious diaspora of Buddhism, and the post-
BCE spread of Christianity and Islam. Elementary global capitalism can be traced to Mediterranean trade
from about 1,300 BCE (Attali 2011). More mature economic globalism was evident from the 14th-century
trade networks linking England with China, extending through France, Italy, and Egypt, and then via the
Silk Road through Central Asia, or via the Red Sea, Indian Ocean, and the Straits of Malacca (History of
Globalization 2011). However, deliberate government policies and technological developments since the
end of World War II radically spurred increased international trade and investment, to the extent that the
volume of global trade increased 34-fold between 1950 and 2010 (see http://www.just1world.org).
While globalism has tended to be defined within a strictly economic/capitalist framework, there are other
significant dimensions to be considered, such as social and ecological globalism (Nye 2002). Economic
globalism (or what we colloquially refer to as ‗globalisation‘) is ―the production and distribution of
products and services of a homogeneous type and quality on a worldwide basis to customers whose tastes
and preferences are similar and converging‖ (Mahoney et al. 2001, p30) - an innocuous and abstract
statement inferring Ohmae‘s (1994) ‗borderless world‘, but one that fails to allude to the complexities of
such a system, and the fallout from such a proposition on globalism‘s social and ecological dimensions.
Indeed, the style and form of global capitalism that now surrounds us was never expressly designed, but
instead, evolved in parallel with the needs of market transactions and excess capital (Bower et al. 2011;
294
Harvey 2011). It emerged post-World War II as an experiment concocted to reconfigure the global
economic landscape, contemporarily obliviating any connection to social and ecological milieu.
From the 1980s, with the fall of socialism and the remorseless logic of ultra-libertarian ideologies
associated with the Reagan and Thatcher eras in the US and UK respectively, global capitalism was driven
by economic dogmatism, surreptitiously imagined as an economic perpetual motion machine; one
producing massive output from apparently little input, especially when externalities, such as environment
and society, are excluded from the analysis. Economists, in fomenting the notion that some corporate
inputs (such as air) and some corporate outputs (such as waste) are free gifts bequeathed by nature, have
covertly driven private profits at public cost to both present and future generations.
We have now entered a unique historical period in which global capitalism may implode due to the
inexorable forces of affluence, hedonistic consumerism, and communities mesmerized to paralysis by the
unfolding ecological crisis (Orlov 2006). We are likely on the edge of what Gladwell (2002) calls a
'tipping point'; Laszlo (2010), a 'chaos point'; and, Thurow (1996, p.326), a period of ―punctuated
equilibrium‖.
COLLAPSITARIANISM
The term ‗collapsitarianism‘ has been attributed to the American social critic James Kunstler (see Kunstler
2005), who stated in 2009 that ―I‘ve never been a complete collapsitarian‖ (McGrath 2009, p.40), in
reference to the work of Dimitry Orlov, who made extensive use of the word ‗collapse‘ in his treatise on
the future of global capitalism (see Orlov 2006). Collapsitarianism has since been associated with
dystopian groups and triumphant pessimism, fostering a ‗wish‘ for, more than a ‗belief‘ in, impending
plenary global economic breakdown (Heffernan 2009).
Orlov (2006) is one such collapsitarian who expressed a pessimistic view of the future of global
capitalism. He viewed the pre-1700s tragedy of Easter Island as a microcosm of humanity‘s future, and
proffered a subsistence lifestyle and self-sufficiency as an insight into the future of humankind; or what
McGrath (2009, p.40) noted as ―bourgeois survivalism‖. Kingsnorth and Hine (2009), following Orlov‘s
(2006) apotheosis of global collapse, argued that ‗uncivilisation‘ is the appropriate segue to the future,
involving a simplification of lifestyle, and a reconnection with nature. They view the global economic
system as an ―empire corroded from within‖ (p.3); our path so far, one of mythological progress; and, the
current status, ―proof not of our genius but our hubris‖ (p.6). While the collapsitarian perspective on
global capitalism is one of catastrophic breakdown, and is heavily influenced by dystopian ideologies,
there is also considerable evidence that the basis upon which the perspective rests cannot easily be
discounted. The next section outlines the unsustainability of the ecological and social dimensions of
globalism.
GLOBAL ECOLOGICAL OVERSHOOT
The United Nations Brundtland Commission of 1987 offered the initial definition of ‗sustainable
development‘ as humanity‘s ability to ―ensure that it meets the needs of the present without compromising
the ability of future generations to meet their own needs‖ (Brundtland 1987, p.3). The Brundtland Report
also provided an early ‗call for action‘, including a new era of economic growth, equity for the poor,
greater democracy in international decision-making, and a notice that the ―affluent adopt life-styles within
the planet‘s ecological means‖. The report warned: ―sustainable development can only be pursued if
population size and growth are in harmony with the changing productive potential of the ecosystem‖ (p.3).
Since the release of the Brundtland Report, then, how ‗harmonious‘ has our development actually been?
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Both global energy consumption and carbon dioxide emissions increased radically from the 1960s, and
continue to do so. Table 1 shows that the rate of increase of energy consumption per person stabilised in
the 1990s, but again accelerated after 2000 in a period of rapid economic growth, considerably leveraged
off China‘s development. Figure 1 demonstrates that, in the 1990s, global carbon emissions grew by only
1 percent per year, while 2000-2008 carbon emissions increased by over 3 percent per year. Hence, the
data indicates a fundamental and critical linkage between energy consumption, carbon emissions, and
economic growth.
TABLE 1: ENERGY CONSUMPTION PER PERSON (TONNES COAL EQUIVALENT PER
PERSON)
1966 1990 2000 2005
Developed countries 4.5 7.3 7.1 7.2
Developing countries 0.3 1.0 1.2 1.5
World 1.6 2.5 2.5 2.7
World increase per year 3.8% 0.0% 4.0% Source: Schumacher (1999/1973, p.13); WRI (2012)
Figure 3 proposes three scenarios for future ecological footprints as imagined in the year 2000.
Superimposed on the scenarios is the actual 2000-2010 trajectory, which indicates a situation considerably
worse than the worst case anticipated in 2000; rather than 'moderate business as usual', growing affluence
and rampant consumerism has accelerated the ecological duress on the global system. Living Planet
Report 2010 anticipates the current trajectory requiring 2.0 planet Earths by 2030, and 2.8 planets by
2050, assuming the conservative 'business as usual' scenario from 2010 which proved an entirely
inaccurate assumption previously.
FIGURE 1: GLOBAL POPULATION VERSUS CO2 EMISSIONS: 1900-2010
Source: http://www.keebraparkshs.eq.edu.au/EUREKA2009-tidalpower/BackgroundInformation.htm
The foregoing discussion profoundly points to the crux of the world‘s current conundrum. Since 1985,
humanity has demanded more than the Earth has been able to provide, and consequently, the Earth has
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been in an ‗overshoot‘ trajectory leading increasingly further from sustainable development (Laszlo 2010;
Meadows et al. 2005; Wackernagel et al. 1997). The manifest symptoms are climate change, water
shortages, overgrazing, soil erosion, desertification, deforestation, reduced cropland productivity, and
collapse of fisheries (Global Footprint Network 2010). Most worryingly, the ecological overshoot has
accelerated since 2000, during a period of intensifying debate on the dangers of this very course of action!
FIGURE 3: ECOLOGICAL FOOTPRINT SCENARIOS
The unsustainability of escalating global ecological overshoot will be felt as incremental foreshocks,
which will increase in scale and frequency (Homer-Dixon 2006). These may take the form of severe
climate events, drastic commodity price fluctuations, localized social disorder, or global financial crises.
Indeed, one innocuous foreshock could indulge a string of events culminating in catastrophic outcomes;
for example, a severe drought in one locale may lead to food and water shortages, rising prices, civil
conflict, and mass migration. Taleb (2007) brought this phenomenon to our attention as ‗black swan‘
probability. Likewise, Gladwell (2002) reminded us that a tipping-point is the precursor to outcomes that
are massively disproportional to the tipping-point event. Both have drawn on the work of Lorenz (1993)
who warned of the ‗butterfly effect‘ involving sensitive dependence on initial conditions; ―does the flap of
a butterfly‘s wings in Brazil set off a tornado in Texas?‖ (p.181).
Back in the 1960s, Hardin (1968), at a localized level, offered a profundity that has far-reaching analogous
consequences for current ecological overshoot. In admonishing the ‗tragedy of the commons‘, he pointed
to a dilemma in which people‘s short-term self-interest is contrary to long-term social interest and the
common good. As Hardin noted for cattle herders, ―each man is locked into a system that compels him to
increase his herd without limit - in a world that is limited. Ruin is the destination toward which all men
rush, each pursuing his own best interest in a society that believes in the freedom of the commons.
Freedom in a commons brings ruin to all‖ (p.1244); that is, the freedom of unconstrained consumption has
led to the ruin of the global commons.
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GLOBAL RISK FACTORS
Consequential to global ecological overshoot, the World Economic Forum (WEF 2011) recently identified
37 risks that are either ‗likely‘ or ‗very likely‘ to eventuate between 2010 and 2020. The economic impact
of the 37 risks is estimated to be approximately US$16 trillion, or an average of US$1.6 trillion per year,
representing 2.5 percent of 2010 global GDP (i.e., US$63 trillion). Given that global GDP grew by an
average of 2.73 percent per year 1990-1999, and 2.69 percent per year 2000-2010, economic growth in the
coming decades is unlikely to meet global human development expectations, especially since market
capitalism requires average compound growth of at least 3 percent per year to secure long-term survival
(Harvey 2011).
The major polemic of global capitalism is that politicians, academics, and corporations have wedded us to
‗growth‘ in all aspects of human existence. It has been semantically linked to 'progress' and 'development'
at the international and national levels, and to 'success' at the corporate and individual levels. Kohr (1957,
p.155) observed long ago that ―instead of growth serving life, life must now serve growth, perverting the
very purpose of existence‖. In more recent times, Heinberg (2011) has predicted that economic growth is
at an end, and will ultimately be replaced by a steady-state economy, and the inherent dysfunction
economic anomie entails.
Global risks, then, are promising to undermine the very basis upon which global capitalism is founded –
that is, growth. Several of the most significant global risks are discussed next. These include human
population, food and water insecurity, energy scarcity, and migration.
Human Population
Thomas Malthus (1990/1803) contended that populations expand in times when, and in regions where, the
bounty from the land is plentiful until such time as the size of the population causes ecological stress;
―The vices of mankind are active and able ministers of depopulation. They are the precursors in the great
army of destruction, and often finish the dreadful work themselves. But should they fail in this war of
extermination, sickly seasons, epidemics, pestilence, and plague advance in terrific array, and sweep off
their thousands and tens of thousands. Should success be still incomplete, gigantic inevitable famine stalks
in the rear, and with one mighty blow levels the population with the food of the world‖ (Malthus
1990/1803, p.61). Malthus argued that populations may be held in check by either of two mechanisms: (1)
Positive checks, which raise the death rate (e.g., famine, disease, war); and, (2) Preventive checks, or
‗moral restraint‘, which lower the birth rate (e.g., celibacy, birth control, postponement of marriage).
The Malthus thesis appeared to break down in Western countries in the 1800s because of the effects of the
Industrial Revolution on productivity and economic growth (Bernstein 2004; Kunstler 2005). From this
time, population growth generally tracked increasing agricultural productivity. However, current trends in
population growth and declining biocapacity may well couple to demographic entrapment to accrete the
Malthusian scenario in modern times, as indeed it did in Rwanda in the 1980s-90s (see discussion below).
Based on population growth data (see Figure 4) and neo-Malthusian beliefs, Ehrlich (1968, p.i) predicted
that ―the battle to feed all of humanity is over. In the 1970s hundreds of millions of people will starve to
death in spite of any crash programs embarked upon now. At this late date nothing can prevent a
substantial increase in the world death rate‖. He argued that human population was already excessive in
the 1960‘s, and that negative consequences of overpopulation, such as famine, disease, and social
disorder, were inevitable. Since 1970, world population has doubled from 3.5 billion to 7 billion, and
continues at a growth rate of about 75 million per year (see http://www.worldometers.info/world-
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population/), leading to the prediction in 2004 that world population will be 7.4-10.6 billion by 2050, with
developing countries growing by 58 percent and developed countries by 2 percent (UN 2004). Given that
the world passed the 7 billion mark on 31 October 2011 (UN 2011), the 2050 ‗high‘ trajectory appears to
be the more likely scenario. While Ehrlich‘s dire prediction for the 1970‘s was obvious wrong, the
preventative mechanism was accelerated exploitation of the environment; in other words, the world
averted cataclysm with cheap oil, and on ‗ecological credit‘.
FIGURE 4: GLOBAL POPULATION, 1950-2050
Source: UN 2004
Others argue that overpopulation is not the fundamental problem; rather it is massive overconsumption by
affluent countries, and growing affluence in developing countries (see, for example, Angus & Butler
2011; Foster et al. 2010). For instance, despite China having predominantly stabilised population growth
with its ‗one child policy‘ since 1979 (2000-2010: 0.57 percent per year; ‗The most surprising
demographic crisis‘ 2011), its energy consumption continues to increase at a rate of 5.2 percent per year
(‗China's coal consumption continues to rise‘ 2012) because of increasing affluence.
Meanwhile, the world‘s population continues to increase at a rate of 75 million per year, with 70 million
people per year ingressing a middle-income bracket (US$ 6,000-30,000). This phenomenon is expected to
continue for the next 20 years, accelerating to 90 million new middle-income consumers per year by 2030
(WBCSD 2008). By 2050, then, and following current projections, 2 billion additional people will have
joined the ranks of the middle class, whose consumption patterns will more closely resemble those of
high-income countries (see Figure 2).
FIGURE 2: ECOLOGICAL FOOTPRINT BY INCOME CATEGORY, 1960-2003
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Source: WBCSD 2008, p.9
The accruing evidence is pointing to a dual quandary related to human population demands: (1) growth
(biological), and (2) affluence (social); neither of which would necessarily prove devastating for human
existence if it was not for the problems associated with future supply, as discussed next.
Food and Water Insecurity
World average calorie consumption has risen from 2,550 calories per capita per day in 1980 to 2,800 in
2002; an increase of 0.4 percent per year driven by increasing consumption of protein and fat in developed
countries (UNFAO 2006), and sustained by the luxury of cheap fossil fuels and government subsidies
(Lovins & Cohen 2011). In the meantime, the United Nations Food and Agriculture Organization
indicates that there are about 740 million undernourished people in the world (UNFAO 2012); this
number is likely to increase significantly by 2030, given the projected 50 percent increase in demand for
food (WEF 2011). In parallel, the US (currently the world‘s biggest agricultural exporter) will cease to be
a food exporter because of domestic demand for food and biofuels by 2025 (Ruppert 2009); for example,
biofuel production is expected to double merely in the period 2009-15 (Lovins & Cohen 2011).
Clay (2004, p.8) highlighted the paradox between growing agricultural consumption and declining soil
productivity; ―the two trends are on a collision course‖. China and India will each loose about 10 percent
of their cropable land by 2050 (Nelson et al. 2010). Likewise, agricultural production in Africa declined
by 0.4 percent in the period 2000-4 (UNEP 2007a), and is projected to decline by up to 50 percent by
2020 and 90 percent by 2100 (Lovins & Cohen 2011). Globally, 5-7 million hectares of cropland are lost
annually; if this trend continues, then by 2050, 2.7 billion hectares would remain to support 10 billion
people, or 0.27 hectare per person (Laszlo 2010, p.39). Given that 0.15 hectare per person is the
benchmark merely for subsistence-level survival (Pottier 1993; Uvin 1998), declining soil productivity is
promising to become a critical global risk. Consequential to rising demand and declining productivity,
global prices for staple foods, such as rice, wheat and maize, are expected to increase by up to 60 percent
by 2030 (Hertel et al.. 2010), and 100 percent by 2050 (Nelson et al. 2010).
Angus and Butler (2011), on the other hand, contend that the world easily has the capacity to produce
sufficient food for a projected population of up to 10 billion by 2050. Indeed, the world produces
sufficient grain alone to provide every person with 3,500 calories per day. The problem is not the quantity
produced, but rather, the uses made of that food, which leads to food disparity between the daily
availability of 4,000 calories per person per day in developed countries, and the 2,500 calories per person
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per day in developing countries; what Neumayer (2006, p.204) calls ―eco-fascism‖. The food disparity is
associated with wealth disparity, but apart from the unconscionable outcome implied, there is also the
conversion of grain to biofuels and meat (40 percent of all grain harvested is used to feed animals), and
post-harvest loses of 15-30 percent. In others words, the world's food system is currently ―grossly
inequitable, wasteful and inefficient‖ (p.77).
Water insecurity is also proving a looming quandary. Vorosmarty et al . (2010) found that 80 percent of
the world‘s population (approximately 4.8 billion people) is currently exposed to high levels of threat to
water security, which will be exacerbated in coming years, given the expected 30 percent increase in
demand for water by 2030 (WEF 2011). UNEP (2007a, p.11) suggested that, ―about 1.8 billion people
will be living in countries or regions with absolute water scarcity by 2025 and two-thirds of the people in
the world could be subject to water stress‖.
Energy Scarcity
Between 1960 and 1989, oil was discovered at a rate of more than twice the amount consumed. However,
between 1990 and 2006 only about 50 percent of the oil consumed was found in extractable deposits
(Lovins & Cohen 2011). Consequently, 'Peak Oil‘ is speculated to have occurred during the period 2005-8
(Deffeyes 2003; Worth 2010); that is, production plateaued despite increasing prices. Ruppert (2009)
reported that world oil production is currently declining by 4-9 percent per year. Heinberg (2011) used the
‗Peak Oil‘ thesis to suggest that the future will be one of soaring energy costs as resource extraction
becomes increasingly difficult and expensive. As such, the price of oil is speculated to increase to over
US$300 per barrel by 2020 (Worth 2010); an increase of at least 300 percent from US$100-115 per barrel
in early-2012, or about 35 percent per year (see http://www.oil-price.net). Rising energy costs infers rising
food costs because of the intervening variables of fertilisers, pesticides, and transportation.
WEF (2011) predict that there will be a 40 percent increase in demand for energy by 2030, of which over
75 percent is expected to be met through fossil fuels, especially coal. Within the context of this degree of
increasing demand, Ruppert (2009) suggests that the world may reach ‗Peak Coal‘ by 2025. Zaipu and
Mingyu (2007) empirically concluded that China‘s coal production will peak and start to decline from
about 2025.
Migration
Some imagine the period to 2050 evolving in a way that is very different from the catastrophic ‗population
bomb‘ concept of the 1960s (see Ehrlich 1968). What is imagined is more akin to ‗population cluster
bombs‘, as social disorder emanates in poorer regions, such as Africa, South Asia, and Central America,
that have high birth rates, and remain mired in poverty, disease, illiteracy, and/or government dysfunction
(Revkin 2007). Christian Aid (2007) suggests that ―on current trends, a further 1 billion people will be
forced from their homes between now and 2050‖ (p.1), including climate change victims escaping war,
urban ghettos, ethnic persecution, and natural disasters, as well as those pushed aside to make way for
dams, roads, and other large-scale development projects.
Hence, the evidence indicates that escalating demands by human population are colliding with decreasing
supply of food, water, and energy (Guterres 2009; UUUNO 2011). By 2030, the cost of staple food is
expected to rise by up to 60 percent, and oil by 300 percent, while over 5 billion people may be suffering
water stress. Under these conditions, mass human migration is expected as desperate people seek survival.
But, what will be the reaction of people in better-off countries to the migration of perhaps another 1
billion people into their territories? At what point does immigration become intolerable?
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VIOLENCE: THE ULTIMATE SOLUTION?
Zhang et al. (2007), in a study of the historic impact of climate change in the northern hemisphere from
1400 to 1900, found a strong temperature-war correlation, with the intervening variable being ecological
stress. In the current milieu, they concluded that ―global warming would likely cause disastrous results in
human societies‖ (p.192).
In seeking more modern examples, Rwanda is tendered as a recent archetypal Malthusian trap, in which
the population was led to extreme violence by ecological distress. From the 1980s, soil fertility in Rwanda
began to fall sharply. This was caused by excessive deforestation, overcultivation, and consequently, soil
degradation and erosion (Percival & Homer-Dixon 1996; van Hoyweghen 1999). In addition, the
capricious climate created food shortages, as crops failed on a regular basis, including 1989-90, 1991, and
1993 (Andersen 2000; Destexhe 1995; Hilsum 1994; Pottier 1993). For example, the country‘s maize
production fell from 110,000 tonnes in 1983 to <100,000 in the early-1990s (10 percent decline in 8
years). Likewise, sorghum production (another staple in the Rwandan diet) declined from 213,000 tonnes
in 1982 to 140,000 in 1989 (34 percent decline in 7 years). From the early-1980s to the early-1990s, per
capita agricultural output fell by nearly 20 percent (Percival & Homer-Dixon 1996). The decline in food
production led to dramatic decline in the calorie intake of the general population, from an average of 2,055
calories per person per day in 1984 to 1,509 in 1991 (Uvin 1998).
By the late-1980s, life in Rwanda bordered on the catastrophic. For instance, based on the criteria of a
minimum farm size of 0.7 hectare being required to feed an average household of 5 persons, 43 percent of
Rwandan farms lacked the minimum land, and hence, lived in a situation of chronic malnutrition (Uvin
1998). Pottier (1993) cites a study in the northern Gisenyi Prefecture in the late-1970s that found average
farm size to be only 0.2 hectare. Indeed, as the Rwandan population increased the phenomena of ―severe
demographic stress‖ (Percival & Homer-Dixon 1996, p.270), and ―demographic entrapment‖ (Bonneux
1994, p.1689), prevailed; that is, an overbearing population density entrapped by its country‘s borders. By
1988, land scarcity was escalating social conflict, while land disputes were increasingly difficult to resolve
(Andre & Platteau 1998). An unnoticed local ―foreshock‖ (Homer-Dixon 2006, p.254) set in motion a
series of events that ultimately led to genocide in 1994, and the deaths of almost 1 million people (Keane
1995; UN 2007). The Rwandan genocide has been described as ―the most brutal, widespread, and
systematic killing spree the world has ever witnessed‖ (Uvin 1998, p.49); ―one of the most appalling
bloodbaths of the 20th century‖ (Lemarchand 2000, p.1).
But Rwanda is not a lone modern example of social cataclysm induced by ecological distress and
Malthusian entrapment. Faris (2009) observed the same phenomenon in Darfur in 2003; aggression
―forged in a time of desertification, drought, and famine‖ (p.7). Likewise, Ban Ki Moon (2007, p.1) noted
that ―the Darfur conflict began as an ecological crisis, arising at least in part from climate change‖. UNEP
(2007, p.8) found that ―competition over oil and gas reserves, Nile waters and timber, as well as land use
issues related to agricultural land, are important causative factors in the instigation and perpetuation of
conflict in Sudan‖, and concluded that ―there is a very strong link between land degradation,
desertification and conflict in Darfur‖. Similarly, Howard (1998) wrote of the insidious links between
environmental degradation, rural-to-urban migration, and increasing conflict in Haiti. Scarcities of arable
land, water, and forest resources quickly led to social disorder and civil strife; ―Protests became riots, riots
became increasingly violent‖ (p.7).
The cases of Rwanda, Darfur, and Haiti demonstrate that a Malthusian trap can emerge in developing
countries that lack the resources to subvert the sorts of foreshocks that destabilize social order.
Demographic entrapment can obviously be alleviated by mass migration, which occurred in Rwanda at the
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time of the genocide. Interestingly, it was violence, rather than malnutrition, that induced the migration;
during the genocide, 2 million Rwandans fled to the Congo, Burundi, and Tanzania (Caplan 2005; Pace
1995; Temple-Raston 2005). As such, the mass migration speculated due to the food, water, and energy
insecurities resulting from climate change and resource depletion will likely manifest indirectly as a result
of localised violence. As Schwartz and Randall (2003) note, humans fight when they outstrip the
biocapacity of their natural environment; ―every time there is a choice to be made between starving and
raiding, humans raid‖ (p.17).
Our precarious future raises a question for survival: what mechanisms are currently available to mitigate
the looming quandary of population demands and diminishing supplies of food, water, and energy?
Perhaps elucidation lies with improved technology; leadership by institutions and policy makers; influence
from academia; reformed corporations; or, changing community attitudes. Each of these possibilities is
discussed next.
TECHNOLOGICAL POSITIVISM
While climate change and its associated issues appear to have gained the attention of most, many appear
to believe that technological innovation driven by market forces will solve the problems. However, the
problems we face today are not just the consequences of the failure of economics, but also our
technological hubris. In other words, technology has been part of the problem, and is declining as a source
of the solution.
Technology has been part of the problem because it has provided the rapid improvements to computer
power, communication, and transportation that were conducive to mass production, and the evolution of
hedonistic consumerism. In fact, the primary goal of technological innovation under global capitalism is to
expand production and increase profits, rather than protect the environment. Moreover, ―Jevon's Paradox‖
(Foster et al. 2010, p.170) demonstrates that, while technology facilitates greater efficiency and lower
cost, consumption increases according to the insidious principle of supply and demand; as Kunstler (2005,
loc.3297) observed ―efficiency is the straightest path to hell‖.
Technology is also declining as a source of the solution to the problem for two reasons. Firstly, we are
witnessing the demise of Moore‘s Law as a guiding principle for technological innovation (Attali 2011).
In accordance with Moore‘s Law, transistor density on integrated circuits has approximately doubled
every 2 years since 1968. This phenomenon has driven improved computer functionality and performance,
and decreasing costs for computer-based hardware (Intel 2005). However, a fundamental flaw in Moore‘s
Law technological positivism is that it applies only to technology, and says nothing for the rate of
advancement of innovations in other facets of human existence, such as energy, transportation, and food
production (Heinberg 2011). Secondly, research and development (R&D) activity will likely decline in
coming years, as companies seek less risky business strategies, and governments are constrained by
declining tax revenues. Similarly, US military R&D expenditure is also likely to decline, which was the
major driver of technological innovation from the 1970s (Heinberg 2011).
From an historical perspective, Toynbee (1947), in a study of the decline of past civilisations, noted that
technological improvement was often occurring simultaneously with society decline; that is, technology
has not, in itself, historically been a mechanism to support human development.
INSTITUTIONS AND LEADERSHIP
Global capitalism presupposes legitimacy and effectiveness in its governing institutions, so that stability
and the rule of law are maintained, and at a local level, community services, such as health and education,
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are provided. To aid the effective working of global capitalism, institutions and policy makers must not
only be effective, but must also be trusted by the people to be so (Lorenzoni & Pidgeon 2006).
But, Tushman (1984) observed the incidence of ‗folly‘ by policy makers that resulted in decisions and
outcomes contrary to the interests of the public. Political folly is driven by ego, Faustian lust for power,
preference for the status quo, and stubborn persistence even in the light of escalating dysfunction.
Politicians are too often fixated on the next election and typically choose political expediency over
resolving the truly difficult issues, unless crisis is immanent (Deeb 2011). The World Economic Forum
‗Voice of the People‘ survey of 2003 concluded (WEF 2003, p.4): ―The most troubling finding is that the
principal democratic institution in each country (i.e., parliament, congress, etc.) is the least trusted of the
17 institutions tested; more than half of respondents felt distrust for global and large national companies,
and political leaders‖.
Bower et al.‘s (2011) more recent study of business leaders also found great concern with the inadequacy
of existing national and international institutions. International institutions were viewed as poorly
managed, bloated bureaucracies, which, having emerged in the post-World War II era, had never been
designed to cope with the scale or scope of global problems now looming. Likewise, WEF (2011)
proffered ineffective global governance as one of the most significant global risks to 2020. For instance,
international institutions have been highly ineffective in addressing the climate change issue. The most
significant outcome at the recent United Nations Climate Change Conference in Durban in 2011 was an
agreement for continued dialogue towards a possible legal framework by 2015. This follows the Kyoto
Protocol of 1998, the Bali Action Plan of 2007, Copenhagen 2009, and the Cancun Agreements of 2010
(UNFCCC 2011). Overall, more than 15 years of climate change debate has, at best, produced weak
agreements, lack of effective commitments, and abject failure (see
http://www.iisd.ca/vol12/enb12459e.html; Orlov 2006).
SELF-EMASCULATED ACADEMIA
Universities were once the major repositories of information and knowledge that was largely inaccessible
to the ‗unlearned‘ (Menand 2010). In more recent times, however, information and knowledge has
dispersed by virtue of the internet and social media, to the extent that it is no longer an esoteric value-
adding characteristic of academia.
Menand (2010) notes the loss of respect, and subsequent rise of scepticism, for academic authority as
early as the 1960s. This was driven by the over-specialisation of disciplines and disconnect from an
increasingly complex and inter-disciplinary world. The genesis of the disconnect is suggested as
emanating from a doctoral education system that socialises academics with skills and attributes that bear
little resemblance to the most valuable of tasks which they need to perform; that is, ―to write for a general
audience, to justify their work to people outside their discipline‖ (pp.157-8). The post-1960s massification
of higher education further lifted the veil on an elitist profession, and demystified the historical font of
knowledge to imbue what we experience today as a crisis of institutional legitimacy. This crisis is
exemplified by the degree of public scepticism for climate scientists evident in recent years (see, for
example, Monckton 2007; Oreskes & Conway 2010).
More specifically, economics has failed to provide the theoretical framework and moral compass so
desperately needed in the climate change debate, probably because of insidious collusion within the
mainstream business milieu; what Chomsky (2011) observed as ―conformist intellectuals‖, and Foster et
al. (2010, p.22) as ―capitulation to the status quo‖. Economics exemplifies the general academic
propensity for a quixotic compulsion to embrace certitude from contextually isolated universities (Currie
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et al. 2010; Gosling & Mintzberg 2006; Pfeffer & Fong 2002); a pervasive example of what Saul (1992,
p.575) insightfully referred to as the ―folly of professional dialects‖.
REFORMED CORPORATIONS
Businesses are currently responding to the climate change issue by viewing it as a marketing and public
relations opportunity. The 2011 report of the Carbon Disclosure Project (CDP 2011) notes that 85 percent
of respondent companies are cognisant of ―the sizeable opportunity to seize a market leadership position
by mitigating their climate change effects and communicating their actions to shareholders and customers‖
(p.18); as Thurow (1996) observed, when it comes to the environment, ‗good capitalists‘ will tend to do
nothing because the future is discounted to favour short-term profits. Sophisticated marketing and
advertising, aimed at bringing into being consumer wants that did not previously exist (Galbraith 2007),
has inculcated our destructive overconsumption culture; product design based on disposability and
planned obsolescence has fostered and validated waste; and, persistent lobbying of policy makers has
distorted information flows to the public. This highlights that business is an unlikely liberator of a
collapsing economic system; rather, it has been the primary protagonist (Angus & Butler 2011; Bremmer
2010).
Naisbitt (1994, p.12) foresaw a ‗global paradox‘; ―The bigger the world economy, the more powerful its
smallest player‖, in which case the ‗smallest player‘ was defined as the ‗entrepreneur‘. Naisbitt was right
in imagining the emergence of individual actors as the power-holders under global capitalism, but he saw
this in positive terms expressed as empowerment, innovation, and a counterforce to big business. What he
failed to see was the negative aspects of individual power-holders, and the immense influence they could
sustain over political systems as they pursue personal micro-agendas irrespective of social common good
(Oreskes & Conway 2010). This aspect of the entrepreneur was highlighted by Garnaut (2011, p.7), who
noted ―the struggle between special interests and the national interest‖. Similarly, Hamilton (2010)
observed the existence of conservative think-tanks and influential lobby groups that are intent on blurring
climate science, as well as the rise of ―greenwashing‖ (p.82), in which corporations expend money on
projecting an eco-friendly image, rather than focusing on the environment itself.
COMMUNITY ATTITUDES
A 2008 survey of consumers in Brazil, Canada, China, France, Germany, India, UK, and US, showed that
only 53 percent were concerned with environmental and social issues, and even those were generally not
prepared to spend more money to mitigate negative externalities. The World Business Council for
Sustainable Development (WBCSD 2008) concluded that there is a substantial gap between consumer
attitudes and consumer behaviour. While consumers demonstrate a significant degree of environmental
and social awareness (e.g., ‗green‘ consciousness; climate change), they generally show scant regard in
their behaviour, lifestyle, and purchasing decisions.
Leviston and Walker (2011) found that over 40 percent of Australians believe climate change is the result
of the Earth‘s natural cycle, and hence, feel that lifestyle change is futile. Also in Australia, a community
newspaper reported the results of a survey of local residents on their attitudes to climate change.
Consistent with other studies, Australian respondents are generally prepared to make minor cost-neutral
changes, but are reluctant to make inconvenient changes to lifestyle, or to expend more significant
amounts of money. The survey concluded that ―Readers will do things to save the world – as long as it
doesn‘t cost time, money or effort‖ (Yes, but not at any cost 2010, p.22).
In Europe, 27 percent think that the seriousness of climate change has been exaggerated, and 31 percent
think that it is an unstoppable natural phenomenon (EU 2009). Park et al. (2011) reported that 37 percent
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of British think many claims about environmental threats are exaggerated (up from 24 percent in 2000).
The proportion who think it is ‗definitely true‘ that fossil fuels contribute to climate change has fallen
from 35 percent in 2000 to 20 percent in 2011; 43 percent agreed with the statement ‗We worry too much
about the future of the environment and not enough about prices and jobs today‖; and, in order to protect
the environment, only 26 percent were prepared to pay higher prices, 22 percent higher taxes, and only 20
percent were prepared to make any changes to lifestyle. The authors concluded that ―It seems, generally,
that people may be less likely to change their behaviour for the sake of the environment if this will cost
them money, time or effort‖ (p.98). In addition, the authors highlighted growing scepticism and
‗environmental fatigue‘; ―People, despite their exposure to mounting evidence concerning the negative
future consequences of climate change, may have come to feel over time that climate change has little to
do with them personally or their lives‖ (p.106).
For Americans in 2006, the most serious global concern was terrorism, followed by the war in Iraq.
Concern about the environment ranked 13 out of 18 issues, just behind declining ‗family values‘. In the
US, the environment is usually framed as a trade-off against economic prosperity. According to the
survey, only 35 percent of Americans believe that climate change will lead to a change of lifestyle; 18
percent believe that technology will solve the problem; and, only 27 percent believe that, while climate
change may be a problem, the US will most likely do nothing. When asked how much people were willing
to pay to address climate change, the average amount was US$21 per month overall; for electricity, 80
percent were prepared to pay an extra US$5 per month, while less than 50 percent were prepared to pay an
additional US$15 per month (Curry et al. 2007). Leiserowitz et al. (2011), in a more recent 2010 survey,
found that Americans remain sceptical of climate scientists; 50 percent believe that climate change is
caused by human activity, while 33 percent believe it is a natural cycle; and, in any case, only about 10
percent believe that climate change will have any significant impact on them, their families, or their
communities.
Lorenzoni and Pidgeon (2006), in a meta-analysis of climate change attitude surveys in Europe and the
US, concluded that ―laypeople have an ambivalent attitude towards climate change‖ (p.87), and ―perceive
it as a threat (and therefore potential danger) to others, those more vulnerable and/or future generations‖
(p.87); they noted ―low salience of global warming and the persistent misunderstandings of the problem‖
(p.87).
Hardin (1968, pp.1244-5) observed back in the 1960s that ―the individual benefits as an individual from
his ability to deny the truth even though society as a whole, of which he is a part, suffers‖. People are
reluctant to acknowledge that an accustomed way of life is unsustainable except in the face of prolonged,
devastating failure, and often need a cataclysmic wake-up call to change social attitudes and behaviours
(Attali 2011; Homer-Dixon 2006). In the case of climate change, the wake-up call may likely be way too
late to avoid the world tipping over the edge into irreversible decline.
In summary, there is substantial and accumulating evidence that the world is in ecological overshoot, and
that unsustainability is the hallmark of our economic, social, and ecological milieus. On the other hand,
we appear able to place little credence in technology, institutions and policy makers, academia,
corporations, or changing community attitudes to mitigate the fallout of our failing systems. Indeed, the
evidence indicates that the collapsitarian scenario is one which cannot be easily discounted as a possible
future outcome, and as such, due diligence by business leaders should dictate the imposition of three
fundamental questions for the future. These questions now follow.
QUESTION 1:
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WHEN MIGHT WE EXPECT THE COLLAPSE OF GLOBAL CAPITALISM?
According to the British Stern Review (Stern 2007), if the world continues in a ‗business-as-usual‘
fashion, there is a 50 percent risk of global temperatures rising by more than 5 degrees Centigrade during
the following decades, creating an ―average 5-10% loss in global GDP, with poor countries suffering costs
in excess of 10% of GDP‖ (p.9), and ―a reduction in consumption per head of between 5 and 20%‖ (p.10).
A rise in excess of 5 degrees Centigrade is beyond the collapsitarian tipping point, or the ―catastrophic
scenario‖ as defined by Campbell et al (2007, p.17), which would give rise to mass migration due to a sea
level rise of over 2 metres; massively destructive religion-based terrorism; society rage at government‘s
inability to deal with the abrupt and unpredictable crises; violence toward migrants and minority groups;
and intra- and interstate conflict over food, water, and energy; in such a milieu, ―altruism and generosity
would likely be blunted‖ (Campbell et al 2007, p.85).
The potential consequences of global overshoot are obviously profound. Humanity remains poorly
equipped to deal with looming catastrophes, since we are now embedded in a context for which we have
no living memory to guide appropriate action. Schwartz and Randall (2003), in a report to the US
government, suggest that abrupt climate change is likely to stretch the Earth‘s biocapacity well beyond its
already over-extended limits. Destructive and aggressive wars are then likely to be fought over food,
water, and energy. Deaths from wars, as well as starvation and disease, will radically reduce population
size, which Malthusian-like, will re-balance the biocapacity of the Earth.
Global ecological duress (as is the case with wealth and power) is inequitably distributed among nations
and regions, so that there would be an expectation that collapse of global capitalism will likely start with
those countries at most risk (e.g., Africa, South Asia, Central America) and then spread to others (IPCC
2011). A collapse is imagined that may be slow in starting, but once started, will quickly escalate to
encompass the world, including the possible fragmentation of the EU and the US (Kunstler 2005). The
result of overshoot and collapse will be a standard of living for humanity that is much diminished from
that which we have become accustomed in wealthy countries.
Toynbee (1947) pointed out a similarity of courses for failed civilisations in the past, consisting of an
accumulation of pressures and assault on society, followed by cataclysm and breakdown. He found a
―rhythm of disintegration‖ (p.589) for the decline of civilisations, consisting of ―rout-rally-rout-rally-rout-
rally-rout: three-and-a-half beats‖. Blaha (2002), adapting Toynbee‘s (1947) theory, found that history
shows a period of 134 years from start-up to the tipping-point of breakdown, and that start-up to the end of
the final rout of Toynbee‘s 3.5 beats represents about 1,068 years. Projections suggest the emergence of
powerful Chinese, Indian, Islamic, and Orthodox civilizations to 2050, with global war averted by
continued Western influence. By 2100, however, all of the five civilizations will have exhausted their
potential, and the final ‗rout‘ will have run its course. Thus, by extrapolation of historical periodicity,
Blaha (2002) speculates a new era for human civilisation by 2100, as well as considerable trauma in the
intervening ‗rout‘ years.
To bring the discussion back to the nearer future, Meadows et al.. (2005) predict that, under current
trajectories, the tipping point will be 2020-30; the point at which we begin to ascertain declining industrial
output and declining overall human welfare (as measured by food and water security, health and
morbidity, and life expectancy). By 2100, Meadows et al. (2005) suggest, human welfare will have
declined to levels consistent with 1900.
Attali (2011) imagines that by 2020, nation states will start to disintegrate into extremist dictatorships,
fundamentalist theocracies, tribal groups, and smaller more robust city states, reminiscent of 15th-century
307
Europe. This will lead to nationalisation of important industries (e.g., energy; food; air transportation), and
hence, ―new patterns of ownership‖ (Schumaher 1999/1973, p.230). Moreover, the small city states are
expected to form regional alliances for trade and security purposes.
Brown (2011) and Porritt (2009) imagine the coming of a 'perfect storm' of converging crises, involving
financial and economic fragility, climate change, population growth, soaring energy costs, drought, and
water and food scarcity that will consume the world during the period 2020-30. Worth (2010, p.27)
suggests that the period to 2030 will likely be ―the Second Great Depression‖, as a result of ‗Peak Oil‘ and
‗Peak Debt‘. It will also be a period punctuated by high inflation of 10-15 percent per year in developed
economies. Ruppert (2009) argues that, to 2020, the world will be buffeted by regular boom-bust cycles
because economic growth will stimulate rapidly rising fuel and commodity prices, which will rapidly
counter growth and lead to recession. Deeb (2011, p.101) speculates that ―resource nationalism‖ (i.e., state
control of commodities) will become increasingly common as countries seek to secure scarce resources.
This will inevitably lead to an increasingly hostile world. As such, business beyond 2020 is expected to be
conducted increasingly by local small businesses, while at the global level, we will likely see networked
conglomerates of small localised businesses similar to what are currently referred to as ‗virtual
corporations‘ (Byrne et al. 1993; Davidow & Malone 1992).
At the political level, an enfeebling of governments is imagined as social services, and laws protecting
workers‘ rights and the poor, disintegrate in the wake of weakening tax revenues (Attali 2011; Kunstler
2005). Internationally, government enfeeblement is likely to foster piratical criminal businesses run by
mafiosa and warlords, and which indulge in violence to achieve their objectives. There will emerge an
―anger of the secular‖ (Attali 2011, p.219) as resources deplete and climate change runs out of control;
both global capitalism and democracy will be justifiably denounced as abject failures. Within this context,
global capitalism will ultimately fail and collapse. In its place will be hyper-conflict within urban centres;
between failed states; and, inter-regionally between city state militias. Ultimately, Malthusian-like
cataclysmic global conflict will result in a more balanced global biocapacity, and positive outcomes for
the world beyond 2060.
Smith and Vivekananda (2007, p.3) indicate that many of the poorest countries in the world face a
―double-headed problem‖ - that of climate change and violent conflict. There is a high risk that climate
change will compound the propensity for violent conflict, which in turn will leave poor countries even
poorer, less resilient, and less able to cope with the consequences of climate change. They note that there
are currently 46 countries and 2.7 billion people, in which the effects of climate change interacting with
economic, social and political complications will create a high risk of violent conflict in the short-term.
There is a further group of 56 countries, and 1.2 billion people, where the institutions of government will
have great difficulty accepting the strain of climate change concurrent with other challenges. In these
countries, though the risk of armed conflict may not be so immediate, the interaction of climate change
and resource scarcity will create a high risk of political instability, with potential violent conflict a distinct
risk in the longer-term. Overall, then, Smith and Vivekananda (2007) suggest that, up to 2050, 102
countries and 3.9 billion people (i.e., over 50 percent of the world‘s population) may likely become
imbued in violent conflict induced by ecological duress.
The coming period from 2012 is imagined as one of industrial, financial, and social haemorrhage, which
will escalate beyond 2020, and lead to plenary collapse of global capitalism by 2050-60. From now on, we
are likely to experience symptoms that point to impending crisis, and ultimate collapse.
QUESTION 2:
308
WHAT ARE THE SYMPTOMS OF GLOBAL COLLAPSE?
Meadows et al. (2005, pp.176-7) proffer a number of symptoms of imminent global economic collapse
with which companies and individuals will likely be faced:
Higher government taxes to cover increasing costs of public services;
Increasing cost of commodities as natural resources become more scarce;
Deterioration of public infrastructure as capital costs increase beyond government ability to spend,
with maintenance increasingly deferred;
Escalating military spending as governments become cognisant of growing international hostility;
Diminishing government investment in the future (i.e., education, health, public housing, foreign aid)
as the focus on government revenues is directed to immediate needs, such as public security (i.e.,
police) and land issues (i.e., soil productivity);
Shifting consumption patterns as a financially-constrained public refocus spending onto necessities,
rather than narcissistic satisfaction;
Increasing public antagonism towards government and politicians as they become increasingly
emasculated in the global milieu;
Accession of political and theocratic extremism;
Proliferation of illegal immigration as people seek to escape areas of high ecological duress, or those
subject to conflict; and,
More frequent and severe natural disasters due to declining resilience in the ecological system.
The above symptoms are likely to manifest as severe macro-economic consequences, such as falling stock
markets amidst declining company profits; expanding unemployment as companies downsize; decreasing
private incomes, a return to one-income families, declining pension funds for the retired; and, repressed
interest rates as corporate and government investment is stifled. Given the symptoms and macro-economic
consequences outlined, the third question may now be posed.
QUESTION 3:
WHAT IS THE FUTURE OF BUSINESS UNDER A COLLAPSITARIAN SCENARIO?
The Intergovernmental Panel on Climate Change (IPCC 2011) and the UK Department of International
Development (Conway 2008) have both noted the differential impact of climate change on different
industrial sectors. For example, extreme events will have greater impact on sectors with closer links to
climate, such as water, agriculture and food security, forestry, health, and tourism; while drier climates
will increase the likelihood of food and water insecurities.
Mills (2005) notes the vulnerability of the insurance industry, while Attali (2011) speculates a positive
outcome for the entertainment industry (i.e., movies; television; music; sports) as people seek to escape
present-world difficulties. Worth (2010) suggests a positive outlook for renewable energy, but dire
consequences for any products associated with discretionary spending (e.g., fast foods; cable television;
technology). Lovins and Cohen (2011) suggest negative outcomes for the airline industry, as a major
consumer of oil derivatives, as well as a major contributor to carbon emissions. Climate change consulting
is expected to be a boom industry (EBI 2012). Eilperin (2009) observes the likelihood of shortages of raw
materials, and supply-chain disruptions. The health care industry is viewed as a potential beneficiary of
climate change because of increased incidence of sickness, disease, and natural disasters. Similarly, the
telecommunications industry is imagined to benefit due to less airline travel and more severe weather
occurrences (CDP 2011). On the other hand, tourism is likely to be devastated as air travel becomes less
affordable, leading to severe economic problems for countries heavily dependent on tourism as an export
industry, such as Greece, Thailand, and Bali (Lovins & Cohen 2011). Modes of freight transportation are
309
likely to shift away from trucking to rail (5 times more efficient) and sea (20 times more efficient) (Worth
2010), but this would necessitate considerable infrastructure investment in rail and port facilities by
governments cash-strapped by declining tax revenues (Attali 2010).
In light of the coming corporate devastation predicted above, an obvious question may be: What is the
capability of companies and managers to cope with such traumatic change? In answer to this question,
Hofer (1980) provides an appropriate framework from which an analysis might be gleaned. As company
profitability declines, strategy will progress through cost-cutting, revenue-generation, and finally asset
reduction. Hofer‘s framework is set within a normal business cycle, in which companies are expected to
deal with a temporary downturn until the next growth phase eventuates. However, this normalised
response will be thoroughly inadequate in the case of expansive economic collapse followed by a steady-
state economy. As global capitalism deteriorates, cost-cutting (e.g., downsizing; outsourcing) will be
maximised, options for revenue-generation will be exhausted, leaving only asset reduction as an option in
an increasingly risky and investment funds-deplete environment. In such a scenario, the profitability of
capital will divorce itself from the tradition of advancing asset values.
While industrial sectors may be differentially impacted, over time all industrial sectors in all countries will
feel the wrath of a collapsing global economic system. The ultimate outcome may be a global business
model based on small enterprises in most industrial sectors; that is, we may see a regression of 200 years
to a period synonymous with early-19th-century commerce (History of Small Business 2011); a ―radical
simplification of the economy‖ (Heinberg 2011, p.236), ―relocalization‖ of industry (Ruppert 2009,
p.145), the ‗uncivilisation‘ of society (Kingsnorth & Hine 2009). A segue to 19th-century-style small
business would likely be a traumatic one, indeed, as management in larger companies react ineffectively.
This would be followed closely by mass bankruptcies, as risk escalates and investment funds evaporate, in
an exponential spiral of corporate decay. Cash will disappear as banks fail, and stock markets and real
estate values plunge, leaving large-scale personal financial devastation (Heinberg 2011).
Leonard Kohr (1957) would support the devolution to ‗smallness‘. He studied the breakdown of
civilisations in prior eras, and argued that political, economic, and social polemics throughout history have
been fundamentally caused by ‗bigness‘ (i.e., ―the inability to cope with the problems it creates‖; p.159).
The solution is not more complexity by ‗unification‘ (that is, more international institutions), but rather,
fragmentation into small nation states, local communities, and localised economies.
CONCLUSIONS
Global capitalism is a post-World War II abstract economic theory that, by the 1980s, was portrayed as
Darwinian fact (Saul 2005). The theory subsequently morphed into a totalitarian economic regime
advocating radical global economic integration, while remaining devoid of affinity to ecological and
social contexts that articulate the real soul of humanity.
By 1990, market capitalism had trumped socialism as a basic principle of human organisation.
Henceforth, we became deluded by the prospects of ever more technological marvels; beguiled by
narcissistic consumerism; and, mired in cornucopian passivity, as economists, politicians, academics, and
corporations projected a utopian future of incessant wealth creation. We became seduced within, what
Toynbee (1947, p.582) coined, an ―intoxication of victory‖; a sanctimonious victory over socialism and
nature - indeed, a victory over all, except unwittingly, over ourselves.
It is not as though we have not been warned of the potential dangers of our insipid banality. Thurow
(1996) highlighted that global capitalism was inherently unstable because it fostered increasing wealth
310
inequalities, marginalized citizenry, and projected the absurd impropriety of hedonistic consumption as a
long-term vision for humanity. Likewise, Fukuyama (1999) foresaw globalization as a ‗great disruption‘
that was destined to dissolve into moral decline, weakening social bonds, and the breakdown of social
order.
But, it is the precipitous decay of the global ecosystem that is promising to provide the tipping-point into
social chaos, and the breakdown of global capitalism as an economic system. Growth of human
population and affluence is grafting unsustainable ecological demands on a system that is already in
overshoot. Climate change is the precursor to food and water scarcities, which, when coupled to acute
resource depletion, will likely lead to mass human migration as people seek survival in better-off
countries. The recent cases of Rwanda, Darfur, and Haiti provide sobering evidence of the cataclysmic
consequences of people caught in a Malthusian trap, constrained within their borders and subject to
violence. Extrapolation of these recent cases to the prospect of similar situations occurring in over 100
countries, and involving perhaps 4 billion people simultaneously (Smith and Vivekananda 2007), provides
a frightening scenario on a truly apocalyptic scale.
Meanwhile, humanity remains mired in denial over the recondite evidence that global capitalism is
inherently unsustainable; Al Gore (2006) called it an ‗inconvenient truth‘. There is a tendency to deem
technology and governments as the accredited managers of negative externalities, so that people may bear
no responsibility to act, or to change their unsustainable lifestyles. Our psychological disposition is
abetted by powerful lobby groups and influential conservative elements that aim to besmear information
and maintain the status quo. Given the overwhelming lack of trust of international institutions, political
leaders, governments, and corporations, it is little wonder that the world has become mired in a crisis of
credibility, and stalemate for action; ruin of the global commons has, hence, been humanity‘s trodden path
since the 1980s.
What, then, are the possibilities for proaction from this point in time? Stiglitz (2002) argues that our
current economic system works, but political leadership is lacking and international institutions need
strengthening; but fragmented nation-states will likely be on the ascendency (Kohr 1957), while
politicians remain tarnished by Faustian illusion, dysfunctional folly, and fixation on re-election (Deeb
2011; Tushman 1984). Isaak (2005) contends that the wealthy must become philanthropic, at a time when
equanimity, altruism, and generosity will likely be in recession (Campbell et al.. 2007). The International
Labour Organization calls for a ―stronger ethical framework‖ (ILO 2004, p.7), in a period of ―moral
crisis‖ (Stiglitz 2010, p.278). And Sach (2005, p.358) calls for ―enlightened globalization‖ in which rich
countries use their wealth to help the poor escape the poverty trap; this concurrent with wealthy countries
mired in debt, and the escalating struggle to provide social services demanded of citizens (Attali 2011).
In the final analysis, the mainstream recommended elucidations to the quandary of climate change and
resource depletion will more likely be untenable; a slide towards plenary collapse of the existing model of
global capitalism may likely ensue; and, Malthusian-like human population consolidation, and radical
simplification of the lifestyles of those remaining, may be the only viable segue to a sustainable future for
humankind. In the interstitial period to 2030, current business models will breakdown amidst personal and
corporate devastation; mere survival will then dominate all other human concerns.
Carey (2011) wrote that European and American business enterprises of the early-1800s were small-scale
family-owned operations. Most manufacturing was conducted by artisans, who were often assisted by
apprentices or family members. The textile and shoe industries, for instance, relied on the ‗putting-out
system‘, whereby self-employed, home-based workers drew their materials from, and delivered finished
311
goods to, a central warehouse. The largest industrial enterprises at the time rarely employed more than 50
workers. Is Carey, then, unwittingly describing the fabric of the post-2030 global business model?
The available evidence indicates that the world is on a tipping-point of massive systemic collapse, and that
corporations, societies, and individuals remain ill-prepared and ill-equipped for the potentially apocalyptic
consequences.
312
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319
LIQUIDITY ASPECTS OF LARGE CORPORATE BUSINESS : A STUDY WITH
REFRENCE TO LISTED COMPANIES IN INDIA
K.S.Chakraborty, Regional Director, Indira Gandhi National Open University, Agartala Regional
Centre, Tripura-799004, India and Guest Faculty , School of Management, Tripura University,
India.
Email id : [email protected] , [email protected]
Raveesh Krishnankutty, Research Scholar, ICFAI University, Tripura,
Email: [email protected]
B.B.Sarkar, Senior Consultant, Indira Gandhi National Open University, ICRTA , Agartala,
Tripura-799004, India,
Email:[email protected]
Bhushan Chandra Das Associate Professor, Dept. Of Commerce, M.B.B. College, Agartala,
Tripura.
Email; [email protected]
ABSTRACT
The present study examines the determinants of liquidity of listed companies in India. The
analysis is based on data collected from 219 large companies of Bombay Stock Exchange 500
index. The study evaluates the determinants of liquidity by taking current ratio as well as quick
ratio as dependent variables for checking sensitivity. We found current assets to total assets,
operating profit margin and receivable days positively determine the level of liquidity.
Payable days, trade debtors to current assets, current liability to total assets and size of the
firm negatively determine the liquidity.
Keywords: liquidity, panel data, current ratio.
INTRODUCTION
Liquidity is a prerequisite for the survival of any firm (Khan and Jain 2011). The major ratios
which indicate liquidity are current ratio (current assets divided by current liability) and quick
ratio or acid-test ratio (current assets minus inventories divided by current liability). There are
320
also some other ratios such as cash ratio and net working capital. Current ratio is a measure of
short term solvency. Current ratio of 2:1 is considered as an ideal ratio (Pandey, 2011), (Khan
and Jain, 2011), (Chandra, 2008). Current ratio of 2: 1 indicates that for meeting a one rupee of
current liability the firm is having 2 rupees of current assets. However in the case of the quick
ratio inventory is omitted from current assets for looking at the liquidity, because an asset is
considered liquid only when if it can be converted in to cash immediately without a loss of value
(Pandey, 2011). 1:1 is considered the ideal quick ratio.
The present economic conditions after the 2008 economic recession is that liquidity has become
the major concern for all investors. Stiff competition in the market pushes the companies to keep
the current ratio and quick ratio as low as possible (Krishnankutty and Chakraborty, 2011). Table
1 shows the average current ratios over the past 10 years from 2001-2010 on the basis of the
Bombay Stock Exchange sectoral classification. From the table it is evident that except for
agriculture and healthcare all the selected large corporate sectors as well as a sample taken as a
whole, for most of the stated periods they are below the ideal ratio of 2:1. Even such sectors as
FMCG , Metal & metal products & mining, Oil & gas and Transport equipment all have current
ratios much lower than the sample taken as a whole.
TABLE: 1 SECTORAL AVERAGE OF CURRENT RATIO
Sectors 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Total Sample as a whole 1.50 1.44 1.37 1.26 1.29 1.34 1.39 1.45 1.38 1.39
Agriculture 2.27 2.19 2.00 2.14 1.83 1.96 2.26 1.90 1.53 1.98
Capital Goods 1.68 1.60 1.53 1.47 1.51 1.49 1.42 1.39 1.38 1.33
Chemical & petro
chemical 1.65 1.54 1.54 1.43 1.49 1.46 1.62 1.45 1.30 1.24
FMCG 1.15 1.15 1.10 1.01 1.09 1.13 1.24 1.25 1.41 1.24
Healthcare 2.54 2.54 2.33 2.23 2.31 2.64 2.49 2.22 1.91 1.96
Housing related 1.60 1.55 1.36 1.49 1.32 1.52 1.75 2.15 2.14 2.18
Metal & metal products
& mining 1.15 1.10 0.97 0.99 1.29 1.49 1.81 1.65 1.60 1.74
Miscellaneous 1.55 1.51 1.44 0.63 0.60 1.42 1.79 1.56 1.71 1.74
Oil & gas 1.51 1.37 1.24 1.29 1.21 1.20 1.18 1.38 1.20 1.23
Power 1.96 2.32 2.83 1.72 1.94 1.73 1.61 1.52 1.51 1.56
Transport equipment 1.63 1.44 1.30 1.09 1.18 1.22 1.21 1.08 1.14 0.96
Source: authors’ calculations
RATIONALE OF THE STUDY
321
Ratio analysis is a powerful tool of financial analysis (Pandey, 2011, Khan and Jain, 2011).
Financial ratios are the major tool used for evaluating a firm‘s financial condition and
performance (Van Horne et.al, 2008). According to the various users of financial ratios they are
mainly classified into four categories namely liquidity ratios, leverage ratios, activity ratios and
profitability ratios. Liquidity measures a firm‘s ability to meet the current obligations, leverage
ratio shows the liquidity equity proportion in capital structure, activity ratios shows the
efficiency in utilizing the assets and profitability measures the overall performance and
effectiveness of a firm (Pandey, 2011).
Opler et al. (1999) examine the determinants and implication of cash and marketable securities
of publically traded U.S firms. The study found that firms with strong growth opportunity and
riskier cash flows hold relatively high cash to total non-cash assets. And firms that are having
high credit ratings hold lower ratios of cash to total non-cash assets. Banerjee (2010) said that
the liquidity position of a firm is largely affected by the composition of current assets inasmuch
as any considerable shift from relatively more current assets to relatively less current assets or
vice versa. Therefor it is desirable to study the distribution of current assets to determine the
liquidity position exactly. Krishnankutty and Chakraborty (2011) examine the trend and
determinants of current ratios of listed companies in India using panel data with fixed and
random effect. The study found that current ratio is showing a negative trend in last decade.
Receivable days, payable days, inventory days and size of the firm are the major determinant of
current ratio. Chakraborty. (2003) says that liquidity consider two aspects namely the level of
investment in current assets and the sources of financing current assets.
There are a quite number of studies on liquidity and profitability trade-off, liquidity in the aspect
of investment etc. In this background the present study is designed. The objective of the study is
to understand the determinants of liquidity and to study the level of liquidity in large corporate
businesses of India.
DATA SOURCE AND METHODOLOGY
Source of Data
The study is dealing with the public limited companies listed in the Bombay Stock Exchange
(BSE) 500 index. The period considered for the study is ten years i.e., 2001 – 2010. Banking,
finance and IT companies are kept out of the scope of the study as the current assets and
322
liabilities structure of these companies are different from others. Moreover the non availability of
data of companies continuously for the entire study period also kept them out of the scope of the
study for meaningful interpretation and comparison. Thus the total numbers of companies
considered in the present study is 219. The CMIE (Centre for Monitoring Indian Economy) data
base is used for collecting the financial data.
Variables Used For the Study
The study used two dependent variables as proxy for measuring liquidity in order to check the
sensitivity of the result- Current ratio (CR) and Quick ratio (LQ).
Current ratio (CR) = Current assets/ Current liability
Liquidity ratio (LQ) = (current assets- inventories)/ current liability
Independent variables used for the study are as follows:
Receivable days (ARDAYS) = (Accounts receivable X 365)/ Sales.
Payable days (APDAYS) = (Accounts payable X 365)/ Sales
Inventory turnover (INVTURN) = Sales/ Inventory
Size of the firm (SIZE) = Natural logarithm of sales
Asset turnover (ASSTRN) = Sales/ Total assets
Current assets to total assets (CATA) = Current assets/ Total assets
Current liability to total assets (CLTA) = Current liability/ Total assets
Operating profit margin (OPEM) = PBIT/Sales
Trade debtors to current assets (SDCA) = Trade debtors/ Current assets
323
Panel Least Square with Fixed and Random Effect
The study used balanced panel data for the analysis. A data set contains observations on
different objects studied over a period of time and this is called panel data. It is a combination of
cross-sectional data and time series data. In balanced panel data same time period must be
available for all cross-sections.
To analyze the aspect liquidity the study proposes the panel least square with fixed and random
effects. For assessing the relationship between liquidity and its determinants static panel data
models are used. There are three types of panel data models: a pooled Ordinary Least Squire
(OLS) regression, panel model with random effects and the panel model with fixed effects. The
evaluation of a pooled OLS regression can be presented in the following way:
)1......(,.........)()()()(
)()()()()(
987
543210
ititititit
itititititit
CLTASDCAOPEMCATA
SATAINVTURNSIZEAPDAYSARDAYCR
)2......(,.........)()()()(
)()()()()(
987
543210
ititititit
itititititit
CLTASDCAOPEMCATA
SATAINVTURNSIZEAPDAYSARDAYLQ
Where i indexes firms, t indexes time,β1, β2, β3……….. β9 arethe coefficients of independent
variables. CRit is the current ratio, LQit is quick ratio are the measure of liquidity, ARDAYSit is
receivable days, APDAYS it is payable days, INVTURNit is inventory turnover, SIZEit is size of
the firm, SATAit is asset turnover, CATAit is the current assets to total assets, CLTAit is current
liability to total assets, OPEMit is operating profit margin, SDCAit is trade debtors to current
assets and it is the error term which is assumed to have a normal distribution and varies over
both company and time. However, by using a pooled OLS regression, firms‘ unobservable
individual effects are not controlled, and so, as Bevan and Danbolt (2004) conclude,
heterogeneity, a consequence of not considering those effects, can influence measurements of the
estimated parameters. While by using panel models of random or fixed effects, it is possible to
control the implications of firms‘ non-observable individual effects on the estimated parameters.
Therefore, by considering the existence of non-observable individual effects, we have:
)3......(,.........,)()()()(
)()()()()(
987
543210
ititititit
itititititit
uCLTASDCAOPEMCATA
SATAINVTURNSIZEAPDAYSARDAYCR
)4......(,.........,)()()()(
)()()()()(
987
543210
ititititit
itititititit
uCLTASDCAOPEMCATA
SATAINVTURNSIZEAPDAYSARDAYLQ
324
where ,itiitu with i being firms‘ unobservable individual effects. The difference
between a polled OLS regression and a model considering unobservable individual effects lies
precisely in i .
For testing the relevance of unobservable individual effects, the study used the LM (Lagrange
Multiplier) test. This tests the null hypothesis of irrelevance of unobservable individual effects,
against the alternative hypothesis of relevance of unobservable individual effects. Not rejecting
the null hypothesis, we conclude that unobservable individual effects are not relevant, and so a
pooled OLS regression would be an appropriate way of carrying out evaluation of liquidity
determinants. On the contrary, if we reject the null hypothesis that unobservable individual
effects are not relevant, we can conclude that a pooled OLS regression is not the most
appropriate way of carrying out analysis of the relationship between liquidity and its
determinants.
However, there may be correlation between firms‘ unobservable individual effects and liquidity
determinants. If there is no correlation between firms‘ unobservable individual effects and
liquidity determinants, the most appropriate way of carrying out evaluation is by using a panel
model of random effects. If there is correlation between firms‘ individual effects and liquidity
determinants, the most appropriate way of carrying out evaluation is using a panel model
admitting the existence of fixed effects. For testing the possible existence of correlation, we use
the Hausman test. This tests the null hypothesis of non-existence of correlation between
unobservable individual effects and the explanatory variables, in this study, liquidity
determinants, against the null hypothesis of existence of correlation. By not rejecting the null
hypothesis, we can conclude that correlation is not relevant, and a panel model of random effects
is the correct way of carrying out evaluation of the relationship between liquidity and its
determinants. On the other hand, by rejecting the null hypothesis, we conclude that correlation is
relevant, and so the most appropriate way to carry out evaluation of the relationship between
liquidity and its determinants is by using a panel model of fixed effects. In this study, we also
present the evaluation of the most appropriate panel model, according to the results of the LM
and Hausman tests which is consistent with the existence of first order autocorrelation.
Quantile Regression Analysis
Quantile regression (Koenker and Bassett 1978; Koenker and Hallock 2001) is a method for
fitting a regression line through the conditional quantiles of a distribution. It allows the
examination of the relationship between a set of independent variables and the different parts
of the distribution of the dependent variable. Quantile regression overcomes some of the
disadvantages of the conditional mean framework built upon central tendencies, which tend to
lose information on phenomena whose tendencies are toward the tails of a given distribution
325
(Hao and Naiman 2007). The use of quantile regression approach is chosen also because of
skewed distribution of CR, LQ, ARDAYS, APDAYS, SIZE, INVTURN, SATA, CATA,
CLTA, OPEM, and SDCA(see the evidence in Table 2). Since in such case the usual
assumption of normally distributed error terms is not warranted and could lead to unreliable
estimates. Furthermore, companies analyzed are fundamentally heterogeneous and it may make
little sense to use regression estimators that implicitly focus on the ‗average effect for the
average company‘ by giving summary point estimates for coefficients. Instead, quantile
regression techniques are robust to outliers and are able to describe the influence of the
regressors over the entire conditional distribution of CR, LQ, ARDAYS, APDAYS, SIZE,
INVTURN, SATA, CATA, CLTA, OPEM, and SDCA.
Standard least squares regression techniques provide summary point estimates that calculate
the average effect of the independent variables on the ‗average company‘. However, this focus
on the average company may hide important features of the underlying relationship. As
Mosteller and Tukey (1977, pp.266) correctly argued, ―What the regression curve does is give
a grand summary for the averages of the distributions corresponding to the set of x‘s. We could
go further and compute several regression curves corresponding to the various percentage
points of the distributions and thus get a more complete picture of the set. Ordinarily this is not
done, and so regression often gives a rather incomplete picture. Just as the mean gives an
incomplete picture of a single distribution, so the regression curve gives a correspondingly
incomplete picture for a set of distributions‖. Quantile regression techniques can therefore help
us obtain a more complete picture of the underlying relationship between liquidity (CR, LQ)
and its determinants. In our case, estimation of linear models by quantile regression may be
preferable to the usual regression methods for a number of reasons. While the optimal
properties of standard regression estimators are not robust to modest departures from
normality, quantile regression results are characteristically robust to outliers and heavy tailed
distributions. In fact, the quantile regression solution 0̂ is invariant to outliers of the
dependent variable that tend to (Buchinsky, 1994). Another advantage is that, while
conventional regressions focus on the mean, quantile regressions are able to describe the entire
conditional distribution of the dependent variable. In the context of this study, all determinants
of liquidity (CR, LQ) are of interest in their own right, we do not want to dismiss them as
outliers, but on the contrary we believe it would be worthwhile to study them in detail. This
can be done by calculating coefficient estimates at various quantiles of the conditional
distribution. Finally, a quantile regression approach avoids the restrictive assumption that the
error terms are identically distributed at all points of the conditional distribution. Relaxing this
assumption allows us to acknowledge company heterogeneity and consider the possibility that
estimated slope parameters vary at different quantiles of the conditional distribution of all
determents of liquidity.
The quantile regression model, first introduced by Koenker and Bassett (1978), can be written
as:
ititit xy 0
'
with 0
'| ititit xxyQuant (5)
326
where i denotes company, t denotes time, ityis the dependent variable, itx
is a vector of
regressors, is the vector of parameters to be estimated, and is a vector of residuals.
itit xyQuant | denotes the th conditional quantile of ity
given itx. The
th regression
quantile ,10 solves the following problem:
n
i
it
xyti
itit
xyti
ititn
xyxyn
itititit1:,
'
:,
' 1min||)1(||
1min
''
(6)
where )( , which is known as the ‗check function‘, is defined as‖:
0)1(
0)(
itit
itit
itif
if
(7)
Equation (6) is then solved by linear programming methods. As one increases continuously
from 0 to 1, one traces the entire conditional distribution of ity, conditional on itx
(Buchinsky
1998).
Here we assume that CR and LQ is the function of ARDAYS, APDAYS, SIZE, INVTURN,
SATA, CATA, CLTA, OPEM, and SDCA. Due to the advantages (as stated above) of quantile
regression estimation technique over OLS, fixed and random effect models in the study, we
examined at the 5th
, 25th
, 50th
, 75th
and 95th
quantiles as shown here for first and second
specifications respectively:
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSCRQ
05.5,05.5,05.5,05.5,05.5,05.
4,05.3,05.2,05.1,05.05.05. )(
(8)
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSCRQ
25.5,25.5,25.5,25.5,25.5,25.
4,25.3,25.2,25.1,25.25.25.. )(
(9)
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSCRQ
5.5,5.5,5.5,5.5,5.5,5.
4,5.3,5.2,5.1,5.5.5. )(
(10)
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSCRQ
75.5,75.5,75.5,75.5,75.5,75.
4,75.3,75.2,75.1,75.75.75. )(
(11)
327
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSCRQ
95.5,95.5,95.5,95.5,95.5,95.
4,95.3,95.2,95.1,95.95.95. )(
(12)
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSLQQ
05.5,05.5,05.5,05.5,05.5,05.
4,05.3,05.2,05.1,05.05.05. )(
(13)
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSLQQ
25.5,25.5,25.5,25.5,25.5,25.
4,25.3,25.2,25.1,25.25.25.. )(
(14)
it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSLQQ
5.5,5.5,5.5,5.5,5.5,5.
4,5.3,5.2,5.1,5.5.5. )(
(15) it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSLQQ
75.5,75.5,75.5,75.5,75.5,75.
4,75.3,75.2,75.1,75.75.75. )(
(16) it
it
CLTASDCAOPEMCATASATA
INVTURNSIZEAPDAYSARDAYSLQQ
95.5,95.5,95.5,95.5,95.5,95.
4,95.3,95.2,95.1,95.95.95. )(
(17)
We used sqreg module of STATA 11 for simultaneous quantile regression estimation and
obtain an estimate of the entire variance-covariance of the estimators by bootstrapping with
100 bootstrap replications. Simultaneous quantile regression is a robust regression technique
that accounts for the non-normal distribution of error terms and heteroskedasticity (Koenker
and Bassett 1978; Koenker and Hallock 2001). Unlike traditional linear models, such as OLS
regression, that assume that estimates have a constant effect, simultaneous quantile regression
can illustrate if independent variables have non-constant or variable effects across the full
distribution of the dependent variable.
RESULTS
Panel Least Square with Fixed and Random Effect
Before conducting regression analysis, correlation analysis was carried out in order to find out
whether there is any evidence of severe multicollinearity among the test variables. Since we do
not find evidence of multicollinearity (see appendix 1), regression analysis has been carried out
with incorporation of all variables simultaneously. First, we present the results of the static panel
model analysis. Results of panel data models with random and fixed effects have been presented
in table 3
328
TABLE: 3 THE RESULT OF PANEL LEAST SQUARE WITH FIXED AND RANDOM
EFFECTS
Independent
variable
Model 3 Model 4
FE RE FE RE
ARDAYS .001359***
(0001807)
.001340***
(.0001701)
.0007274***
(.0001623)
.0007693***
(.0001509)
APDAYS -.0025031***
(0004036)
-.0028219***
(.0003958)
-.0008163**
(.0003625)
-.0009949***
(.0003529)
SIZE -.0275186
(.0232609)
-.0424685**
(.0195117)
.0358867*
(.0208895)
.0268993
(.0170912)
INVTURN .0006654
(.0004277)
.0008435**
(.0004218)
.0013438***
(.0003841)
.0014444***
(.0003763)
SATA -.1002509**
(.0463167)
-.1113813***
(.0403838)
-.1999969***
(.0415948)
-.1789142***
(.0355039)
CATA 2.483092***
(.1596137)
2.009579***
(.139276)
1.540021***
(.1433415)
1.161519***
(.1225136)
OPEM .0746823
(.0582761)
.0713454
(.0536048)
.1482225***
(.052335)
.1451047***
(.0474001)
SDCA -.8604573***
(1493588)
-.8202655***
(.1396742)
-.5290588***
(.134132)
-.4320049***
(.1237661)
CLTA -.2818231***
(.0729365 )
-.3506192***
(.065621)
-.013249
(.0655008)
-.0932999
(.0578927)
constant 1.191029***
(.2016914)
1.556322***
(.1838392)
.2842534
(.1811294)
.4926345***
(.1612464)
Model Summary
R2 with in 0.2178 0.2137 0.1420 0.1378
R2 between 0.0664 0.0975 0.0241 0.0470
R2 overall 0.1101 0.1342 0.0576 0.0776
F- test 60.70*** 36.07***
Fixed effect, F-
test
11.97*** 10.24***
Wald test 527.91*** 305.08***
Hausman test 79.49*** 59.87***
No.of firms 219 219 219 219
Total panel
observation
2190 2190 2190 2190
Dependent
variable
Current ratio Current ratio Quick ratio Quick ratio
Notes: 1. The Hausman test has χ2 distribution and tests the null hypothesis that unobservable
individual effects are not correlated with the explanatory variables, against the null hypothesis of
correlation between unobservable individual effects and the explanatory variables. 2. The Wald
test has χ2 distribution and tests the null hypothesis of insignificance as a whole of the parameters
of the explanatory variables, against the alternative hypothesis of significance as a whole of the
parameters of the explanatory variables. 3. The F test has normal distribution N(0,1) and tests the
329
null hypothesis of insignificance as a whole of the estimated parameters, against the alternative
hypothesis of significance as a whole of the estimated parameters. 4. ***, **, and *denote
significance at 1, 5 and 10 % level of significance respectively. 5. FE, RE denotes fixed effect and
random effect respectively.
From analysis of the results of the Wald and F tests, we can conclude that we cannot reject the
null hypothesis that the explanatory variables do not explain, taken as a whole, the explained
variable, and so the determinants selected in this study can be considered explanatory for both
the model.
The results of the Hausman test show that we cannot reject the null hypothesis of absence of
correlation between firms‘ unobservable individual effects and debt determinants. Therefore, we
can conclude that the most appropriate way to carry out evaluation of the relationship between
debt and its determinants is evaluation of a fixed effects panel model. So the study will interpret
the result based on the fixed effect model in both the model.
Receivable days (ARDAYS) and current assets to total assets (CATA) are positively significant
at 1 percent in both the model. However payable days (APDAYS), trade debtors to current
assets (SDCA) and sales to total assets (SATA) are negatively significant at 1 percent, 5 percent
and 1 percent respectively for model 3, and 5 percent 1 percent, 1 percent respectively in case of
model 4. Size (SIZE), inventory turnover (INVTURN) and operating profit margin (OPEM) not
showing any kind of significance for model 3. But in case of model 4 all these variables are
positively determine the liquidity at 10 percent, 1 percent and 1 percent respectively. Current
liability to total assets (CLTA) is negatively significant at 1 percent in case of model three and it
is not showing any kind of significance for model 4.
Quantile Regression
First, we present descriptive statistics of our all variables analyzed in Table 4
TABLE: 4 THE RESULT OF DESCRIPTIVE STATISTICS
Mean Median
Std.
Dev. Skewness Kurtosis
Jarque-
Bera Probability Observations
CR 1.66 1.40 1.09 4.35 37.26 114016.50 0.00 2190
QR 0.91 0.71 0.89 5.28 53.86 246242.40 0.00 2190
ARDAYS 119.63 81.25 189.79 11.36 203.02 3697717.00 0.00 2190
APDAYS 54.06 44.55 50.36 11.86 311.11 8714126.00 0.00 2190
SIZE 6.95 6.88 1.55 0.26 4.24 164.33 0.00 2190
INVTURN 13.05 7.30 45.00 20.76 572.23 29724637.00 0.00 2190
330
SATA 1.01 0.87 0.72 2.41 14.97 15200.36 0.00 2190
CATA 0.49 0.48 0.22 0.18 2.17 75.12 0.00 2190
CLTA 0.28 0.24 0.17 0.92 3.44 326.97 0.00 2190
OPEM 0.24 0.15 0.62 14.20 255.57 5894525.00 0.00 2190
SDCA 0.46 0.46 0.19 0.10 2.63 16.83 0.00 2190
Source: author’s calculation
Table 4 shows one measures of tails i.e., the kurtosis among other descriptive statistics. It is
well known that whenever this quantity exceeds 3, we say that the data feature excess kurtosis,
or that their distribution is leptokurtic, that is, it has heavy tails. It is evident from Table 1 that
except for CATA, CLTA and SDCA distribution of all variables is leptokurtic. This shows that
data is not normal which is also proved with the JB test statistic. JB test statistics shows, in
particular, that no variables follow feature of normality. Therefore, estimation technique (like
OLS) based linear Gaussian models will be biased hence, use of quantile regression estimation
is more appropriate. Therefore, the study applied quantile regression estimation technique and
report result of quantiles }95.0,75.0,50.0,25.0,05.0{ in Table 5 below
TABLE 5: THE RESULT OF QUANTILE REGRESSION
variable/Quantile 0.05 0.25 0.50 0.75 0.95
ARDAYS -.0000148
(.0001336)
.0000458
(.000141)
.0006652*
(.0004027)
.0024982***
(.0009622)
.0064441***
(.0009615)
APDAYS -.0003102
(.0002405)
-
.0008817***
(.000244)
-.0009839*
(.000502)
-.0055724
***
(.0017402)
-.0106295**
(.0042633)
SIZE -.0116334*
(.0065733)
-.0100971*
(.0055473)
-.014754*
(.0083752)
-
.1070995***
(.0266563)
-
.2496088***
(.0331077)
INVTURN .0004743
(.0009316)
.0006844
(.0005639)
.0015268 **
(.0006534)
.0010218
(.0011177)
.0000451
(.0017497)
SATA -.0200322
(.0227599)
-.0067823
(.0153479)
.0046304
(.0220382)
-.052079
(.0571485)
-.1080383
(.0694277)
CATA 1.284888***
(.0750841)
1.602161***
(.0804146)
1.709484***
(.137283)
1.104016***
(.4100967)
-.1620684
(.2606661)
OPEM -.02974
(.0345951)
.0267738
(.059165 )
.0665217
(.1426374)
.7173953
(.4456597)
1.343607***
(.4033663)
SDCA -
.1734254***
(.0659588)
-
.2134072***
(.0608423)
-
.2768251***
(.0980112)
-
.8202248***
(.2841327)
-
2.801899***
(.332155)
CLTA -
.7836924***
-
1.494018***
-
2.037849***
-1.064997
(.776718 )
-.1649004**
(.0709893)
331
(.0911939) (.1094826) (.209865 )
constant .7441981***
(.0775229)
1.07581***
(.0776037)
1.375898***
(.0880631)
2.686525***
(.3909906)
5.817202***
(.4222078)
Model summary
Pseudo R2 0.2463 0.1963 0.1605 0.1463 0.2447
Dependent variable : Current ratio(CR)
Note: ***, **, and *denote significance at 1, 5 and 10 % level of significance respectively.
Source: author’s calculation
It is evident from Table 5 that receivable days(ARDAYS) is not showing significance for
0.05th
and 0.25th
quantile but in case of 0.50th
, 0.75th
and 0.95th
quantile it is positively
significant at 10 percent, 1 percent and 1 percent respectively. Except the lowest quantile 0.05th
payable days (APDAYS) is showing a negative significant in all other quantiles as 1 percent,
10 percent, 1 percent and 5 percent respectively. Size of the firm (SIZE) is showing a negative
significance irrespective of the quantile as 10 percent, 10 percent, 10 percent, 1 percent and 1
percent respectively. Inventory turnover (INVTURN) is showing a positive significance at 5
percent only in case of median quantile (0.50) all other case it is not showing any kind of
significance. Sales to total assets (SATA) is not showing any kind of significance irrespective
of the quantile. Except the highest quantile 0.95th
in all other case current assets to total assets
(CATA) is positively significant at 1 percent. Operating profit margin (OPEM) is positively
significant at 1 percent only in the highest quantile 0.95th
all other case it is not showing
significance. Trade debtors to current assets (SDCA) are negatively significant at 1 percent
irrespective of the quantiles. Constant is positively significant at 1 percent irrespective of the
quantiles.
TABLE 6: THE RESULT OF QUANTILE REGRESSION
variable/Quantile 0.05 0.25 0.50 0.75 0.95
ARDAYS .0001704**
(.000082 )
.0000507
(.0001502)
.0002138
(.0001957)
.001535***
(.0005653)
.0050529***
(.0008062)
APDAYS -.0001728
(.0001936)
-
.0006685***
(.0002424)
-.0009442**
(.0004246)
-.003045***
(.0008896)
-.001586
(.0024815)
SIZE -.0082464**
(.0038967)
.0019985
(.0047509)
-.009198
(.0077416)
-.0125959
(.0155781)
.0506542
(.0575565)
INVTURN -.0002293
(.0003509)
-.0000676
(.0007831)
.0019822
(.0014558)
.0032103**
(.0015054)
.002015
(.0029973)
SATA .0486478***
(.0108099)
.0227531*
(.0124971)
-.0061727
(.0186605)
-
.0776478***
(.029639)
-
.2121861***
(.0576296)
CATA .270684***
(.0517543)
.5389883***
(.0660343)
.5342399***
(.1249824)
.449313**
(.1766196)
.2226931
(.2812334)
332
OPEM .0649691*
(.0366213)
.1630878***
(.0405958)
.2647922***
(.0667938)
.1387364***
(.1749885)
1.296822*
(.6808432)
SDCA .3295429***
(.0533951)
.6128637***
(.044715)
.4985588***
(.0661216)
-.2017263
(.1540333)
-
1.510364***
(.3855738)
CLTA -.0894916
(.0565402)
-
.2570917***
(.0872533)
-.3542804*
(.1959924)
-.0890996
(238902)
-
.2188621***
(.0735277)
constant .0023406**
(.0437993)
-.0165878
(.0475904)
.3377901***
(.0888365)
1.055545***
(.1523291)
1.925666***
(.5318802)
Model summary
Pseudo R2 0.0564 0.0836 0.0622 0.0671 0.1707
Dependent variable : Quick ratio (LQ)
Note: ***, **, and *denote significance at 1, 5 and 10 % level of significance respectively.
Table 6 shows the result of quantile regression based on the Quick ratio taken as dependent
variable. Receivable days (ARDAYS) are positively significant at 5 percent for the lowest
quantile 0.05th
, 0.25th
and 0.50 it is not showing significance and in case of 0.75th
and 0.95th
it is
positively significant at 1 percent. In case of payable days (APDAYS) the lowest quantile 0.05th
and the highest quantile 0.95th
are not significant. All other case i.e., 0.25th, 0.50th
and 0.75th
it is
negatively significant at 1 percent, 5 percent and 10 percent respectively. However size of the
firm (SIZE) is negatively significant at 5 percent only for the lowest quantile all other case it is
not showing any kind of significance. Inventory turnover (INVTURN) is not showing
significance except the 0.75th
quantile, for this quantile it is positively significant at 5 percent.
Sales to total assets (SATA) are positively significant at 1 percent and 10 percent 0.05th and
0.25th quantiles. 0.50th
quantile is not showing significance and in case of 0.75th
and 0.95th
quantile is negatively is significant at 1 percent. Except for the highest quantile 0.95th
current
assets to total assets (CATA) is positively significant at 1 percent, 1 percent, 1 percent and 5
percent respectively. Operating profit margin (OPEM) is the only one variable showing positive
significance irrespective of the quantiles as 10 percent, 1 percent, 1 percent, 1 percent and 10
percent. Trade debtors to current assets (SDCA) are positively significant at 1 percent for 0.05th
,
0.25th
and 0.50th
quantile. 0.75th
quantile is not showing significance. And for the highest
quantile 0.95th
it is negatively significant at 1 percent. Current assets to total assets (CLTA) is
negatively significant for 0.25th
, 0.50 and 0.95th
quantile at 1 percent, 10 percent and 1 percent
respectively. In case of constant except 0.25th
quantile in all other case it is positively significant
at 1 percent.
CONCLUSION
The study was intended to identify the determinants of liquidity for Indian firms using a panel
framework. The study has taken current ratio as well as quick ratio as dependent variable for
checking the sensitivity of the ratios. For the analysis, we have taken 219 firms (from the BSE
500 firms based on the availability of data) during the period 2001-2010, comprising of a panel
model with fixed and random effects. However, most of the variables show skewed
333
distribution and therefore, we relied upon quantile regression analysis as an appropriate tool
and quantiles used for our case are }95.0,75.0,50.0,25.0,05.0{ .
We found that the Fixed and random effect model are not found to performing well. The
overall study find that Receivable days are positively determine the liquidity in case of upper
quantiles (0.75th
, 0.95th
). However the payable days are negatively determining the liquidity for
the lower quarter to upper quarter quantiles (0.25th
- 0.75th
). Size of the firm is negatively
determining the liquidity only for the lowest quantile (0.05th
). Inventory turnover does not have
any impact on determining the liquidity. In case of sales to total assets we are unable to draw
any kind of conclusion because of un-common result in both the model. Current assets to total
assets are positively determining the liquidity except the upper quantile (0.95th)
. Operating
profit margin is positively determining the liquidity in upper quantile (0.95th
). Trade debtors to
current assets negatively determine the liquidity in upper quantiles (0.75th
, 0.75th
). And in case
of lower quantile it is showing significance in opposite signs. Current liability to total assets
negatively determines the liquidity in case of 0.25th
, 0.50th
and 0.95th
quantile.
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structure determinants‘, Working Paper, No. 2001/4, Department of Accounting and Finance,
University of Glasgow, Glasgow G 12 *LE.
Buchinsky, M. (1998). Recent advances in quantile regression models: a practical guide for
empirical research. Journal of Human Resources, 33, 88-126.
Chakroborty, K.S.(2003), Anatomy of overtrading, New Delhi, Mittal Publications.
Chandra, P. (2008) Financial management theory and practice. 7th
Edition. New Delhi: Tata
McGraw Hill Publish Company Limited. 7th
Edition
Hao, L. and Naiman, D.Q. (2007). Quantile regression. Thousand Oaks, CA: Sage.
Khan, M.Y. and Jain, P.K. (2011) Financial management text problems and cases. 6th
Edition.
New Delhi: Tata McGraw Hill Publish Company Limited.
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33–50.
Koenker R, Hallock K.F. (2001). Quantile regression. Jornal of Economic Perspectives, 15, 143–
156.
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Krishnankutty, R & Chakaraborty, K. S (2011). Determinants of current ratio. MPRA working
paper.
Mosteller, F., & Tukey, J. (1977). Data Analysis and Regression Addison-Wesley, Reading,
MA.
Opler. T, Pinkowitz. L, Stulz. R and Williamson. R. (1999). The determinants and implications
of corporate cash holdings, Journal of Financial Economics, 52, 3-46.
Pandey, I.M. (2010). Financial management. 10th
Edition. New Delhi: Vikas Publishing House.
335
UNDERSTANDING STUDENT TUTORIAL ATTENDANCE BELIEFS
Marthin Nanere, Latrobe University, [email protected]
Phil Trebilcock, Latrobe University, [email protected]
Apollo Nsubuga-Kyobe, Latrobe University, [email protected]
ABSTRACT
Many reasons are given as to why students are reluctant to attend tutorials for their chosen
subjects. Using the Theory of Planned Behaviour, this article aims at exploring three different
beliefs - behavioural, normative and control beliefs - to come up with persuasive communication
messages that could be used to encourage students to attend tutorials. 166 structured interviews
were undertaken with business students on Latrobe university Bendigo campus during semester
one 2011. The findings suggest that behavioural and control beliefs are the most different
significant beliefs amongst both those attending tutorials (compliers) and those not attending
tutorials (non-compliers). Based on these significant differences, several persuasive
communication messages are suggested in the paper.
Keywords: Behavioural beliefs, normative beliefs, control beliefs, persuasive communication
messages, tutorial attendance.
336
INTRODUCTION
There is a growing trend for university students to avoid attending tutorial classes (Schmulian, et
al., 2011). This is despite a tutorial being an interactive session between the lecturer and student
that is critical for learning and for enhancing further understanding of subject-related content.
This trend of avoiding tutorial classes has several future negative implications. Students of today
are the managers and workforce of tomorrow. It is imperative therefore to improve attendance
rates, thus helping to maximise the potential for students to gain an understanding of the
information before them, not just having access to it. By attending tutorials, students have the
potential to reinforce essential topics and to understand these topics to a deeper level. Tutorial
attendance is also critical in building an overall positive learning and attendance culture within
universities.
Recent advances in communication theory and research indicate that if we understand what
people think about a given behaviour (tutorial attendance), then we will have a better chance of
influencing these people to adjust their actions. The aim is to see behaviour through the eyes of
substantiated theory. Messages can then be developed and delivered with the goal of influencing
people to behave in particular ways, thus allowing better strategic decisions to be made (Ham, et
al., 2009). Using the Theory of Planned Behaviour (TPB), this research aims to explore three
different beliefs, namely, behavioural beliefs, normative beliefs and control beliefs; in order to
derive persuasive communication messages that can encourage better student tutorial attendance.
This paper is structured into four sections. First, literature review will be discussed, followed
with methodology. Findings and discussions are next. Finally, the paper ends with conclusion
and recommendation.
LITERATURE REVIEW
The relatively low number of university students attending tutorials, and the adverse effects of
this phenomenon, has been a much debated topic. There are many suggested reasons for non-
attendance, with accompanying diverse ramifications. What is clear is that past research
suggests a positive relationship exists between tutorial attendance and overall student
performance (Alagiah, et al., 1999).
Kottasz (2005) suggests that student non-attendance in lectures and tutorials is a major concern
for universities. Kottasz found that whilst 57% of students claimed not to have missed their
weekly tutorial, 35% missed an average of one per week and the remaining students did not
attend any tutorials at all. In an effort to understand the trend, students were asked to give
reasons as to why they would miss a tutorial. The major reasons included: the availability of
lecture material, conflicting tutorial times, illness, transport issues, work commitments, and
student indifference.
337
Van Walbeek (2004) indicates that a positive relationship existed between students attending classes
regularly and those consistently receiving above average results. Several other researchers have identified
this trend (Durden and Ellis, 1995; Devadoss and Foltz, 1996; Rodgers, 2001; Marburger, 2001; Paisey
and Paisey, 2004; Gump, 2005; Sauers et al., 2005; Woodfield et al., 2006; Massingham and Herrington,
2006; Halpern, 2007; Chen and Lin, 2008; Newman-Ford et al., 2008; Horn and Jansen, 2009; Credé et
al., 2010).
Van Walbeek concludes that students attending tutorials regularly recorded marginally higher results, yet
those students having other external and internal commitments did not necessarily record poor results.
Dobkin et al. (2010) found that ―class attendance significantly improves student performance‖. The
results confirm that a positive relationship existed between attendance and performance.
However, in contrast to other research, Dobkin et al. (2010) found that the main reason for non-
attendance was not timetable clashes, or travel constraints, but simply that students ‗slept in‘.
Some literature advises tutors to not only encourage tutorial attendance, but in some cases where
performance is lacking, to enforce attendance. To gain above average results, students were
encouraged to attend all tutorials.
METHODOLOGY
166 structured personal interviews were conducted with Latrobe Business students across all
years of study during semester 1, 2011. The 10 minute interviews used questions designed
around the Theory of Planned Behaviour (TPB) and derived from the three differing types of
beliefs - Behavioral, Normative and Control as depicted in Figure 1 below.
FIGURE 1. THEORY OF PLANNED BEHAVIOUR-
338
According to TPB, human action is guided by three considerations:
(1) Behavioral beliefs, or what people believe to be the likely outcomes or consequences of a
given behavior, and their positive or negative judgment about each of these possible
outcomes;
(2) Normative beliefs, or how people believe others of importance to them would want them to
behave, and their motivation to comply with the wishes of these important others ;
(3) Control beliefs, or beliefs about the presence of situational and internal factors that make
the behavior easy or difficult to do, and how much each factor facilitates or inhibits
performing the behavior.‖ (Ham et al, 2009).
The Theory of Planned Behavior attempts to predict behavior by analysing these three beliefs.
TPB ―provides a useful conceptual framework for dealing with the complexities of human social
behavior. The theory incorporates some of the central concepts in the social and behavior
sciences, and it defines these concepts in a way that permits prediction and understanding of
particular behaviors in specified contexts.‖ (Azjen, 1991). Both compliers and non-compliers
made up the sample of individuals who were surveyed. A complier in this case was someone
who attended as many tutorials as they possibly could. A non complier attended only some
tutorials, if any at all. From our sample, 47.6% of students were compliers and 52.4% were non-
compliers. Thus a complier is someone who plays by the rules and 'complies' with regulations to
attend tutorials.
FINDINGS AND DISCUSSIONS
TABLE 1: RESPONDENT PROFILE
Categories Compliers Non-compliers Total
Gender Male 40 (50.63%) 39 (44.83%) 79 (95.46%)
Female 39 (49.37%) 40 (45.98%) 79 (95.34%)
Total 79 87 166
Age age 17-20 36 (45.57%) 41 (47.13%) 77 (92.70%)
age 21-25 33 (41.77%) 40 (45.98%) 73 (87.75%)
age 26-30 8 (10.13%) 5 (5.75%) 13 (15.87%)
age 30+ 2 (2.53%) 1 (1.15%) 3 (3.68%)
Year of study First 13 (16.46%) 14 (16.09%) 27 (32.55%)
Second 35 (44.30%) 25 (28.74%) 60 (73.04%)
Third 27 (34.18%) 43 (49.43%) 70 (83.60%)
Fourth 4 (5.06%) 5 (5.75%) 9 (10.81%)
Number of subjects 2 3 (3.80%) 1 (1.15%) 4 (4.95%)
3 9 (11.39%) 15 (17.24%) 24 (28.63%)
4 63 (79.75%) 68 (78.16%) 131 (157.91%)
4+ 4 (5.06%) 3 (3.45%) 7 (8.51%)
Where you live? On campus 14 (17.72%) 11 (12.64%) 25 (30.37%)
339
Off campus renting 31 (39.24%) 42 (48.28%) 73 (87.52%)
Off campus with parents 29 (36.71%) 33 (37.93%) 62 (74.64%)
Other 4 (5.06%) 1 (1.15%) 5 (6.21%)
Employment Not working 28 (35.44%) 22 (25.29%) 50 (60.73%)
Casual 30 (37.97%) 35 (40.23%) 65 (78.20%)
Part- time 19 (24.05%) 26 (29.89%) 45 (53.94%)
Full time 2 (2.53%) 4 (4.60%) 6 (7.13%)
Working hours 0 28 (35.44%) 22 (25.29%) 50 (60.73%)
0 - 14 31 (39.24%) 32 (36.78%) 63 (76.02%)
15 - 30 19 (24.05%) 28 (32.18%) 47 (56.23%)
31 - 46 1 (1.27%) 4 (4.60%) 5 (5.86%)
47 + 0 (0.00%) 1 (1.15%) 1 (1.15%)
1. What do you see as the good things that could occur by attending your chosen tutorial
this semester?
Question 1 Behavioral Beliefs
Belief Label
Compliers (n=79)
Non-compliers (n=87)
1 Achieving better results 11 (14%) 28 (32%)
2 Good source of assignment / exam Prep 15 (19%) 5 (6%)
3 Achieving better understanding of course content 20 (25%) 12 (14%)
4 Gaining a sounder knowledge 12 (15%) 21 (24%)
5 Having access to help 6 (8%) 10 (11%)
6 Developing a good attendance record 3 (4%) 4 (5%)
7 Developing a good work ethic 12 (15%) 7 (8%)
Total 79 87
The above table represents a condensed set of behavioural beliefs relating to the good things that
compliers and non-compliers perceive will result from attending their tutorials.
Responses have been grouped into seven general beliefs, ranging from ‗achieving better results‘
to ‗developing good work ethic‘. Compliers and non-compliers differed as to what they
perceived as the benefits of attending tutorials.
Perhaps surprisingly, 32% of non-compliers listed the good things about attending tutes as
‗achieving better results‘, compared to only 14% of compliers. These non-compliers may display
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a certain level of denial on their part. Whilst they acknowledge that attending tutorials would
result in better results, they don‘t, or are unable to, do anything about it.
Compliers saw a strong benefit gained of ‗achieving a better understanding of course content‘.
Non-compliers did not. This may indicate a maturity of thought in that compliers see tutorial
attendance as a ―means to an end‖. There was a significant difference in the belief that tutorials
are a good source of exam and assignment preparation. 19% of compliers listed this as a benefit
of tute attendance, yet only six percent of non-compliers thought the same way.
2. What do you see as the bad things that could occur by attending your chosen tutorial this
semester?
Question 2 Behavioral Beliefs
Belief Label
Compliers (n=79)
Non-compliers (n=87)
1 Time Wasted 19 (24%) 30 (34%)
2 Boredom 12 (15%) 13 (15%)
3 Tutorial Times 5 (6%) 12 (14%)
4 Work Commitments 8 (10%) 8 (9%)
5 Friends / Social Obligations suffer 14 (18%) 6 (7%)
6 Poor Results 6 (8%) 4 (5%)
7 Poor Tutor / Lecturer Abilities 9 (11%) 9 (10%)
8 None 6 (8%) 5 (6%)
Total 79 87
In contrast to the previous table, some beliefs about what students perceive as being the ‗bad
things‘ to occur by attending tutorials, are more convergent between compliers and non-
compliers. For example, beliefs two, four, six, seven and eight are all relatively close in their
percentages. This suggests that both compliers and non-compliers possess similar beliefs about
the negative outcomes - perhaps because both groups have experienced, or observed, this.
In contrast, the beliefs of ‗time wasted‘, ‗tutorial times‘ and ‗friends/social obligations‘ reveal
quite different results. These results suggest very different sets of beliefs and may be due to the
fact that compliers choose to spend their time in tutorials, and do not view this time as wasted.
For the category titled ‗friends/social obligations‘, an 11% differential exists between compliers
and non-compliers. 18% of compliers believe trading off friends and social obligations to be a
bad thing, whereas only seven percent of non-compliers felt the same way. This is an interesting
anomaly in the data, one that seems to defy reasoning. One would expect that friends would be
more important to non-compliers - and hence justify their skipping tutorials. However the data
indicates the opposite. Possible reasons for this could be that non-compliers‘ friends are also
non-compliers; therefore they too are not attending the tutorials.
NORMATIVE BELIEFS
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3. Who (individuals or groups whose opinions you consider personally influential) do you
think would support or approve of you attending tutorials this semester?
Question 3 Normative Beliefs
Belief Label
Compliers (n=79)
Non-compliers (n=87)
1 Classmates 2 (3%) 0 (0%)
2 Everyone 19 (24%) 20 (23%)
3 Friends 2 (3%) 3 (3%)
4 Family 28 (35%) 20 (23%)
5 Family & Friends 9 (11%) 14 (16%)
6 Lecturer 4 (5%) 10 (11%)
7 Lecturer & friends 0 (0%) 5 (6%)
8 Lecturer & family 6 (8%) 12 (14%)
9 Work 0 (0%) 2 (2%)
10 Myself 5 (6%) 0 (0%)
11 Possibly employers 1 (1%) 0 (0%)
12 N / A 3 (4%) 1 (1%)
Total 79 87
Interestingly, for the first question of the normative beliefs, the results were very similar between
compliers and non-compliers. Of particular note is the salient beliefs of ‗everyone‘ and ‗family‘
as these were the most approving of tutorial attendance. The belief ‗everyone‘ is divided into
three categories and must fulfil all 3 for it to be classed as ‗everyone‘. This category is a
combination of differing types of salient beliefs - friends, family and lecturer. Students
predominantly put down all three, if they had any at all, thus indicating that there was strong
encouragement coming from all three aspects of the significant others, whether they were
compliers (24%) or non compliers (23%). ‗Family‘ as a sole response was also high, perhaps
indicating that there was significant encouragement for both compliers and non compliers to
attend tutorials.
4. Who (individuals or groups whose opinions you consider personally influential) do you think
would object or disapprove of you attending your chosen tutorial this semester?
Question 4 Normative Beliefs
Belief Label
Compliers (n=79)
Non-compliers (n=87)
1 Friends 19 (24%) 23 (26%)
2 Family 2 (3%) 5 (6%)
3 Family & Friends 2 (3%) 1 (1%)
4 Work 3 (4%) 7 (8%)
5 Work & friends 4 (5%) 3 (3%)
6 Myself 4 (5%) 1 (1%)
7 N / A 40 (51%) 40 (46%)
8 Other 5 (6%) 7 (8%)
Total 79 87
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The second normative belief question centred around who would disapprove or object to the
student attending tutorials. The main contributors to this factor were the salient beliefs of
‗friends‘ and ‗Not Applicable. Answers here as to who would disapprove, were notably similar
between compliers and non-compliers.
CONTROL BELIEFS
5. What factors or circumstances enable or make it easy for you to attend your chosen
tutorial this semester?
Question 5 Control Beliefs
Belief Label
Compliers (n=69)
Non-compliers (n=78)
1 Own car, convenient timetable 5 (7%) 6 (8%)
2 Convenient timetable 20 (29%) 20 (26%)
3 Friends are there 6 (9%) 9 (12%)
4 Have own car 4 (6%) 0 (0%)
5 Proximity to home 18 (26%) 29 (37%)
6 Compulsory 2 (3%) 2 (3%)
7 Family approval 1 (1%) 0 (0%)
8 Learning 5 (7%) 3 (4%)
9 Good grades 4 (6%) 2 (3%)
10 Not working 2 (3%) 0 (0%)
11 N / A 2 (3%) 1 (1%)
12 Motivation 0 (0%) 1 (1%)
13 Late start 0 (0%) 5 (6%)
Total 69 78
Students were asked ‗What enables you to attend tutorials?‘ Reasons given were grouped in a
narrow band of responses.
The main reason was having a ‗convenient timetable‘. A convenient timetable included factors
such as classes being blocked together; and classes being on convenient days or at convenient
times of the day. The second highest reason was ‗close proximity to home‘; where students felt
that it was easy to attend tutorials if they lived only a short distance from the university.
6. What factors or circumstances make it difficult for you to attend your chosen tutorial
this semester?
Question 6 Control Beliefs
Belief Label
Compliers (n=68)
Non-compliers (n=76)
1 Waste of time 6 (9%) 7 (9%)
2 Clashes with other subjects 4 (6%) 4 (5%)
3 Distance 10 (15%) 6 (8%)
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4 Inconvenience 15 (22%) 0 (0%)
5 Family commitments 2 (3%) 1 (1%)
6 Financial issues 9 (13%) 11 (14%)
7 Heavy work load 4 (6%) 2 (3%)
8 Illness 2 (3%) 0 (0%)
9 No motivation 5 (7%) 4 (5%)
10 Personal reasons 3 (4%) 0 (0%)
11 N / A 1 (1%) 1 (1%)
12 Other commitments 0 (0%) 7 (9%)
13 Bad timetable 0 (0%) 24 (32%)
14 Friends 0 (0%) 4 (5%)
15 Parking is difficult 7 (10%) 5 (7%)
Total 68 76
Compliers identified a variety of factors that made it difficult for them to attend tutorials. The
main reason was ‗inconvenience‘ with 22% of students indicating this. When probed further,
inconvenience was found to include poorly structured timetables and clashes with other
commitments.
Ten students stated that it was difficult to attend tutorials due to the university‘s distance from
their homes. Nine students found it difficult to attend due to financial issues, meaning they
needed to work during the times their tutorials were being held.
Of the 76 students who didn‘t attend tutorials, 32% of these blamed a poorly structured
timetable. This finding indicates that both compliers and non-compliers find well-structured
timetables and living close to university to be the main factors making it easier for them to attend
tutorials. Compliers and non-compliers both too had issues with the university‘s parking
arrangements. Students found it difficult to attend tutorials if they couldn‘t find a car park.
Financial issues were another significant reason for not attending tutorials. In these cases, student
were identifying their need to put work before study.
CONCLUSION AND RECOMMENDATION
Conclusions There are many reasons why students are reluctant to attend tutorials. The aim of this research
has been to explore different beliefs, namely, behavioural beliefs, normative beliefs and control
beliefs; in order to derive persuasive communication messages that can encourage better student
tutorial attendance. Beliefs have differed, sometimes significantly, between compliers and non-
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compliers. However, several unexpected similarities also emerged. ‗Achieving better results‘
was found to be the most significant outcome for non-compliers in the good things that occur
from attending tutorials. However, it was found that ‗gaining a higher knowledge and
understanding‘ was the main driver in tutorial attendance for compliers. In comparison, the
results for the ‗bad things that could occur by attending tutes‘ was relatively similar between
compliers and non-compliers. ‗Time wasted‘ was the biggest consideration for both. The control
indicated that the determinants making it easier to attend tutorials included ‗proximity to
university‘ and ‗a convenient timetable‘. In comparison, the determinants that made it difficult to
attend were ‗distance travelled‘ and ‗inconvenient tutorial times‘.
Recommendation for persuasive communication messages Many salient beliefs have emerged for all three of the behavioural, normative and control beliefs.
These beliefs include: ‗achieving better results‘, ‗gaining a sounder knowledge‘,
‘assignment/exam prep‘, and ‗being encouraged via friends, family and tutors‘. ‗Having a
convenient timetable‘ and ‗living close by‘ was shown to affect student tutorial attendance.
From the student responses, persuasive communication messages can be created with the end in
mind to improve student tutorial attendance.
The major persuasive communication messages that need to be promoted include the increased
performance that the students will achieve, from attending tutorials, and the positive
encouragement they will receive from their significant others.
Based on the outcomes from this research, below are examples of possible persuasive
communication messages that could be used to motivate students to attend tutorials:
1. Attend Tutorials and Achieve Greatness!
Attending tutorials not only gives me good results; it also teaches me
valuable habits that will help me succeed in other areas.
2. Your family, friends and lecturers all want you to attend
your tutes!
3. Whose time are you really wasting by not attending
tutorials?
Accompanying this text is the picture of a man about to smash his
phone on the ground in frustration at a lost opportunity. This message
evokes images of how frustrating it is when things don‘t work out.
Good grades can be achieved by tutorial attendance!
4. Attending tutorials not only gives me the knowledge I need,
it also shows me how far I have come!
5. I’m only hurting myself if I don’t attend tutorials
6. Learning important things will never be a waste of time for
me.
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THE EFFECTS OF TEACHING QUALITY ON STUDENT SATISFACTION AND
BEHAVIOURAL INTENTIONS FROM VIEWPOINT OF UNIVERSITY STUDENTS.
Saripah Basar
Research and Innovation Department
University College Shahputra, Malaysia
ABSTRACT
This study focuses on the effects of teaching quality on student satisfaction and behavioural
intentions, with an emphasis on students’ experiences from university. Data were collected
from168 students of the university. Using partial least square structural equation modelling
(PLS-SEM) tool, the hypothesized effects among the constructs were tested empirically. The
results indicate that the path coefficients from tangible, empathy and outcome constructs are the
key factors that influence student perception of service quality. Also, the path coefficient from
service quality of student experience to student satisfaction was significant. In addition, students
who are satisfied are more positive in their behavioural intentions toward university compared
to the unsatisfactory students. The limitation of the study is due to the nature of the sample which
only involved university students; the findings are limited to be generalized to other institutions.
It is recommended that the similar study to be carried out at other higher educational institutions
and the findings can be used by the private institutions to convince prospective students.
Keywords: Higher education institution, Service quality, student satisfaction, Behavioural
intention.
INTRODUCTION
Customer satisfaction is a very important marketing concept. Strong competition in today‘s
competitive educational market forces higher educational institutions in Malaysia to adopt
market orientation strategy to differentiate their offering from those of their competitors (Sirat,
2005). Thus, the administrators of higher education institution need to understand the target
customer needs in order to boost the customer satisfaction. In higher education, the term
―customer‖ is different from that in other industries since groups such as students, employers,
academic staff, government and families are all customers of the education system with a variety
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of needs. However, students are the direct recipients of the service provided by the higher
education institution. Continuous improvement in quality has become an extremely important
issue for higher educational institution to enhance educational value and to increase satisfaction
among students and stakeholders. As the core service provided by higher educational institution
is teaching, the instructors or academic staffs are considered playing a vital role as service
provider to the students as their customer. Thus, higher educational institution should put highest
priority on effective teaching provided by instructors and engages student in the learning process.
Student satisfaction is often used to assess quality of service provided by the institution
(Standifird, 2008; and Qureshi et al. 2010).
Many studies had been conducted on the perception of teaching service quality at higher
education level, however, the studies were mostly performed in the developed countries (Nasser
and Abouchedid, 2005). There were no consensus on the factors that contributes significantly to
service quality, student satisfaction or behavioural intentions is determined. The study on student
evaluations of service encounters, technical and functional service quality, service satisfaction,
and affects on behavioural intentions is not well documented. Very limited studies of such affect
have been conducted in developing countries, including Malaysia. Therefore, the present study
was conducted to determine the effects of teaching quality delivered by instructors/lecturers on
student satisfaction and behavioural intentions, with an emphasis of the view from university
students.
LITERATURE REVIEW
Customer satisfaction with service quality is a major goal in service organizations. Service
providers cannot detach from this general concern, managers and practitioners must address
quality and customer satisfaction issues as a priority. Service quality is basically difficult to
define and measure and has been subject to much debate (Legčević, 2009). Thus, the concepts of
perceived quality and related customer satisfaction are coined out. Service quality in general
differs from product quality due to special characteristics including intangible, simultaneity and
heterogeneity (Parasuraman et al. 1985). Service quality is more difficult for the consumer to
evaluate than goods quality, moreover quality evaluations are not made exclusively on the
outcome of service but rather they involve evaluations of the process of service deliver
(Parasuraman et al. 1985). Intangible, this is principal feature of higher education since most
quality attributes cannot be seen, felt, or touched in advance and the production and consumption
of the service are performed simultaneously. That is, personal contact between students and
instructors plays an important role in the service action. Consequently, the student contributes
directly to the quality of service delivered, and to his or her satisfaction or dissatisfaction.
Service quality in higher education especially instructors‘ teaching ability varied depend on
many factors, most important service quality is context specific. Driscoll and Cadden (2010) in
their research of teaching effectiveness found that instructor‘s teaching ability is influenced by
the department that offers the course, course‘s requirement- core or elective, and the students‘
anticipated grade.
The development of quality management in the education sector still considered new compared
with other sectors (Ramseook-Munhurrun, et al., 2010). Attention on service quality in the
education setting increasing due to the demand for excellence in education (Sahney et al. 2004).
In recent years, numerous empirical studies on higher education have shown several examples of
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the successful use of systematic quality management in education (Lagrosen et al. 2004;
Stodnick & Rogers, 2008). Ling et al. (2010) conducted a study on 458 undergraduate business
students from a private university to evaluate the determinants of perceived service quality of
higher education institution and found that contact personnel, access to facilities, cost of courses
offered, physical facilities of the tertiary institution and resource input model of education
quality were positively related to the overall students‘ perceived service quality. However,
according to Douglas et al.. (2006) the most important aspects of service quality in HEI are those
associated with teaching and learning.
Dimension of service quality
The quality dimensions can be classified into technical quality and functional quality (Gronross,
1998). The technical quality or outcome quality of the process can be measured objectively; it is
practical result of service. The functional quality or process quality dimension is often perceives
in subjective manner and is related to the interaction between service provider and customer
(Gronross 2001). Functional quality can be divided into tangible and intangible; the intangible
aspects of quality include reliability, responsiveness, assurance, and empathy (Banwet and Datta,
2003). The functional quality very much relate to higher education service where influence of
interaction between instructors and students very significant. Due to the abstract nature of the
concept of service quality and the characteristics of the service, measuring service quality
appears to be a complicated and difficult evaluated (Sultan and Wong, 2010; Hoffman and
Bateson, 2006; and Parasuraman et al. 1985).
Service quality is generally defined as a consumer‘s perceived or impress about superiority of an
entity (Cronin and Taylor, 1992: Parasuraman et al.1985, 1988: and Bitner and Hubbert, 1994).
Moreover, service quality is context-specific (Dagger et al. 2007; and Sultan and Wong, 2010)
such as it depends on nature of work, environment, and culture. Thus, it is attached to different
meanings and inferences depend on contexts. There is no conclusive definition of service quality.
In order to define service quality in the right perspective, it is vital to study in the context of
service being study (Lagrosen et al. 2004). In complement to that, to comprehend the service
quality in educational sector, we must have strong understanding of service quality attributes in
other sectors and to do some adaptation as necessary.
Approach in measuring service quality
A number of researchers have provided lists of service quality dimensions, but the best known
service quality dimensions is SERVQUAL developed by Parasuraman et al. (1985, 1988). The
SERVQUAL is based on the assumption that customers are able to express their expectation of
service quality and could distinguished these from their perception of the actual service quality
being provided; the instrument is based on the different between perception and expectation
(Parasuraman et al. 1985, 1988). While the SERVQUAL instrument has been widely used, it has
been subjected to certain criticisms. SERVQUAL has been much criticised over the years
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(Cronin and Taylor, 1992; and Asubonteng et al. 1996). Cronin and Taylor (1992) disagreed
with the concept of perception minus expectation and propose alternative measurement,
SERVPERF which utilize the perception only in the service quality model. Both of SERVQUAL
and SERVPERF are based on the dimensional approach to service quality (Sultan and Wong,
2010); service dimensions are conceptualized as component of service quality construct.
Whereas, Dabholkar et al. (2000) determine service dimensions as antecedents to overall service
quality construct. However, the antecedents concept is more acceptable and being used lately
(Dagger and Sweeney, 2006; and Dagger, Sweeney and Johnson, 2007). Accordingly, there
seems to be consensuses among researchers that satisfaction and service quality are two
distinctive construct; however, dissimilarity in their definitions are not always clear (Choi et al.
2004; Chen and Ting, 2002; and Spreng and Mackoy, 1996). Parasuraman et al. (1988) and
Bitner (1990) argued that customer satisfaction is an antecedent of service quality. On the other
hand, many researchers (such as Cronin and Taylor, 1992; Dabholkar et al., 2000; and Dagger
and Sweeney, 2006) believed that it is service quality that leads to customer satisfaction.
As such, selecting a reliable method to assess the service quality is very important. There are
many models and instruments developed and used in research to determine service quality in
higher educational institution. Many researchers have adapted SERVQUAL scale (Parasuraman
et al. 1985, 1988), SERVPERF (Cronin and Taylor, 1992), and HEdPERF (Abdullah, 2005) are
among the service quality models that have been used to measure higher educational service
quality by incorporating student satisfaction into their survey instrument.
The present study, as mentioned before sets out to diagnose the effects of service quality
dimensions on perceive service quality (teaching), student satisfaction and behavioural intentions
of the students in the classroom environment by using an integrated scale. The study takes the
view, that perceived service quality is led to satisfaction, in agreement with the empirical
research by Cronin and Taylor (1992); and Dagger and Sweeney, (2006). The following section
discusses briefly the literature of the integrated model dimensions or constructs as developed by
previous researchers.
Behavioural intentions
Studies showed that perceived service quality and service satisfaction have mixed impact on
behavioural intentions. Many researchers (such as Cronin and Taylor 1992; and Dabholkar et al.
2000) have found that service quality is indirectly related to behavioural intentions through
service satisfaction as mediating variable. However, Cronin, Brady, and Hult 2000 in their study
found that service quality is directly impact on behavioural intentions. Accordingly, students‘
intention to re-attend or recommend lectures is dependent on their perceptions of quality and the
satisfaction they received from attending previous lectures (Banwet and Datta, 2003).
Student satisfaction
Kim et al. (2008) describe customer satisfaction as results from customers having good
experiences. Ott and van Dijk (2005) assert that customer satisfaction is an important indicator of
the performance of an organization. According to Storbacka et al. (1994), a satisfied customer
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creates a strong relationship with the provider and this leads to customer retention or customer
loyalty and generates steady revenues and profit to the firm. When service quality increases,
correspondingly, the satisfaction with the service will increase and intentions to reuse the service
is also increase (Dagger, Sweeney, and Johnson 2007). Number of studies has confirmed that
service quality is an antecedent to customer satisfaction (Cronin and Taylor, 1992; Dabholkar et
al. 2000; and Dagger and Sweeney, 2006). Satisfaction is an affective, feeling-based, and
subjective therefore, satisfaction is hard to measure accurately (Dabholkar et al. 2000).
Thus, customer satisfaction as mention above is a critical factor that determines the quality of the
product or service. In higher education sectors, student satisfaction is considered to be an
indicator of service quality delivered (Wiers-Jenssen et al. 2002). Wiers-Jenssen et al. (2002)
conducted a study in Norway to determine student satisfaction in relation to learning experience.
They found that the academic and pedagogic quality of teaching are important factors in
determining student satisfaction. Banwet and Datta (2003) in their study believed that satisfied
students are likely to attend another lecture delivered by the same instructor or opt for another
module or course taught by the instructor. However, social climate, aesthetic aspects of the
physical infrastructure and the quality of services from the administrative staff are also
influencing overall student satisfaction (Rapert et al. 2004; and Diamantis and Benos 2007).
Perceived service quality
Customers usually have some expectations of service provided by providers before receiving the
actual service and this expectations will be compared to the actual perception of the provided
service p. The degree and gap between service perception and customer expectations is defined
as service quality (Parasuraman et al. 1985, 1988). Thus, perceived service quality in classroom
teaching can be described when lecturer meets or exceeds students‘ expectation. Accordingly,
Thai students place teaching and ability of lecturers to communicate skilfully is very important
attributes in selecting international universities (Srikatanyoo and Gnoth, 2005). Meanwhile,
student satisfaction with the services offered at a university is influenced by students‘ perceived
service quality (Gruber et al., 2010).
Service quality dimensions
Reliability
The reliability dimension is one the strongest effect on perceived lecture quality (Banwet and
Datta, 2003). Reliability in teaching refers to instructors‘ ability to deliver the lecture
dependably, accurately, and consistently (Stodnick and Roger, 2008). Accordingly, the ability of
instructor to deliver lecture clearly, emphasis on the relevance and practicality of the subject, the
punctuality of the instructor, and the instructor‘s sincerity and problem solving ability are rated
as very important factors contributing to a superior teaching quality (Banwet and Datta, 2003).
Responsiveness
Instructor responsiveness is important dimensional of student perception toward teaching quality.
Among the indicators of responsiveness that students expect from the instructors are: respond
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promptly when needed; willing to go out of his or her way to help students; always welcomes
student questions and comments; availability and approachability outside class hours (Stodnick
and Roger, 2008; Banwet and Datta, 2003).
Empathy
The instructors or faculty members‘ empathy and understanding of students‘ problems and needs
can greatly influence perceived service quality. Students are desired by the faculty members to
be attentive and understanding towards them. According to Stodnick and Roger (2008) and
Banwet and Datta (2003), the faculty member must be: genuinely concerned about the students;
understood the individual needs of students; put the student‘s best long-term interests in mind;
and encourages and motivates students to do their best. This reflects faculty members‘ empathy.
Tangibles
Physical evidence of the college or university will provide first impression of service quality and
it is very important to student perceived service quality judgments. Generally, good appearance
of the physical facilities, equipment, personnel and written materials create positive impressions.
A clean and organized appearance of a college or university, its staff, its premises, restrooms,
equipment, classrooms, workshops, laboratories, library, computer and information systems can
influence students‘ impressions about the college or university. Jones et al. (1996) study on
international business students attending colleges and universities in the United States and found
that tangibles is one of the most important factors in their assessment of educational service
quality. Tangibles aspect such as classroom environment, quality of presentations and the
appearance of instructors are influenced on students‘ perception of teaching quality (Banwet and
Datta, 2003; Markovic, 2006; and Hill and Epps, 2010). In numerous of studies students
considered tangibles a very important factor to their satisfaction with educational service quality
(Arambewela and Hall, 2006; Markovic, 2006; and Banwet and Datta, 2003).
Outcome
Outcome or technical quality is also a vital dimension that affects perception of service quality
by students. The technical dimension is rated the most important factor contribute to perception
of service quality by students in Banwet and Datta, (2003) study. In their survey of 168 students
who attended four lectures delivered by the same instructors, they found that students placed
more importance on the outcome of the lecture than any other dimension. The outcome or
technical quality in this study refer to: knowledge and skills gained during lecture, availability of
class notes and reading materials, instructor‘s feedback on assessed work, and coverage and
depth of lecture (Banwet and Datta, 2003).
THEORETICAL FRAMEWORK
A theoretical framework for teaching service quality is developed based on literature review and
discussions presented above. Figure 1 shows the service quality dimensions, namely, tangibles,
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responsiveness, reliability, empathy, and outcome; perceive service quality construct; student
satisfaction construct; and behavioural intentions construct. All the constructs/dimensions have
been explained in the above section.
FIGURE 1: TEACHING SERVICE QUALITY FRAMEWORK
Source: adapted from Dabholkar et al. (2000).
Hypotheses
Prior discussion has led to a brief examination of the existing literature and the resultant research
gaps led to the development of the hypotheses in this research. The eleven hypotheses are:
H1: Tangibles dimension is positively related to the students‘ perceived service quality.
H2: Responsiveness dimension is positively related to the students‘ perceived service quality.
H3: Reliability dimension is positively related to the students‘ perceived service quality.
H4: Empathy dimension is positively related to the students‘ perceived service quality.
H5: Outcome dimension is positively related to the students‘ perceived service quality.
H6: Students‘ perceived service quality is positively related to student satisfaction.
H7: Student satisfaction is positively related to behavioural intentions.
H8: Student satisfaction mediates the relationship between perceived service quality and
behavioural intentions.
METHODOLOGY
Instrument
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Basically the instrument was adapted from Dabholkar et al. (2000). The instrument has been
modified in order to suit the context of the study. However, the service quality dimensions: that
is functional quality aspects of service for this study have been adopted from SERVQUAL
(Parasuraman, 1988); and a technical dimension, that is, outcome has been adopted from Banwet
and Datta (2003). The perceived service quality, student satisfaction, and behavioural intentions
constructs have been adopted from Dabholkar et al. (2000). All the items in this study adapted or
adopted from previous studies, namely, Stodnick and Roger (2008), Banwet and Datta (2003),
Markovic (2006), and Dabholkar et al. (2000). To establish support for content validity a panel
of lecturers reviewed the constructs and the initial set of measure items. Based on their
suggestions few items were changed in the wording and no item deleted. This study adapted 7-
point Likert-type scale to assess the model. All constructs were reflective since the items reflect
the meaning of the construct. Reflective indicators mean they are measured the same underlying
phenomenon (Chin, 1998).
Sample
The population for this study was students enrolled in the university. The sampling unit was
included all the current full-time students at the university. The university is one of the newly
established universities in Malaysia; with an estimated student population of 3,000 pursuing 20
programs in five faculties. Students who have completed at least one semester of their study were
selected as sample because they are familiar with the teaching style and services provided at the
university as compared to the first semester students. The general rule for the minimum number
of respondents or sample size is five-to-one ratio of the number of independent variables to be
tested. Hair et al. (2010) suggested that the acceptable ratio is ten-to-one. Since there are 7
independent variables in this study, a minimum sample size of 70 respondents would be
appropriate.
The self-administer surveys questionnaire were randomly distributed to the students during class
hours by research team. The time allocated for the students to answer the questionnaire is 15
minutes. The verbal consent from each student was requested before they answer the survey
questionnaire. The confidentiality of each set of questionnaire was guaranteed with diligence; the
questionnaires do not contain any of the names of the students. Consequently, this study was able
to collect 168 samples. Therefore, the response rate achieved was considered adequate for the
study.
Results and Data analysis
This study used partial least square structural equation modelling (PLS-SEM) tool to evaluate the
manner in which the constructs presented in Figure 1 might relate to each other. The PLS-SEM
technique is statistical method that has been developed for the analysis of latent variable
structural models involving multiple constructs with multiple indicators. PLS-SEMs have a
number of potential strengths, including ability for the testing of the psychometric properties of
355
the scales used to measure a variable, as well as the strength and the direction of relationships
among the variables (Akter et al. 2011).
The PLS-SEM is consisted of two sets of testing equations: first, the assessment of measurement
model, which is the process of calculating the item reliability, validity; the second, the
assessment of the structural model, which is the method of determine the appropriate nature of
the relationships (paths) between the measures and constructs. The estimated path coefficients
indicate the sign and the power of the relationships while the item‘s weights and loadings
indicate the strength of the measures (Hair, Ringle and Sarstedt, 2011). The confirmatory factor
analysis was first conducted to assess the measurement model; then, the structural relationships
were examined (Anderson and Gerbing 1988; Hair et al.. 2010).
Measurement Model
The two main criteria used for testing measurement model are validity and reliability. The
reliability of a research instrument concerns the extent to which the instrument produces
consistency results in repeated measurements, whereas validity is the degree to which a test of
how well an instrument that is developed measures and what is supposed to measure (Sekaran
and Bougie 2010). To validate our measurement model, three basic approaches to validity were
assessed: content validity/construct validity, convergent validity, and discriminant validity.
Construct validity
Construct validity refers to whether the instruments correlates with the theories around which the
test is designed (Sekaran and Bougie 2010). Construct validity is assessed, first, by looking at the
respective loadings and cross loadings if there are problems with any particular items (Table 1).
The cut off value of 0.5 for loadings is considered significant (Hair et al.. 2010). Item which has
loading value of higher than 0.5 and having cross loading was deleted. In this current study, four
items were deleted due to low loading value and cross loading (Hulland, 1999). Two items from
Tangible construct and one item from Service Quality construct were deleted due to low loading
value and one item from Satisfaction construct was deleted due to cross loading. As the result
Table 1 shows all the items measuring a particular construct loaded highly on that construct and
loaded lower on the other constructs thus confirming construct validity.
TABLE 1 LOADINGS AND CROSS-LOADING
Item 1 2 3 4 5 6 7 8
T1 0.762 0.244 0.116 0.156 0.148 0.380 0.350 0.393
T3 0.857 0.336 0.177 0.246 0.228 0.453 0.416 0.392
T4 0.655 0.285 0.239 0.111 0.274 0.302 0.237 0.236
E1 0.300 0.729 0.349 0.232 0.394 0.358 0.226 0.166
E2 0.364 0.817 0.396 0.247 0.517 0.418 0.298 0.264
E3 0.250 0.812 0.443 0.364 0.489 0.408 0.428 0.385
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E4 0.206 0.780 0.553 0.433 0.599 0.382 0.384 0.255
E5 0.339 0.754 0.489 0.290 0.494 0.479 0.449 0.319
REL1 0.328 0.543 0.864 0.565 0.688 0.511 0.504 0.293
REL2 0.207 0.549 0.876 0.545 0.637 0.421 0.434 0.257
REL3 0.126 0.492 0.908 0.598 0.702 0.371 0.372 0.165
REL4 0.063 0.438 0.825 0.556 0.664 0.354 0.347 0.129
REL5 0.178 0.416 0.799 0.467 0.499 0.412 0.339 0.163
OC1 0.191 0.371 0.548 0.758 0.433 0.409 0.298 0.207
OC2 0.213 0.235 0.523 0.845 0.392 0.437 0.397 0.271
OC3 0.176 0.357 0.573 0.898 0.454 0.563 0.466 0.239
OC4 0.202 0.364 0.494 0.824 0.453 0.592 0.488 0.303
RES1 0.250 0.580 0.615 0.450 0.856 0.361 0.385 0.234
RES2 0.164 0.574 0.625 0.439 0.866 0.381 0.318 0.178
RES3 0.153 0.472 0.658 0.461 0.830 0.343 0.310 0.128
RES4 0.337 0.551 0.643 0.430 0.849 0.494 0.419 0.282
SQ1 0.383 0.469 0.448 0.534 0.410 0.888 0.587 0.393
SQ3 0.495 0.468 0.404 0.537 0.402 0.930 0.611 0.482
SQ4 0.478 0.497 0.479 0.584 0.467 0.887 0.653 0.521
S1 0.264 0.417 0.412 0.371 0.353 0.473 0.835 0.630
S2 0.464 0.367 0.300 0.395 0.308 0.610 0.899 0.726
S3 0.430 0.419 0.421 0.478 0.363 0.712 0.889 0.640
S5 0.330 0.376 0.523 0.483 0.454 0.481 0.734 0.421
BI1 0.393 0.250 0.168 0.230 0.172 0.497 0.655 0.862
BI2 0.491 0.330 0.225 0.252 0.221 0.472 0.696 0.915
BI3 0.373 0.410 0.353 0.349 0.348 0.423 0.655 0.867
BI4 0.307 0.252 0.129 0.243 0.155 0.412 0.563 0.854
BI5 0.402 0.332 0.174 0.273 0.181 0.456 0.602 0.862 Bold values are loading of items which are above the recommended value of 0.5
Convergent validity
When multiple items are used for an individual construct, the researcher should be concerned
with the extent to which the items demonstrate convergent validity. The measurement model was
tested for convergent validity which is the degree to which multiple items to measure the same
concept are in agreement. Anderson and Gerbing (1988) stated that convergent validity is
established if all factor loadings for the items measuring the same construct are statistically
significant. According to Hair et al. (2010) convergence validity should be accessed through
factor loadings, composite reliability and average variance extracted. The loadings for all items
exceeded the recommended value of 0.5 (Hair et al. 2010). Composite reliability (CR) values
(see Table 2), which is a measure of internal consistency, the value ranged from 0.805 to 0.932
357
which exceeded the recommended value of 0.7 (Hair et al. 2010). The average variance extracted
(AVE) measures the variance captured by the indicators relative to measurement error, and it
should be greater than 0.50 to indicate acceptability of the construct (Fornell and Larcker, 1981;
Henseler, Ringle, and Sinkovics, 2009). Table 2 shows that the average variances extracted range
from 0.582 to 0.813, which are above the acceptability value.
TABLE 2 RESULTS OF MEASUREMENT MODEL
Construct Items Loading CR1 AVE
2
Tangible T1 0.762 0.805 0.582
T3 0.857
T4 0.655
Empathy E1 0.729 0.885 0.607
E2 0.817
E3 0.812
E4 0.780
E5 0.754
Reliability REL1 0.864 0.932 0.732
REL2 0.876
REL3 0.908
REL4 0.825
REL5 0.799
Outcome OC1 0.758 0.900 0.693
OC2 0.845
OC3 0.898
OC4 0.824
Responsiveness RES1 0.856 0.913 0.724
RES2 0.866
RES3 0.830
RES4 0.849
Service Quality SQ1 0.888 0.929 0.813
SQ3 0.930
SQ4 0.887
Satisfaction S1 0.835 0.906 0.709
S2 0.899
S3 0.889
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S5 0.734
Behavioural Intention BI1 0.862 0.941 0.761
BI2 0.915
BI3 0.867
BI4 0.854
BI5 0.862
Note: 1. Composite reliability (CR) = (square of the summation of the factor loading)/
{(square of the summation of the factor loading) + (square of the summation of the error variances)}
2. Average variance extracted (AVE) = (summation of the square of the factor loadings)/ {(summation of the square of the factor loadings) + (summation of the error variances)}
Table 3 summarizes the results of the measures in our research model. The results show that all
the constructs, i.e., tangible, empathy, reliability, outcome, responsiveness, service quality,
satisfaction, and behavioural intention are all valid measures of their respective constructs based
on their parameter estimates and statistical significance (Chow and Chan 2008). All measures are
significant on their path loadings at the level of 0.001
TABLE 3 SUMMARY RESULTS OF THE MODEL CONSTRUCT
Construct Items Standardized
Estimate
t-value
Tangible T1 0.762 13.05
T3 0.857 29.28
T4 0.655 8.24
Empathy E1 0.729 14.02
E2 0.817 26.62
E3 0.812 22.86
E4 0.780 17.28
E5 0.754 17.70
Reliability REL1 0.864 35.82
REL2 0.876 33.26
REL3 0.908 37.37
REL4 0.825 21.93
REL5 0.799 19.17
Outcome OC1 0.758 14.27
OC2 0.845 22.80
OC3 0.898 47.93
OC4 0.824 29.01
Responsiveness RES1 0.856 26.12
RES2 0.866 26.28
RES3 0.830 18.25
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RES4 0.849 37.60
Service Quality SQ1 0.888 38.55
SQ3 0.930 78.00
SQ4 0.887 33.14
Satisfaction S1 0.835 18.98
S2 0.899 59.56
S3 0.889 50.54
S5 0.734 14.83
Behavioural Intention BI1 0.862 41.10
BI2 0.915 77.76
BI3 0.867 31.40
BI4 0.854 29.86
BI5 0.862 30.41
Discriminant validity
Next we validated the discriminant validity of our instrument. The discriminant validity is
represented the extent to which measures of a given construct differ from measures of other
constructs in the same model. In a PLS context, the most important criteria for adequate
discriminant validity is that a construct should share more variance with its items than it is
should shares with other constructs in a given model (Hulland, 1999). It was assessed by
examining the correlations between the measures of potentially overlapping constructs. Items
should load more strongly on their own constructs in the model, and the square root of the
average variance extracted for each construct is greater than the levels of correlations involving
the construct (Fornell and Larcker, 1981). As shown in Table 4, the square root of the average
variance extracted for each construct is greater than the items on off-diagonal in their
corresponding row and column, thus, indicating adequate discriminant validity. The inter-
construct correlations also show that each construct shares larger variance with its own measures
than with other measures. In sum, the measurement model demonstrated adequate convergent
validity and discriminant validity.
TABLE 4 DISCRIMINANT VALIDITY OF CONSTRUCTS
Construct 1 2 3 4 5 6 7 8
1. Tangible 0.763
2. Empathy 0.378 0.779
3. Reliability 0.224 0.575 0.855
4. Outcome 0.233 0.401 0.639 0.833
5. Responsiveness 0.277 0.642 0.747 0.522 0.851
6. Service Quality 0.503 0.531 0.492 0.613 0.474 0.902
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7. Satisfaction 0.449 0.466 0.475 0.506 0.427 0.685 0.842
8. Behavioural Intention 0.455 0.362 0.244 0.309 0.250 0.519 0.730 0.872 Diagonals (in bold) represent the square root of the average variance extracted while the other entries represent
correlations.
Reliability analysis
To analyse the reliability/internal consistency of the items, we used the Cronbach‘s alpha
coefficient. Table 5 shows all alpha values are above 0.6 as suggested by Nunnally and Berstein
(1994). Another way to determine internal consistency is by looking at composite reliability
values. The composite reliability values also ranged from 0.805 to 0.932 (Table 2). A composite
reliability of 0.70 or greater is considered acceptable (Fornell and Larcker 1981). As such we can
conclude that the measurements are reliable.
TABLE 5 RESULT OF RELIABILITY TEST
Construct Cronbachs Alpha
Number of
items
Tangible 0.638 3(5)
Empathy 0.838 5(5)
Reliability 0.908 5(5)
Outcome 0.853 4(4)
Responsiveness 0.874 4(4)
Service Quality 0.885 3(4)
Satisfaction 0.862 4(5)
Behavioural Intention 0.921 5(5) Note: Final items numbers (initial numbers)
The method variance is identified as a potential issue in this study due to the self-report nature of
the survey. The instrument was also organised into various sections by separating the variables in
an effort to reduce single-source method bias (Podsakoff, et al. 2003). A further Harman‘s one-
factor test was also performed for common method bias. The test revealed the presence of six
distinct factors with eigenvalue greater than 1.0. The six factors together accounted for 68.15
percent of the total variance; the first (largest) factor did not account for a majority of the
variance (38.91%). Thus, our analysis showed that no general factor was present. While the
results of these analyses do not preclude the possibility of common method variance, they do
suggest that common method variance is not of great concern and thus is unlikely to confound
the interpretations of results.
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Hypotheses testing
The standardized PLS path coefficients for testing the structural model are shown in Figure 2.
Table 6 present the results and hypothesis testing. The findings support the hypotheses H1 and
H4 to H8; hypotheses H2 and H3 were not supported (Table 6). Responsiveness and reliability
were not significant predictors to service quality (H2 and H3). The 2R value of service quality
construct was 0.568 suggesting that 56.8% of the variance in service quality can be explained by
tangible, empathy, reliability, outcome and responsiveness. The H1 and H4 to H8 were
supported with t-value range from 2. 956 to 18.419. The 2R value of satisfaction construct was
0.470 suggesting that 47% of the variance in satisfaction can be explained by service quality and 2R value of behavioural intention construct was 0.533 suggesting that 53.3% of the variance in
satisfaction can be explained by satisfaction. The H8, student satisfaction mediates the
relationship between perceived service quality and behavioural intentions was supported
because the relationship between service quality and satisfaction, and satisfaction with
behavioural intention were significant. In order to assess if there is full or partial mediation, we
also used the method suggested by Baron and Kenny (1986). We found that student satisfaction
is fully mediated the service quality and behavioural intention.
FIGURE 2 RESULTS OF THE PATH ANALYSIS
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TABLE 6 PATH COEFFICIENT AND HYPOTHESIS TESTING
Hypothesis Relationship Beta t-value Supported
H1 Tangible-Service Quality 0.310 4.675, 01.0 Yes
H2 Responsiveness Service Quality 0.009 0.094, 05.0 No
H3 Reliability Service Quality 0.001 0.013, 05.0 No
H4 Empathy Service Quality 0.229 2.956, 01.0 Yes
H5 Outcome Service Quality 0.443 5.106, 01.0 Yes
H6 Service Quality Satisfaction 0.685 16.176, 01.0 Yes
H7 Satisfaction Behavioural Intention 0.730 18.419, 01.0 Yes
H8 Service QualitySatisfactionB.
Intention
Yes
DISCUSSION AND CONCLUSION
Higher education institutions (HEI) in Malaysia are facing competitive market challenges,
customers or students are evaluated the services provided by the HEI. In this competitive
environments, student perceptions of service quality and their satisfaction level of teaching
process have become very important in order to attract and retain them. Thus, the main purpose
of this paper is to look at the relationship between behavioural intention and related constructs;
more precisely the relationships between antecedents of service quality, perceived service
quality, student satisfaction and behavioural intention. Behavioural intention is perceived as
being the ultimate dependent variable of the research model.
The empathy dimension indicates the lecturers‘ willingness to help and motivate the students. It
is also reflects the sensibility and cautions to students' needs. The smallest beta value ( =
0.229, t = 2.866) shows that students still not satisfied with quality of this dimension. The results
of analysis on this dimension indicate that student not satisfied with lecturer supportive
behaviour toward fulfilling their needs. That is, student perceived that the lecturers did not put
the interest on student development and did not encourage them to do their best in the study.
Accordingly, the empathy dimension was found to have positive impact on student perception of
teaching/service quality and satisfaction with the instructor/lecturer (Standifird et al. 2008;
Hancock, 2000). The tangible dimension focuses on lecturer appearance and physical facilities
available in the classroom. From the analysis the dimension generated the value equal to
0.310 and t value equal to 4.639 that show the dimension is still not adequate to fullfil student
363
needs; the students learning process were affected by classroom comfort. However, we did not
find that the lectures/instructors attire and appearance affected student learning process. The
tangible dimension is very important in teaching and learning process, that is modern, fully equip
and clean classroom would give positive perception of teaching/service quality provided by
instructor/lecturer (Hill and Epps, 2010). The outcome dimension is highlighted the technical
part of service quality. Student evaluate the lecturer/instructor in term of knowledge and skills
gained, the availability of class note and reading materials, instructor‘s feedback on assessed
work, and the coverage and depth of the lecture. From the analysis, we found that the dimension
was paramount in determine service quality (with = 0.443 and t = 4.959). That is, in an
educational context, the lecturer/instructor performance during the teaching or service
transactions is very important.
Our final conclusion was that the reliability and responsiveness constructs were not very
important in determining perceived service quality from student perspective. The values of
the two dimensions were not significant. It was apparent also that the two constructs were
perceived by student being delivered effectively.
The results demonstrate the student satisfaction was the most influential factor, directly and
strongly related to behavioural intention. The service quality construct has only indirect
relationship to service quality via student satisfaction. This study revealed that the service
quality, satisfaction and behavioural intention are distinct concepts. Taking into consideration the
significance levels of the path coefficients, satisfaction and behavioural intention dimensions
have the highest degree of association with service quality either directly or indirectly. That
shows the service quality and satisfaction dimensions are very important in determining student
behavioural intention, thus academic leaders should emphasis more on the dimensions. Such
insight can help the leaders when making decisions concerning the allocation of scarce resources.
We suggest that students should be viewed as customers because higher education institutions
are facing great competitions in attracting students and thus, they are moving towards marketing
approach to draw student interest toward their institutions (Sultan and Wong, 2010). Satisfying
students is important for positive word of mouth and referral decisions. From the analysis
techniques presented here, tangible, empathy and outcome (technical quality) can be the source
of help to instructors/lecturers. They should identify the components and subcomponents that
are important in increasing teaching quality and eventually student satisfaction. If the constructs
(tangible, empathy, and outcome) are improved, this improvement will benefit other constructs
as well.
Due to the diversity of courses offered in other higher education institutions and having different
facilities, equipment, staff and faculty members, thus, the results of this study are contributed
very limited to be generalized to other institutions. Hence it is recommended that every
institution carry out a similar study so that a model with more conformity will be produced for
planning to improve teaching services quality.
364
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GAMIFICATION: IMPLICATIONS FOR WORKPLACE INTRINSIC MOTIVATION
IN THE 21ST
CENTURY
Chris Perryer
Business School, University of Western Australia
M261, 35 Stirling Highway, Crawley, Western Australia 6009
Tel: +61 8 6488 1445
Fax: +61 8 6488 1072
E-mail: [email protected]
Brenda Scott-Ladd
School of Management, Curtin University,
GPO Box U1987, Perth, Western Australia 6845
Tel: +61 8 9266 9150
Fax: +61 8 9266 7897
E-mail: [email protected]
Catherine Leighton
Business School, University of Western Australia
M404, 35 Stirling Highway, Crawley, Western Australia 6009
Tel: +61 8 64887987
Fax: +61 8 6488 1072
E-mail: [email protected]
ABSTRACT
Gamification is a term which has gained currency in the media over the last few
years. Gamification refers to the application of characteristics from computer games into non-
gaming contexts. The concept under other names has attracted the interest of scholars for more
than twenty years, due to its possible value in motivating students to learn. However few
scholars have investigated ways in which the concept can be applied to building intrinsic
motivation in employees in relation to their on-going jobs. This is a particularly important area
for research, as new generations who have been brought up with computer games become the
dominant cohort within the workforce. This paper summarises the literature on game playing as
a motivator, and outlines likely motivators for younger members of the workforce. The paper
goes on to discuss how the concept of gamification might be integrated into the Four-Drive
theory of motivation, and how it might be integrated into workplace systems to benefit
organisations in the 21st century.
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INTRODUCTION
Gamification as a term and as a concept has as many detractors as it does advocates. Advocates
claim that gamification has increased user engagement by up to eight times, while detractors
argue that it is nothing more than a gimmick. The term was probably coined in 2002 by UK
based games designer Nick Pelling. Pelling used the term to refer to the application of game-like
accelerated user interface design to make electronic transactions more enjoyable and faster
(Mobile Content 2011). Since 2002 the term has acquired a broader meaning, and is now
generally considered to refer to the application of characteristics and design techniques from
games into non-gaming contexts. Gamification concepts and techniques are now used primarily
to engage audiences and motivate them to behave in a particular way. They do this by showing a
people a path towards task mastery and autonomy. Despite the criticism, the techniques are now
being used in a number of learning and commercial settings to encourage people to perform tasks
which they might otherwise not wish to do, such as assimilating new knowledge, completing
forms and surveys, learning new skills, or accessing new websites.
While there is now a good deal of evidence to support the use of games as a motivator for
learning, or as a way to motivate people to complete one-off tasks, little attention has been paid
to the potential for gamification to motivate employees to complete their normal day to day jobs.
This paper first discusses motivation, and game playing as a motivator. It then goes on to outline
how motivational needs might be satisfied by aspects of game playing, and how these findings
might be relevant to the workplace of the 21st century.
MOTIVATION
Motivation exists when a person is energised or moved to perform a task or behave in a
particular way (Ryan & Deci 2000a). Motivation can vary in its level, intensity, or orientation.
Much has been written on the topic of motivation. Hierarchies of need, hygiene theories, process
and content theories, expectancy theory, equity theory, and many other theories and models, will
be familiar to scholars in the area. One basic and important distinction in the study of motivation
in the workplace involves differentiating between intrinsic and extrinsic motivation. Intrinsic
motivation occurs when a task is inherently interesting or enjoyable, whereas extrinsic
motivation may occur when performing the task leads to a separate desirable outcome (Ryan &
Deci 2000a), such as an award, promotion or an increase in salary. Extrinsic motivators, by their
very nature, tend to be effective only until the desirable outcome has been achieved. For
example, if a person is motivated to work hard at a particular task by the expectation of a
promotion, as soon as that promotion has been achieved, there is no longer the motivation to
work hard. On the other hand an intrinsic motivator, such as job satisfaction will continue to
motivate the worker to work hard indefinitely. Intrinsic motivators tend to be a function of the
design of the job and values or interests of the worker, whereas extrinsic motivators tend to have
373
little to do with job design. Intrinsic motivators are more stable over time, and tend to require
less management intervention, whereas extrinsic motivators require closer management scrutiny
and attention, as effective motivational content escalates over time. We see this often in relation
to salary increases. While the promise of a salary increase may be a motivator, the reality of a
pay rise rarely is, as the worker adjusts their expectations, and quickly sees their new salary as
the norm.
A relatively recent addition to the literature is the Four-Drives theory of motivation. This theory
was originally proposed by Lawrence and Nohria (2002). The theory suggests that all humans
are subject to four basic drives, namely the drive to acquire, to defend, to bond, and to
comprehend (Lawrence 2011). According to Lawrence, the drive to acquire propels people to
obtain physical goods such as food and shelter, intangible things such as travel and
entertainment, and social things of value such as status. The drive to defend is rooted in the
basic fight-or-flight response, but manifests itself in the need for financial and job security,
resistance to change, and a sense of vulnerability in uncertain times. The drive to bond motivates
people to build and retain family and kinship ties. It also promotes a sense of belonging to and
pride in one‘s work organisation, and a sense of fulfilment through the membership of networks,
clubs and associations. The drive to comprehend encompasses the need to understand and make
sense of the world around us, the desire to make a meaningful contribution, and the desire to
grow, be challenged and learn. It is part of the human condition, and the inherent need people
have to play and engage suggested by McGregor (1960).
From an organisational perspective, motivating employees involves satisfying the employee
needs that flow from those four basic drives. Nohria, Groysberg and Lee (2008) argue that each
of these drives can be addressed through the application of primary organisational levers, as
shown in Table 1.
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TABLE 1: DRIVES AND ORGANISATIONAL LEVERS
Drive Primary Lever Actions
The need to acquire Reward System Differentiate performance
Tie rewards to performance
The need to defend Performance-Management and
resource-allocation process
Build trust
Increase transparency
The need to bond Culture Foster mutual reliance
Value collaboration
The need to comprehend Job Design Design meaningful jobs
Design challenging jobs
The integration of these levers and actions into management will be discussed later in the paper.
THE 21ST
CENTURY WORKPLACE
Generational cohorts
Currently, the paid workforce consists of three generational cohorts, Baby Boomers, Generation
X and Generation Y (also known as Millennials). Baby Boomers (those born between the mid-
1940s and the mid-1960s) are usually portrayed as being idealistic, optimistic and inner-directed
(Kupperschmidt 2000; Loomis 2000). Their affinity with technology varies greatly. Many have
been early adopters of e-technology over the last two decades, while others do not even possess a
mobile phone. Most of the people within this cohort are now in their 50s or 60s, and those still in
work are approaching the end of their working lives, and consequently, this paper will not
examine motivational issues for this cohort.
Generation X (Gen X - those born between the early/mid-1960s and the early 1980s) is usually
described as adaptable and technologically competent (Ferres, Travaglione & Firns 2001;
Jurkiewicz 2000; O'Bannon 2001). Gen X has grown up and reached adulthood during a period
375
of extraordinary technological, economic, and social change. Gen X employees are eager to
upgrade their skills through training on the job and externally in order to keep themselves
―employable‖ (Tulgan 1995). This sense of maintaining their marketability, in the face of
uncertain job futures, may explain why they are generally less inclined to be committed to
organisations. On average, Gen X employees will hold six different jobs during their careers,
significantly more than their parents typically did.
Generation Y (Gen Y), consists of those born after the early 1980s. This cohort has only
recently attracted scholarly interest, but they are generally described as optimistic, smart and
cooperative (Perryer & Esteban 2009). This generation has known mobile phones, home
computers, hand-held electronic devices, and a plethora of other e-technology for all of their
lives. They are quick to adopt new technological options such as Facebook, Twitter,
smartphones and tablets. They tend to accept and trust authority and follow rules to a far greater
extent than the two preceding generations (Howe & Strauss 2000). The willingness to work
within rules suggests that this generation is more likely to apply themselves to work systems and
procedures that are rule-based. Consequently they are ideal candidates to be motivated by
computer games.
It follows then that the workplace of the 21st century will quickly be peopled by workers who
have grown up with computers as an integral source of information and entertainment, both at
school and in the home. Games may have been important to the development of previous
generations, but there was always a schism between work and play. Gen X and Gen Y (and the
generations which will follow will not have experienced the divide. For them, integration of
play into work is something they are likely to expect. This notion will be explored more in the
next section.
GAME PLAYING AS A MOTIVATOR
We know from the anthropology and sociology literature that games have always been an
important aspect of learning, and the increasing use of computer games over the last two decades
has sparked interest in research into the use of computer games as educational tools (Rieber
1996). Winn (2002) maintains that the current trend in the field of instructional design is
towards the development of interactive learning environments. This is logical, and a function of
the expectations of students, training of the teachers, and the capabilities of the available
technology. If this is indeed the case, then there is considerable scope to integrate computer
games into such environments, due to the interactive nature of these games.
While it is acknowledged that the primary purpose of game playing is entertainment, the basis of
any game involves achieving an objective while and working within a set of rules. Games
entertain for a number of reasons. Firstly, they allow people to take risks which they might not
be willing to take in real life, where the cost of failure may be significant. Secondly they distract
people from the mundane or unpleasant tasks which they are required to do to as part of their
376
―normal‖ life. Thirdly, games provide people with a sense of achievement when they succeed at
the game. In order to play the games, however rules must be learned. Consequently, there is
much in common between playing a game and accomplishing a work related task. Perhaps the
only difference is that the former is usually seen as ―fun‖, whereas the latter is often seen as
―work‖, the implication being that the two terms are mutually exclusive. However, managers and
those responsible for job design should question this assumption. Many jobs now consist almost
entirely of information processing. Successful completion of a task will often produce a message
along the lines of ―your transaction has been accepted‖. It would require little thought or effort
to introduce messages which aroused the interest and stimulated the enjoyment and satisfaction
of the person inputting the transaction. Perhaps the perception that work should not be play will
eventually disappear due to the changing demographics of all workplaces. Prensky (2002)
argues that the generation which grew up with computer games no longer accepts the separation
of fun and learning, and it may be that they will have similar attitudes to fun and work.
Computer games are now widely used in many training applications, and a number of studies
have found that they lead to improved learning (Ricci, Salas & Cannon-Bowers 1996; Whitehall
& McDonald 1993). A study by (Malouf 1988) found that the integration of computer games
into training did not produce an increase in task skill post-training, but did produce significantly
higher levels of continuing student motivation to learn that task. Despite these positive findings
there is still no consensus on the elements of instructional games which lead to positive learning
outcomes (Garris, Ahlers & Driskell 2002). There have also been suggestions that computer
games are a male pastime (Bryce & Rutter 2003), but the evidence suggests that this situation is
now changing, and females are much more likely to find computer games appealing (Dickey
2006). Consequently, integrating computer game concepts into job design are likely to produce
similar positive outcomes in both male and female workers.
Games have also been found to be useful as a motivator in contexts other than education.
Nintendo‘s Wii and Konami‘s Dance Dance Revolution have been widely used to motivate
sedentary people to be more physically active (Yim & Graham 2007). This suggests that games
have the potential to motivate people to do a range of things, and are not limited to the
motivation of learning. The critical issue here is that games have been shown to be motivators in
areas other than education. For this reason it is argued that games are likely to be useful
motivators in the workplace.
A number of scholars (Deci & Ryan 1985; Przybylski, Rigby & Ryan 2010; Ryan & Deci
2000b) have argued that Self-Determination Theory (SDT) helps to explain the process of
motivation in sport education and leisure domains. The SDT model is founded on the
satisfaction of three basic human needs, namely the need for competence, the need for autonomy,
and the need for relatedness. If these scholars are correct, then there are clear similarities
between the motivational needs of sport and leisure, and the motivational needs of employees.
377
GAMIFICATION AS AN INTRINSIC WORKPLACE MOTIVATOR
Gamification has the potential to increase motivation by providing employees with experiences
that satisfy universal psychological needs. These needs can be addressed through the application
of the four workplace levers set out in Table 1.
Reward system
There is extensive evidence in the psychology and management literature to establish that there
is a response of some sort to every effective stimulus (Hodgkinson 2003; Latham 1989; Yeo
2002). Elements which can be borrowed from computer games include ―real-time‖ feedback.
Positive feedback gives reinforcement of appropriate behaviour, while negative feedback
facilitates learning and adjustment (Machin 1999; Perryer 2004; Rouiller & Goldstein 1993;
Tracey, Tannenbaum & Kavanagh 1995). Feedback which is built into the job will provide more
regular feedback than annual performance reviews or monthly sales information. Businesses
need to introduce systems and processes that allow fast and meaningful feedback, accelerating
employee learning and performance. Additionally, employees need to know where they are in
comparison to others people in the workplace, and games can facilitate that. Games can also
assist in goal setting, in that they can provide clear objectives with milestones (getting to the next
level in a game context), while at the same time providing feedback on performance. Progress
through such levels should lead to employee engagement, an essential management objective for
Generation Y employees. This can be facilitated through the use of badges that appear on the
user‘s profile, or through employee awards. An example of how this is already occurring in
some workplaces is the Six-sigma levels or ―belts‖. Badges, whether real or virtual,
acknowledge the expertise of the participant, and serve to inform other ―players‖ of that level of
expertise. Leader boards can also be used as reward and recognition tools. Similar reward and
recognition are used by airlines in the retail loyalty programs.
Performance-management and resource-allocation processes
Computer games can make a significant contribution to transparency and fairness in the
workplace (Dickey 2006; Garris, Ahlers & Driskell 2002). In games, all organisational ―players‖
are subject to the same systems and rules, with similar outcomes for similar inputs. The range
and nature of outcomes available to employees can be increased through the use of gamification
concepts. For example, it is not uncommon for the budget of a manager or section to be varied
based on performance. Using information technology, this can be done immediately, or perhaps
weekly, rather than annually. It can be done by the allocation of credits or points, rather than
through a budget allocation. The awarding, spending and exchanging of points or credits gained
378
through completion of tasks and the quality of task completion is a game element that is
available to business now, but to the best knowledge of the authors is rarely if ever utilised.
Culture
Most workplaces now make extensive use of teams. Apart from the practical advantages of
covering absences, and the synergies that can be gained by people bringing diverse experience
and skill sets to the job, teams provide a social dimension to work. Teams can generate healthy
competition and social connection. They can also serve to stretch employees, who generally do
not want to be a weak link in their workplace. Teams facilitate shared learning and are able to
take advantage of organisational learning concepts. They provide a vehicle for different
perspectives to be developed and serve as a barrier to negative group processes and outcomes
such as groupthink. However, teams can produce negative cultures, and indoctrinate members
with incorrect or unhelpful assumptions about work. New team members can be taught ways to
―beat the system‖, leading to a reduction in overall organisational performance. Game concepts
can provide a barrier to negative or erroneous assumptions held by employees by encouraging
and rewarding desired behaviours.
Job design
The job design literature has long advocated making jobs more meaningful for employees.
Traditional areas of focus have included job enlargement, job enrichment and job characteristics.
The Job Characteristics Model proposed by Hackman and Oldham (1980) stress the importance,
among other things, of autonomy and feedback. Aspects of computer games can assist in
remedial outcomes. For example, many trainee pilots now use flight simulator games to review
their performance after a flight. The game technology allows trainees to set up similar
conditions and to view a simulated aircraft from a number of angles. Game concepts allow
inexperienced employees to rehearse and practice without the risks and costs associated with
developing their skills in real business transactions.
Nobody will read the manual in the workplace of the 21st century according to many, and
systems need to assist learning. If this does not occur organisations are likely to suffer significant
wasted time and effort from employees. Game technology, and computer technology more
generally, can assist here too. Progress bars and other visual indicators, for example can show
how close to finalisation a task is.
379
CONCLUSION
Gamification is happening and there are many benefits, but also a downside. Employees are not
Avatars who respond according to script. Engaging in game playing runs the risk of
disassociation that contributes to a raft of problems. At the less extreme this may contribute to
time wasting, or raised expectations from those who do develop a higher level of competence
that others struggle to match. At the more extreme end of the continuum, it may lead to learning
outcomes that are totally unrelated to the reality. In addition, gamification may not suit some
learning styles so it needs to be combined with other learning strategies. An advantage of this
approach is that individuals get the opportunity to practice in their own time and space however
they may not always get the opportunity to learn beyond the basic information problem-solving
in concert with others brings. Focus on skill development may be at the cost of knowledge and
holistic development (i.e. a manager is focused on productivity outcomes and overlooks the
human dimension – hence lack of genuine management support).
Gamification has the potential to increase the capability of a workforce through increasing the
self-efficacy (Bandura 1969; Bandura 1977) of individual workers.
The question for managers is essentially the same as that raised by (Garris, Ahlers & Driskell
2002) – which characteristics of games have relevance to the workplace, and if they do, will this
be beneficial for organisations? Three characteristics come immediately to mind, namely
learning, rewards and individual and group performance.
Gamification is now seen by many people as a concept that has relevance to the workplace.
While it has long been considered to be a useful way to motivate people to learn, it may also
have value in other work related areas such as job design and team work.
There is a need for studies which examine the extent to which game playing elements in job
design are impacted by the four drives.
380
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