quality management practices and operational performance

D O C U M E N T O D E T R A B A J OW O R K I N G P A P E R S S E R I E S

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

QUALITY MANAGEMENT PRACTICESAND OPERATIONAL PERFORMANCE:EMPIRICAL EVIDENCE FOR SPANISH

INDUSTRY

Javier Mer ino

DT 40/00

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Universidad Públicade NavarraNafarroakoUnibersitate Publikoa

C a m p u s d e A r r o s a d í a , 3 1 0 0 6 P a m p l o n a , S p a i nT e l / P h o n e : ( + 3 4 ) 9 4 8 1 6 9 4 0 0

F a x : ( + 3 4 ) 9 4 8 1 6 9 4 0 4E - m a i l : w o r k i n g . p a p e r s . d g e @ u n a v a r r a . e s

Quality Management Practices and Operational Performance:

Empirical Evidence for Spanish Industry

Javier Merino-Díaz de Cerio∗∗

Department of Business Administration,

Public University of Navarra, Pamplona, Spain.

Contact: Mr Javier Merino Díaz de Cerio Departamento de Gestión de Empresas Campus de Arrosadía 31006 Pamplona España Tel.: +34-948-169383 Fax: +34-948-169404 e-mail: [email protected]

∗ I thank Fundación BBVA for the financing provided

3

Quality Management Practices and Operational Performance:

Empirical Evidence for Spanish Industry

The progressive implantation of the ideas and techniques related to the conceptof Quality Management is perhaps the most patent expression of the change andinnovation which has taken place in organisations in recent years. The aim of thecompanies is to improve their competitiveness by improving their operationalperformance. Is this in fact the case? The purpose of our study is to provide an answerto this question. Our study is based on information we have obtained from an extensivesample of industrial plants (965), employing more than fifty workers and belonging toall sectors of the manufacturing industry in Spain. After a thorough revision of theliterature relevant to this issue, we contrasted the hypotheses using logit models. Wealso used a multiple regression analysis in order to ascertain which of the QM practiceshad the biggest influence. Our results are consistent with those of the majority of thestudies carried out to date. They demonstrate that there is a significant relationshipbetween the level of implementation of QM practices and the improvement inoperational performance, in terms of cost, quality and flexibility. The QM practicesrelated to product design and development, together with human resource practices, arethe most significant predictors of operational performance.

Key Words: Quality Management, Performance, Manufacturing, EmpiricalAnalysis

INTRODUCTION

Spanish manufacturing businesses have been very favourable to the adoption ofdifferent practices related to QM over the last few years. In an increasingly competitiveglobal marketplace, Spanish businesses have needed to make greater efforts to improvetheir competitive edge by improving their operational performance in terms of cost,quality and flexibility.

Although businesses in the West started this process in the years approaching the80’s and despite the abundant literature on QM, empirical studies of the relationshipbetween QM practices and operational performance did not start to appear until after1994. Moreover, few of them use large samples of manufacturing businesses fromdifferent sectors. The purpose of this study, which is based on extensive fieldwork, is toprovide new evidence to consolidate previous findings as well as guidance for managersworking in this field.

4

The study seeks to discover whether the implementation of QM practices has animpact on the improvement in operational performance and to find out in which areasthe influence is greatest. In order to do so, we have firstly carried out a revision of theliterature related to this issue. Next, the conceptual framework was established in orderto define the indicators which measure the implementation of QM practices. Once thehypotheses were established, the variables were set in the empirical model in which theywere to be contrasted. Finally, the results obtained were analysed and the conclusions ofthe study were reached.

The large sample of industrial plants from all sectors means that the results havea greater solidity and lend themselves to a greater generalisation. They furtherconsolidate the findings in this field. The geographical area of the study, (different fromothers, which are, in the majority in the Anglo-Saxon countries), the incorporation ofthe results taken from the whole of the manufacturing industry, as well as theintroduction of other explanatory variables, not previously included in other studies,(the level of automation, the organizational climate etc.) contribute in an important wayto the field of QM.

THE RELATIONSHIP BETWEEN QUALITY AND PERFORMANCE: AREVISION OF THE LITERATURE

The relationship between the adoption by companies of certain practices andperformance is the subject of constant interest among researchers in the field of businessmanagement. The implementation of any kind of practice represents a cost for thecompany, both in terms of human and material resources. If the efforts made withregard to the implementation and maintenance of these practices are to show a return,then an improvement in the results must be achieved. In the field of QM we need todistinguish between QM practices (input) and quality results (output). Over the last fewyears, there have been a number of studies which have tried to relate QM practices withdifferent operational results, including quality results, among others. In the majority ofthe different empirical studies on QM this issue appears, in one form or another,(Ebrahimpour and Johnson 1992; Flynn, Schroeder and Sakakibara, 1994,1995a, 1995band 1995c; Adam, 1994; Hendricks and Singhal, 1997, etc.).

There are several studies of an empirical nature, undertaken by differentinstitutions or firms of consultants for informative purposes, the conclusions of whichindicate that the companies which adopt total quality models obtain better results (U.S.GAO, 1991; American Quality Foundation, 1991[1] ). However, precaution must beshown in regard to these conclusions as these studies can be partial and notscientifically accurate (Powell, 1995).

Although they did not put the concept of QM practices into operation,Ebrahimpour and Johnson (1992) studied the relationship between the commitment toquality and the role of quality in strategic planning, the resulting operationalperformance were of a diverse nature. Later, Flynn et al. (1994) in their attempt tocreate an instrument to measure QM and in order to establish its validity as a criterion,they analysed the correlation of its dimensions with two measurements of qualityperformance and found a strong relationship between them. From that moment on,

5

different empirical studies have appeared over the years. Table 1 shows these studiesand includes the names of the authors, the samples’ characteristics, the explanatoryvariables, the measurements used for the results, the methodology and the mainconclusions.

These studies have been published recently, after 1994, except for one particularcase. On the whole, they are based on data from American companies and almost all thecases centre exclusively on one sector of manufacturing. The explanatory variables,although in a different way, are measurements of QM (several dimensions of theconcept). No other control variables are introduced in the majority of the studies. All thecases use measurements of operational performance and, what is more, in 50% of thestudies economic-financial results are used, with a predominance of measurementperformance of a subjective nature. The methodology used for the analysis is varied,although the multiple regression analyses are most common. In general, the relationshipbetween the QM practices and the results is positive, that is to say, the greater the levelof implementation, the better the results obtained. Nevertheless, the significance ofsome dimensions is not the same as for others, nor is the repercussion the same on theoperational performance or the economic-financial results. All the studies presenttransversal type data and have the methodological limitations peculiar to this type ofstudy.

7

Table 1. Summary of papers about the relation between QM practices and performance SAMPLE EXPLANATORY

VARIABLES PERFORMANCEMEASURES

METHODOLOGY ASSOCIATION WITHPERFORMANCE

Ebrahimpour y Johnson (1992)

222 American andJapanese manufacturingcompanies (three industries)

Commitment and qualitystrategy

Operational and financialperformance

Causal model Positive for Japanese, nofor American.

Flynn et al. (1994) 42 Americanmanufacturing plants(three industries) 706 valid responses

Seven QM dimensions Operational performance(quality performance)

Canonical correlation Positive

Adam (1994) 187 Americanmanufacturing companies

Five QM dimensions Operational and financialperformance

Stepwise regression Some significativerelations.

Mann y Kehoe(1994)

211 Englishmanufacturingcompanies.

Six quality improvementspractices

Operational and financialperformance.

Descriptive statistics Positive

Powell (1995) 54 Americanmanufacturing andservice firms

Twelve QM dimensions Financial performance Correlation analysis Positive (intangiblesspecially)

Flynn et al. (1995a) 42 Americanmanufacturing plants(three industries) 706 valid responses

Eight QM dimensions Operational performance Multiple regressionanalysis

Positive

Flynn et al. (1995b) 42 Americanmanufacturing plants(three industries) 706 valid responses

Eight QM dimensions Operational performance Path analysis Positive

Flynn et al. (1995c) 42 Americanmanufacturing plants(three industries) 706 valid responses

Four QM dimensions Operational performance Discriminant analysis Non lineal

Forza (1995) 34 manufacturing plantsin Italie (two industries)

Five QM practices andeight variables related toinformation systems

Operational performance Canonical correlationanalysis

Positive

Lawler III et al.(1995)

279 Fortune 1000manufacturing andservice companies

Three groups QMpractices (14 practices)


Corrrelation analysis Regression analysis

Positive with someexceptions

8

Table 1. (Continuation)

SAMPLE EXPLANATORYVARIABLES

PERFORMANCEMEASURES

METHODOLOGY ASSOCIATION WITHPERFORMANCE

Ittner y Larcker(1996)

249 companies inGermany, Canada, USAand Japan (two industries)

Twelve QM factors andseven information andreward systems variables


Multiple regression No association found

Martínez (1996) 217 Spanishmanufacturing plants

One QM index Operational and financialperformance

Non parametric tests(Kruskal-Walli, Mann andWhitney)

Positive in some cases

Madu et al. (1996) 165 Americanmanufacturing and servicecompanies

Three QM indexes Operational performance Correlation analysis Positive

Leal (1997) 113 Spanishmanufacturing and servicecompanies

Ten QM dimensions Finance performance Correlation analysis No with one measure, yeswith others.

Forker (1997) 264 American companies(one industriy)

Seven QM dimensionsand relative efficiency

Operational performance Multiple regression Positive

Adam et al.(1997)

977 manufacturing andservice companies in ninecountries.

Nine QM dimensions. Operational and financialperformance

Stepwise regression Stronger with operationalthan financialperformance

Terziovski (1997) 1341 manufacturing firmsin Australia and NewZealand (all industries)

ISO 9000 Certification TQM environment Size


MANOVA andANCOVA

Negative

Choi yEboch(1998)

339 Americanmanufacturing firms (twoindustries)

Four TQM dimensions Operational performanceCustomer satisfaction

Structural equationsmodel

Positive

Wilkinson et al.(1998)

Summary of six studies inUK

Several Several Descriptive statistics Various results, althoughthere are more positiveimpacts

Samson yTerziovski (1999)

1024 manufacturing firmsin Australia and NewZealand (all industries)

Six TQM dimensions Operational performanceCustomer satisfaction Employers satisfaction

Regression analysis Three dimensionspositively related(Leadership, HumanResources and CustomerFocus), the rest not related

9

THE CONCEPTUAL FRAMEWORK AND THE ESTABLISHMENT OFTHE HYPOTHESES

Quality Management Practices

In order to carry out the empirical investigation we have established theconceptual framework shown in figure 1 as our basis. Within this framework, there arefive dimensions or five sets of practices, associated with product design, thetransformation process, relations with the suppliers, relations with the customers, andhuman resource management. They are considered to be primary dimensions in thesense that they are directly related to improvement in the quality of the product. Thesame dimensions appear, more or less explicitly, in almost all of the studies on qualitymanagement, (Deming 1986; Saraph et al., 1989; Hackman and Wageman, 1995; Flynnet al, 1994 and 1995; Powell, 1995; Black and Porter, 1996; Ahire and Golhar, 1996etc.). Based on the study by Flynn et al. (1995c) the difference is that we incorporateone of their dimensions (“workforce management”) and part of another (“informationmanagement”) into a single dimension of human resource practices in relation toquality. The other part of the last dimension is incorporated into Processes.

The framework we have established reflects the idea expressed by Ahire andGolhar (1996). They are of the opinion that commitment on the part of managementmust be seen by implementing a set of strategies which take into account threeimportant stakeholders in the operations of the organisation: customers, suppliers andemployees. Customer attention is very important for an efficient QM initiative. Thequality of the material supplied by suppliers who are competent, reliable and flexible isa prerequisite for the quality of the finished product. The strategies which allow thecompany to produce high quality products starting from supplies of quality comprise ofthe following; the introduction of quality in the design of the products, quality assurancein the processes through the use of different instruments and the judicious use ofexternal and internal information. Nevertheless, the key to success lies in humanresource management through the empowerment of employees and the creation of astructure which promotes their participation and training.

10

Figure 1. Conceptual Framework for Quality Management Practices.

Adapted from Flynn et al. (1995c)

DESIGN PROCESS

SUPPLIERS

CUSTOMERS

HUMAN RES.

Operational PerformanceThe subject of the measurement, evaluation and conceptualising of operational

performance in a company is a recurrent theme in the different sections of the academicliterature. One of the first general classifications, which has been widely used, is that ofVenkatraman and Ramanujan (1986). This adopts a Strategic Management perspectiveand focuses on the measurement to establish a division between financial andoperational performance, with the emphasis on the latter. Following a similar line,Kaplan and Norton (1992) believe that the traditional measurements of financialperformance are no longer valid for today’s business demands. Therefore, they considerthat operational measurements for management are needed in relation to; customersatisfaction, internal processes and the activities concerning improvement andinnovation in the organisation, which lead to future financial returns.

Manufacturing performance, which include part of the operational performancepreviously mentioned, are commonly used in the field of Operations Management. Thistype of results takes into account the company’s performance in reaching its basicobjectives, that is, productivity, quality and service. There are several studies which aimto establish a classification of this kind of results (Corbett and Van Wassenhove, 1993;Neely et al., 1995; Filippini et al., 1998).

11

It is important to explain the two characteristics of the measurements of theperformance we have used in this study. First of all, they are relative measurements ofthe improvement in the results of the plant in relation to the situation three years earlier.The different manufacturing performance, which are measured in an absolute way,depend a great deal on the technology and type of process found in the plant. Therefore,it becomes difficult to establish comparisons when the data is obtained from a group ofheterogeneous plants, even when the sector variable is introduced as a control variable.The other noteworthy characteristic is the subjectivity of the information used. Resultsof a subjective kind are often used in research on organisation. Some studies haveshown that there is an important relationship between the objective economic-financialperformance and the same measured in a subjective way. This may serve as ajustification for the use of this kind of performance (Venkatraman and Ramanujuan,1986; Powell, 1995).

For the manufacturing performance we have adopted Corbett and VanWassenhove’s model (1993), which considers the measurements of the performance inthree dimensions (cost, quality and time) although we use other individualmeasurements. The indicator for cost results we used is the percentage of productivehours in relation to the total number of hours the workforce is directly present. Itreflects the waste and inefficiency of the productive system and states the unproductivemoments owing to organisational problems (lack of material, breakdowns, problemswith quality etc.). The three indicators of improvement in quality performancecorrespond to a concept of product quality as conforming to specifications (New, 1992).We have included aspects related to unfinished products as well as finished products,both from an internal perspective (percentage of defective products) and an externalperspective (percentage of returned products). The time factor has been considered asrepresenting a competitive advantage over the last few years, and is a fundamentalmeasurement of manufacturing performance (Stalk, 1988; Blackburn, 1991; Azzone etal., 1991). The reduction in the time taken from the moment the material is received tothe moment the product is delivered to the customer serves as an indicator of the speedof the processes. In the same way, the percentage of delivery dates complied with is atypical measurement of punctuality (Filippini et al., 1998), considered a basic aspect ofcustomer attention.

The Establishment of the Hypotheses

From a theoretical point of view, it is reasonable to assume that the adoption onthe part of the company of QM practices contributes to an improvement in theperformance, above all in those of an operational nature. The purpose of this type ofpractices is error prevention. This work on prevention will result in fewer errors, whichwill immediately lead to a reduction in the number of defective products (the quality asconformance to specifications improves). If conformance to specifications is achievedwith no great difficulty, then, undoubtedly, the processes will be more efficient as thenumber of stoppages to adjust the process will decrease and resources, both material andhuman will be saved. Moreover, the speed of the process will also increase, which, in

12

turn, may help to improve the level of conformance with delivery dates and finally,achieve customer satisfaction.

This line of argument, together with the evidence from the empirical studiesleads us to contrast the following hypothesis, stated in a generic way :

Hypothesis: The plants which have undertaken a greater implementation of QMpractices have achieved a greater improvement in their manufacturing performancethan those with a lower level of implementation.

This hypothesis leads to a set of hypotheses to the same effect, as many as thenumber of different measurements of performance used.

RESEARCH DESIGN AND METHODOLOGY

The process of obtaining data

The Spanish manufacturing industry constitutes the scope of our study. Theconcept of manufacturing industry is clearly defined in the National Classification ofEconomic Activity (NACE), which includes all the manufacturing industries (from code15 to code 37) with the exception of oil refining (code 23). The sector of production anddistribution of electricity, gas and water (codes 40 and 41), as well as the miningindustries (from 10 to 14) are also excluded.

Fixing the unit of analysis was an important matter to settle. Two possibilitieswere initially open to us: to choose the company or plant as the organisation to beconsidered under study. We opted for the latter. In the industrial sector, the plantconstitutes the business unit which is of strategic importance for the implementation ofthe practices which make up our study. These practices are adopted in the plant, andtherefore, it is at this level where problems arise and where the results must be analysed.Moreover, the answers to the different questions raised are expected to be more reliablewhen taken from the plant; since the knowledge of these issues is greater even if onlybecause of greater proximity.

Another aspect of the field of application to determine was the size of the plants.The industrial plants included in our sample employ fifty or more workers. This limitand it serves to cover a wide spectrum of the population employed in Spanish industry,what is more it simplifies the field work. With these criteria the reference universe wasformed by 6013 units, a thousand units being the aim of the sample, these werestratified according to sector and size.

In order to carry out the investigation a questionnaire was made up and after thecorresponding pre-test, it was modified in different ways to form the final questionnaire.As we had foreseen at the beginning of the study, most of the questionnaires (more thanthree-quarters of them) were filled in by either the plant manager of the productionmanager. The questionnaire covered different issues, all linked to production. It wasmeant to be filled in by a person with a broad understanding of the organisationalaspects of the plant as well as, though to a lesser degree, the technical ones.Nevertheless, the questionnaire’s complexity did not mean it could not be understood byany of the plant managers with a knowledge of the areas under study.

After making 3246 telephone calls to make the necessary appointments, 965valid interviews were undertaken. This number represents 16,04 percent of the total

13

population under study and constitutes the initial sample about which we haveinformation.

Measurement of the Variables

The Creation of the IndexesWe have built five indexes, each associated with one of the five dimensions

established in the conceptual framework (see figure 1) in order to put the concept ofQM into operation. The following indexes were used, associated with the practices ofdesign and development of new products (DESPROD), the production process (PROC),relations with the suppliers (SUP), relations with the customers (CUST) and humanresource management (HUMRES). Each index incorporates a series of items, as can beseen in the appendix 2 [2]. The choice of these items was based on existing literature aswell as on the experience of professionals, experts in the implementation of QualityManagement programmes.

The different items which make up the five key indexes are measured ondifferent scales. The variables were standardised and converted into z scores beforecombining them additively to form the indexes and in this way unify the unit ofmeasurement. In order to simplify the interpretation, a linear transformation was appliedto the z scores, the totals of which were obtained for each of the five indexes. With theresult that a 0 value for the indexes is given to the plant which has the lowest score ofthe sample, and a value of 100 is given to the plant with the highest score (Mac Duffie,1995). We obtained a global QM indicator in the same way, that is to say, we obtainedthe average of the z scores for the five indexes and this was then transformed onto ascale of 0 to 100.

Fiability and validity of the indexesIn the construction of our indexes we attempted to capture different aspects of a

construct using several objective measures of different quality management aspects.These were generally substitute manifestations of the underlying construct referred to as“cause indicators”[3], for wich the definition of reliability does not work well.Traditional reliability measures like coefficient Alpha assume that indicators areredundant, each measuring the same thing from a different vantage point. Causeindicators assume their are multiple, objectively different manifestations of anunderlying phenomenon which need not correlate. Bollen and Lennox (1991) arguedthat “we have no recommendations for the magnitude of correlations for causalindicators, because these correlations are explained by factors outside of the model.With causal indicators we need a census of indicators, not a sample. That is, allindicators that form it should be included”. Nonetheless, we provide Cronbach alphavalues for the indexes in the table.

14

Table 2. Descriptive statistics, Cronbach´s αα and correlations for QM indexes

INDEX Mean Std. Dev. αα Cronbach 1 2 3 4 5

1.DESPROD 59,39 18,23 0,52

2.PROC 6,87 20,28 0,75 0,433***

3.SUP 61,53 20,20 0,60 0,290*** 0,495***

4.CUST 41,14 25,18 0,66 0,325*** 0,427*** 0,520***

5.HUMRES 41,13 20,68 0,60 0,256*** 0,402*** 0,413*** 0,372***

6. GC 58,38 18,13 0,75 0,634*** 0,769*** 0,747*** 0,719*** 0,679****** p ≤ 0,01

It is important that the measurement used is not only reliable but valid. Hair et al(1995), define validity as being the ability of the indicators to measure in a precise waya specific concept, which cannot be measured in a direct way. There is no single way ofdetermining the validity of a measuring instrument. In our case, we have fixed threetypes of validity (Nunnally, 1978; Flynn et al., 1990): content validity, constructvalidity and criterion-related validity.

The content validity refers to whether the set of items which make up the scale issuitable for the evaluation of the construction (De Vellis, 1991). The items used tocreate the indexes have been taken from the literature on QM and from interviews withexperts in the field. The same rigorous approach has been taken with regard to thecreation of these instruments. The validity of the content of this instrument is thusdemonstrated.

A way of demonstrating the construct validity is to carry out a principalcomponent analysis for each of the indexes. If the information on the predetermineditems for each of the indexes can be summarised in a single factor, we can consider thatall the items are measuring the same concept and we can confirm the validity of theconstruction. A principal component analysis[4] with a varimax rotation for each ofthem, has been carried out in order to confirm their one-dimensional nature. The resultsof the analysis for each index are shown in the following table.

As a criterion for choosing the factors we have used the latent root (Hair et al.,1995), by which only the factors with eigenvalues superior to one are consideredsignificant. The principal component analysis carried out for each concept indicatesthat, in every case, only one factor with a associated eigenvalue superior to one can beseen. This corroborates the one-dimensional nature of the established concepts. It canalso be seen that the factor weights of the items are high in each case (>0,4).

15

Table 3. Results of the Principal Component Analysis for each index

QM INDEX EINGENVALUE ITEMS FACTOR LOADINGS DESPROD 1,643 CUSDES 0,478

SUPDES 0,771 DESMANF 0,722 VALAN 0,547

PROC 2,790 SPC 0,694 NORMA 0,704 POKA 0,762 ORDER 0,628 INSTRU 0,640 PREVENT 0,655

SUP 1,814 QSUP 0,470 EVSUP 0,719 COLSUP 0,724 QAGSUP 0,744

CUST 2.047 SURCUS 0,400 EVCUS 0,812 COLCUS 0,783 QAGCUS 0,784

HUMRES 1,86 TRAIN 0,532 INVOL 0,815 EMPOW 0,532 INFO 0,497

QM 2,53 DESPROD 0,598 PROC 0,776 SUP 0,766 CUST 0,727 HUMRES 0,677

The third type of validity we need to analyse is the criterion validity. We have to

show to what degree the measurement behaves as is expected in relation to one of thecriterion variables. To this end we have used QSIS as a criterion variable, this is adichotomous variable which indicates whether the company has adopted a qualityassurance system or not. Statistically significant differences in the value of the QMindexes are to be expected. The validity of the QM measurement is seen between thecompanies which have a quality assurance system and those which do not; the valuesare expected to be higher for the former. In order to contrast this we have used ananalysis of variance[5], the results of which are shown in table 4, these are verysignificant and demonstrate the existence of the criterion validity.

Table 4. ANOVA for the criterion-related validity QM INDEX Mean(Group I) Mean (Group II) F-Value Levene´s statistic DESPROD 52,92 62,1 43,358*** 2,015 PROC 56,76 71,19 101,302*** 7,472**

SUP 51,49 66,13 117,286*** 1,007 CUST 28,53 47,25 120,308*** 3,989**

HUMRES 30,57 45,98 91,817*** 7,311**

16

The Measurements of performanceThe improvement in the percentage of productive hours in relation to the total

number of hours of direct presence of the workforce (EFIC) is used as an indicator ofthe results related to cost. The indicators of improvement in product quality are; theimprovement in the percentage of returned products over sales (RETURN), theimprovement in the percentage of defective finished products (QFP), and theimprovement in the percentage of defective products in process (QPP). Finally, in orderto determine the level of improvement in the results related to time, the percentage ofdelivery dates complied with (PUNCT), and the time taken from the moment thematerial is received to the moment the product is delivered to the customer (SPEED) areused.

The Empirical ModelIn order to contrast the hypotheses originating from the generic hypothesis

previously established, six similar models are used (as many as there are variables ofmanufacturing results), their only difference being, therefore, the dependent variable.

The basis of the established empirical model is the reasoning put forward byHansen and Wernerfelt (1989). These authors establish a model to determine company’sperformance (of an economic-financial nature in this case) in which they integrate theeconomic and organisational models. On the one hand, variables related to the sector,competition and the company’s resources (the economic model) are included, and onthe other, variables related to human resource management, the organisational climateetc. (the organisational model). The model we have established here is far from being areplica of the one put forward by these authors, however it shares with them the fact ofincluding variables which refer to the two models already mentioned and its character istherefore, one of integration.

The independent variables included in the models are, in the first place , thenatural logarithm of size (SIZE) and the sector (SECTOR), these are control variables,commonly used in this type of models.

With regard to the market and competition, the variables TYPROD (type ofproduct), which define whether the market for the product is made up of othercompanies or consumers, are included, as well as COMPET (the increase in competitionin the last three years). In part it could be said that the plants which have moredemanding customers (the industrial products market), have had to make a greater effortto improve than those which produce consumer goods. It is also to be expected thatthose plants which are faced with increasing competition have to make a greater effortto improve their manufacturing results. This improvement is expected to be moreobvious.

The level of automation[6] (AUTOMAT) being the variable most related to theresources of the companies is included in the model. This is expected to have animportant impact on the behaviour of the manufacturing results (Mac Duffie, 1995).The efficiency of the production system, together with the percentage of defectiveproducts and the flexibility of the production system are, in general, better in the plantswith a greater degree of automation. These should presumably present a more positivedevelopment in their manufacturing performance.

17

The organisational climate (CLIMA) variable is included as a variable of anorganisational nature. There is a series of studies which analyse the impact of differentaspects of the organisational climate on performance (Lawler et al., 1974; Pritchard andKarasick, 1973; Capon et al., 1992), the relation they discover is a positive one. In thisstudy, is measured as the average of three items which define the relations betweenmanagement and employees, the degree of employee identification with the companyand the degree of employee satisfaction. The explanatory variable, which constitutes thecentre of our interest, that is to say, the QM index, can be considered to have the samecharacter. The purpose of this study is precisely to determine whether the effortcompanies make in implementing this type of practices is reflected in the results.

The dependent variable in each model is a dichotomous variable, which has thevalue of “0” for those plants whose manufacturing results have not improved in the lastthree years, the variable takes the value “1” for those plants whose results haveimproved[7]. The hypotheses we have established are contrasted using a logit model,the use of which is determined by the characteristics of its variables. The dependentvariable has a dichotomous nature and many of the independent variables show anabsence of normality[].

Finally, we have contrasted the model by replacing the QM index andintroducing in its place the five dimensions of the index as explanatory variables. Thefactor scores of the six initial measurements of results are used as the dependentvariable RESUL in the regression analysis. This will give us more information as towhich of the five dimensions under study are associated with the improvement inoperational performance

RESULTS

Table 5 shows the average, the standard deviation and the correlations of all thevariables included in the established models. We can observe that, between 59%, in thecase of EFIC and 73% in the case of SPEED, of the plants in the sample show animprovement in their results in relation to those obtained three years earlier. Moreover,all the results variables show an important correlation between each other, whichsuggests that the improvements in results are common to all of them. We have alsoobserved the important correlation of QM with all the results variables. AUTOMAT isalso seen to be highly correlated.

18

Table 5. Descriptive statistics and correlations for the variables included in the models

Variable Mean Std. Dev. 1 2 3 4 5 6 7 8 9 10 11

1. LNTAMAÑO 4,94 0,89

2. AUTOMAT 4,10 2,38 0,309***

3. COMPET 3,54 0,89 -0,029 0,004

4. TIPROD 0,42 0,49 0,025 -0,024 -0,115***

5. CLIMA 7,13 1,32 -0,037 0,205*** 0,050 -0,081**

6. EFIC 0,65 0,48 0,105*** 0,120*** -0,001 0,013 0,068**

7. DEV 0,59 0,49 0,050 0,176*** 0,025 0,065 0,013 0,396***

8. QPT 0,63 0,48 0,103*** 0,141*** 0,048 0,056 0,009 0,383*** 0,655***

9. QFAB 0,64 0,48 0,135*** 0,172*** 0,018 0,036 0,029 0,394*** 0,605*** 0,765***

10. PUNT 0,69 0,46 0,090*** 0,111*** 0,037 0,062 -0,017 0,466*** 0,456*** 0,454*** 0,473***

11. VELO 0,73 0,45 0,135*** 0,228*** 0,046 0,081** 0,080** 0,220*** 0,231*** 0,245*** 0,289*** 0,272***

12. GC 58,38 18,13 0,032 0,460*** 0,032 0,167*** 0,229*** 0,175*** 0,134*** 0,191*** 0,235*** 0,228*** 0,290***

** p ≤ 0,05 *** p ≤ 0,01

19

Table 6. Results of the logit analysis for EFIC and RETURN

EFIC DEV

Model 1 Model 2 Model 1 Model 2

b s.d. b s.d. b s.d. b s.d.

CONSTANT -2,8644*** 0,8389 -2,6688** 1,2493 -0,6253 0,7843 0,4713 1,1393

TYPROD -0,1617 0,2041 0,0511 0,2933 0,1918 0,1964 0,4833* 0,2748

AUTOMAT 0,0587 0,0382 0,0110 0,0612 0,1477*** 0,0377 0,0717 0,0578

SIZE 0,3091*** 0,1120 0,0699 0,1676 0,0787 0,1008 -0,1007 0,1486

COMPET 0,0794 0,0947 0,0498 0,1341 0,0626 0,0907 0,1011 0,1264

CLIMA 0,1521** 0,0647 0,1934* 0,1009 -0,0024 0,0612 -0,1053 0,0923

QM 0,0156* 0,0082 0,0144* 0,0077

Pseudo-R2 10,1 16,9 8 11,1

χ2 54,135*** 46,700*** 43,172*** 31,469***

Log L -430.95 -204,05 -456,26 -231,51

% corrects 65,64 70,91 63,79 66,4

N 716 361 707 369* p ≤ 0,1 ** p ≤ 0,05 *** p ≤ 0,01

20

Table 7. Results of the logit analysis for QFP and QPP

QFP QPP



CONSTANT -1,5895** 0,8048 -1,2664 1,2550 -1,7649** 0,8175 -1,6061 1,2794

TYPROD 0,1892 0,2008 0,3359 0,2966 -0,1197 0,2028 0,0243 0,2985

AUTOMAT 0,1054*** 0,0377 0,0467 0,0613 0,1098*** 0,0382 0,0366 0,0628

SIZE 0,1848* 0,1059 0,0712 0,1702 0,2335** 0,1083 0,0809 0,1742

COMPET 0,1995** 0,0930 0,3080** 0,1384 0,1956** 0,0953 0,2796** 0,1419

CLIMA -0,0112 0,0629 -0,1130 0,1053 0,0132 0,0632 -0,1077 0,1079

QM 0,0195** 0,0086 0,0284*** 0,0089

Pseudo-R2 9,4 18,7 12,7 23

χ2 51,115*** 51,644*** 69,372*** 64,946***

Log L -445,54 -198,49 431,48 -193,38

% corrects 65,70 69,70 66,11 73,15

N 723 363 720 365* p ≤ 0,1 ** p ≤ 0,05 *** p ≤ 0,01

21

Table 8. Results of the logit analysis for PUNCT and SPEED

PUNCT SPEED



CONSTANT -0,9696 0,8070 -0,6844 1,2629 -1,9211** 0,8527 -0,3733 1,3408

TYPROD 0,0971 0,2016 0,5190* 0,2988 0,1837 0,2133 -0,0417 0,3133

AUTOMAT 0,1071*** 0,0383 0,0222 0,0608 0,2479*** 0,0434 0,1605** 0,0662

SIZE 0,1693 0,1071 0,0923 0,1689 0,1964* 0,1162 0,0071 0,1800

COMPET 0,1200 0,0939 0,0772 0,1378 0,1853* 0,0986 0,0677 0,1433

CLIMA -0,0297 0,0635 -0,1606 0,1063 0,0118 0,0654 -0,1037 0,1061

QM 0,0339*** 0,0087 0,0276*** 0,009

Pseudo-R2 7,6 15,4 15,8 21

χ2 41,750*** 42,857*** 91,199*** 60,292***

Log L -444,93 -202,24 -414,50 -192,25

% corrects 69,40 74,41 73,48 76,20

N 755 379 792 395* p ≤ 0,1 ** p ≤ 0,05 *** p ≤ 0,01

Tables 6, 7 and 8 show the results obtained for each of the six establishedmodels. For each dependent variable we established two models, the central explanatoryvariable (QM) was not included in the first model but was in the second. Our aim was toshow the impact of this variable on the explanatory capacity of the model. Finally, table9 presents the results of the multiple regression analysis which shows the impact of eachof the dimensions on the global indicator of the improvement in manufacturingperformance.

The results we have obtained fully confirm each of the hypotheses, as can beseen in tables 6,7 and 8. It can be stated that a statistically significant relationship existsbetween the set of QM practices and the improvement in manufacturing performance.This relationship is a positive one (the signs of the QM coefficients are positive in allthe cases). In other words, the plants with a greater degree of implementation of QMpractices, have a greater probability of improving their manufacturing performance.These results coincide with the majority of the studies which have dealt with thissubject in one way or another (see table 1).

The relationship between QM and performance is significant in all theestablished models, but the level of significance is higher for the relationship with theresults based on time( in all cases below five per thousand). This is somewhat surprisingas, in the beginning, one assumes that the association with the quality results and eventhe cost results, is going to be the most significant.

The R2 values, although discreet, are considerably better than those obtained insimilar studies, such as those of Adam (1994), Forker (1997) and Adam et al.,(1997)[],and are similar to those shown in the sudies by Samson and Terziovski (1999), Ahire etal. (1996) and Black and Porter (1996). In all cases, the value of χ2 indicates that thevalue of the coefficients is significantly different to zero in its entirety. The introductionof the QM variable ostensibly improves the explanatory capacity of the model (R2), inall cases[].

If the rest of the variables included in the model are analysed, few significantrelationships with the improvement in results are seen, except in the case of theAUTOMAT variable (level of automation). For the majority of the initial models(except for EFIC), a statistically significant association with the improvement of theresults can be seen, as well as a positive coefficient sign. This means that, thosebusinesses with a greater degree of automation have an increased probability ofimproving their manufacturing performance. Nevertheless, in all cases, when the QMvariable is introduced into the model, this is reduced or even disappears, which is aconsequence of the important correlation between the two variables.

There are some significant associations for the other variables[]. Thus, we find apositive relationship between the organisational climate and the improvement in theindicator of productive efficiency, which was to be expected. On the other hand, nosignificant relationship with the other variables has been seen. The increase incompetition over the last few years has served to improve two of the qualityperformance under study (QFP and QPP). Nevertheless, we have found no relationshipfor the other performance variables

The results of the regression analysis of the five QM elements on performance

provide some interesting insights (table 9). Two of the variables, Desing and HumanResources Management, proved to be strongly significant and positively related toperformance. The other three variables were shown to be either not significantly related(Process, Suppliers and Customers). This is not to say that the three factors should beignored but rather to note that these weaker dimensions of QM did not powerfullydistinguish the highs from the low performers.

Table 9. Multiple regression analysis predicting RESUL

Beta stand. t Student

CONSTANT 0,045*** -3,456

TYPROD 0,054 0,723

AUTOMAT 0,085 1,381

SIZE 0,030 0,515

COMPET 0,054 1,040

CLIMA 0,034 0,595

DESPROD 0,123** 2,051

PROC 0,044 0,623

SUP 0,081 1,205

CUST -0,027 -0,402

HUMRES 0,164** 2,573

Adjusted R2 0,126

F 3,314***

Our finding that the operational performance is most closely affected by “humanresource management practices” is consistent with the findings of Ahire and Golhar(1996) and Samson and Terziovski (1999). These result suggest that the key toperformance lies not in Process and Suppliers and Customers tools and techniques butin the different aspects of Human Resource Management like involvement,empowerment, training and information sharing. The R2 value reported by our model islower than the reported by Samson and Terziovski (1999) in their study. Nonethelessthis value is acceptable and similar to other studies about QM and performance.Certainly there is an important unexplained variance but this is normal in this type ofmodels.

CONCLUSIONS

The purpose of this study was to analyse the relationship between the QMpractices and the results. The information we have obtained is taken from almost onethousand manufacturing plants employing more than fifty workers in Spain. Firstly, wehave undertaken a revision of the studies which analyse the relationship between the

adoption of specific QM practices and the results. These studies, which have onlyrecently appeared, are now becoming more common. They are of a heterogeneousnature, both in terms of the QM practices in the field of application and in terms of themeasurements of results used.

Six hypotheses are contrasted (one for each performance measurement) and alogit model is estimated for each one. In all cases, the global variable which representsthe total implementation of quality practices (QM) is significant in relation to theimprovement in performance. The introduction of this variable into the modelostensibly improves their explanatory capacity. In the same way, the relationship ismore significant with the based on time performance than with those related to qualityor cost. Finally, we established a multiple regression analysis in order to determinewhich of the components of the QM index influence the improvement results. We havefound that the practices related to the design and development of the product as well asthe human resource management practices are the ones which have a statisticallysignificant impact.

One limitation is the study´s cross-sectional research design. Although the datashowed a significant QM-performance correlation, they did not strictly prove that QMcaused performance to increase, but only that an association existed. High performanceimprovement may give rise to QM programs, or QM and performance may both becaused by some third factor not measured in this study (although, based on previousresearch, the most powerful known explanatory factors were included in the study). Themost probable explanation is that a relation between QM and performace exist but alongitudinal study would be required to support a causal inference strictly.

This study contains findings useful to both practising managers and otherresearchers. The message for the managers is that they must insist on theimplementation of quality programmes. Efforts must be made in a dual sense. On theone hand, they must develop good projects to design and develop products in order toavoid problems later on at the production stage. In the same way, they must encouragehuman resource practices such as, empowerment, involvement, training and informationsharing, since here seems to lie one of the keys to success in these programmes.

Finally, we can conclude that the implementation of QM practices is related tothe improvement in all of the manufacturing results we have analysed. This conclusion,based on the analysis of an important number of companies in Spain, may helpbusinesses to further establish improvement processes, resulting from the application ofthe practices included in the framework of Quality Management.

This study makes several contributions to quality management research. Firstly,it reinforces some of the results obtained in similar investigations carried out in otherplaces. Secondly, it studies the relationship with the different types of measurements ofoperational performance in greater depth. Moreover, the consideration of otherexplanatory variables apart from those related to QM, means that the results obtainedhave a greater validity.

Notes

[1] Cited by Powell (1995)

[1] Some of these items have been defined, in turn, as the sum of the dichotomous variables. In each case,we have determined their one-dimensional nature by using a principal component analysis adapted to thedichotomous variables (PRINCALS in SPSS 7.5).[1] Bollen (1989) gives some examples for cause indicators. Time spent with family and time spent withfriends are cause indicators of the latent variable of time in social interaction and race and sex are causeindicators of exposure to discrimination. In the first example, time spent with family and friends need notcorrelate (hence a low coefficient alpha), but they add up to form the construct of “social interaction”.Similarly, race and sex need not correlate with one another. In contrast to effect indicators, in causeindicators the latent variable is the effect of the observed variables rather than vice versa.[1] We have to previously verify that the data is suitable for the analysis. We must therefore, check thatthe data is sufficiently correlated (Hair et al., 1995). The two tests we undertook, Bartlett’s spherical testand the measurement of sample suitability KMO confirm the suitability of our data.[1] In order for an ANOVA to be considered formally valid, three conditions must be fulfilled: theindependence of sample observations, the normality of the independent variables, and the equality ofvariances between the groups (Hair et al., 1995). The first condition is satisfied. The non-fulfilment of thenormality hypothesis would hardly have any impact in samples of such a large size, as is the case here.With regard to the homogeneous nature of the variances, this is not fulfilled in all cases. Nevertheless,according to Hair et al. (1995) if very high levels of significance are obtained, (0,000 in our case), thenon-fulfilment of the test of homogeneity of variances has no repercussion on the analysis.[1] AUTOMAT is created as an ordinal variable with four levels from the arithmetical average of thevalue of the four items in which the following are indicated: the extent of implementation of robots orPLC’s, automatic systems for storing and handling material, CIM and information webs for the treatmentof production data. The value of the Cronbach a coefficient is 0,78 and the principal component analysiswe carried out of the confirmed the existence of a single component with a true value superior to 1 (2,46).The factor weights of the four items on this component are high (the lowest 0,740)[1] The rank of the initial variables (in the questionnaire) is one to five.[1] According to Maddala (1983), if the independent variables are distributed normally, the estimator ofthe discriminating analysis is the true estimator of maximum likelihood and therefore, is asymptoticallymore efficient than the true estimator of maximum likelihood of the logit. However, if the independentvariables are not normal, the estimator of the discriminating analysis is not consistent, whereas the logitestimator is consistent and therefore, is more robust.[1] In the study by Flynn et al. (1995a) the R2 values obtained are very high, but the number of cases(plants) was considerably inferior (37 and 40 cases).[1] Six logit models with QM as the only independent variable have also been estimated. In all cases, therelationship with the improvement in performance was positive and highly significant[1] In our comments on the results of the analysis for these variables, we are referring to the models inwhich the QM variable is incorporated.

REFERENCES

Adam, E.E.Jr.; Corbett, L.M.; Flores, B.E.; Harrison, N.J.; Lee, T.S.; Rho, B.H.;Ribera, J.; Samson, D. and Westbrook, R. (1997), “An International Study of QualityImprovement Approach and Firm Performance”, International Journal of Operations &Production Management, Vol. 17, nº 9, pp. 842-873.

Adam, E.E. Jr. (1994), “Alternative Quality Improvements Practices andOrganization Performance”, Journal of Operations Management, Vol. 12, nº 1, pp. 27-44.

Ahire, S.L. y Golhar, D.Y. (1996), “Quality Management in Large vs SmallFirms”. Journal of Small Business Management, Abril , pp.1-13.

American Quality Foundation and Ernst & Young (1991), International QualityStudy: The Definitive Study of the Best International Quality Management Practices,Ernst & Young, Cleveland.

Azzone, G.; Masella, C and Bertelé, U. (1991): “Design of Performancemeasures for Time-Based Companies”, International Journal of Operations &Production Management, Vol. 11, nº 3, pp. 77-85.

Black, S.A. and Porter, L.J. (1996), “Identification of the Critical Factors ofTQM”, Decision Sciences, Vol. 27, nº 1, pp. 1-21.

Blackburn, J.D. (1991), Time -Based Competition: The Next Battle-Ground inAmerican Manufacturing, Irwin, Homewood.

Bollen, K.A. (1989), Structural Equations with Latent Variables, John Wiley &Sons, Inc., Nueva York.

Bollen, K.A. and Lennox, R. (1991), “Conventional Wisdom on Measurement:A Structural Equation Perspective”, Psichological Bulletin, Vol. 110, nº 2, pp. 305-314.

Capon, N.; Farleuy, J.U.; Lehmann, D.R. and Hulbert, J.M. (1992), “Profiles ofProduct Innovators Among Large U.S. Manufacturers”, Management Science, Vol. 38,nº 2, pp. 157-169.

Choi, T.Y. and Eboch, K. (1998), “The TQM Paradox: Relations among TQMpractices, Plant Performance and Customer Satisfaction”, Journal of OperationsManagement, 17, pp. 59-75.

Corbett, C. and Van Wassenhove, L. (1993): “Trade-offs? Whay Trade-offs?.Competence and Competitiveness in Manufacturing Strategy”. California ManagementReview, Vol. 35, nº 4, pp. 107-122.

De Vellis, R.F. (1991), Scale development: Theory and Applications, SagePublications, Newbury Park, California.

Deming, W.E. (1982), Out of the crisis. Quality, Productivity and CompetitivePosition, University Press, Cambridge.

Ebrahimpour, M. and Johnson, J.L. (1992), “Quality, Vendor Evaluation andOrganizational Performance: A Comparison of U.S. and Japanese Firms”, Journal ofBusiness Research, 25, pp. 129-142.

Filippini, R.; Forza, C. and Vinelli, A. (1998), “Trade-off and CompatibilityBetween Performance: Definitions and Empirical Evidence”, International Journal ofProduction Research, Vol. 36, nº 12, pp. 3379-3406.

Flynn, B.B.; Sakakibara, S.; Schroeder, R.G.; Bates, K.A. and Flynn, E.J.(1990), “Empirical Research Methods in Operations Management”, Journal ofOperations Management, Vol. 9, nº 2, pp. 250-284.

Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1994), “A Framework forQuality Management Research and an Associated Measurement Instrument”, Journal ofOperations Management, 11, pp. 339-366.

Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1995a), “Relationship BetweenJIT and TQM: Practices and Performance”, Academy of Management Journal, Vol. 38,nº 5, pp. 1325-1360.

Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1995b), “The Impact ofQuality Management Practices on Performance and Competitive Advantage”, DecisionSciences, Vol. 26, nº 5, pp. 659-691.

Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1995c), “Determinants ofQuality Performance in High and Low Quality Plants”, Quality Management Journal,Winter, pp. 8-25.

Forker, L.B. (1997), “Factors Affecting Supplier Quality Performance”, Journalof Operations Management, 15, pp. 243-269.

Forza, C. (1995), “The Impact of Information Systems on Quality Performance.An Empirical Study”, International Journal of Operations & Production Management,Vol. 15, nº 6, pp. 69-83.

Hackman, R. and Wageman, R. (1995), “Total Quality Management: Empirical,Conceptual and Practical Issues”, Administrative Science Quarterly, Vol. 40, pp. 309-342.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1995), Multivariatedata analysis, Fourth Edition, Prentice Hall, Upper Saddle River, New Jersey.

Hansen, G.S. and Wernerfelt, B. (1989), “Determinants of Firm Performance:The Relative Importance of Economic and Organizational Factors”, StrategicManagement Journal, Vol. 10, pp. 399-411.

Hendricks, K.B. and Singhal, V.R. (1997), “Does Implementing an EffectiveTQM Program Actually Improve Operating Performance? Empirical Evidence fromFirms that Have Won Quality Awards”, Management Science, Vol. 43, nº 9, pp. 1258-1274.

Ittner, C.D. and Larcker, D.F. (1996), “Total Quality Management and theChoice of Information and Reward Systems”, Journal of Accounting Research, Vol. 33,Suplemento 1995, pp. 1-34.

Kaplan, R.S. and Norton, D.P. (1992), “The Balanced Scorecard - Measures thatDrive Performance”, Harvard Business Review, Enero - Febrero, pp. 71-79.

Lawler III, E.E. and Hall, D.T. (1974), “Organizational Climate: Relationship toOrganizational Structure, Process and Performance”, Organizational Behaviour &Human Performance, Vol. 11, nº 1, pp. 139-155.

Lawler III, E.E.; Mohrman, S.A. and Ledford Jr., G.E. (1995), Creating HighPerformance Organizations: Practices and Results of Employee Involvement and TotalQuality Management in Fortune 1000 Companies, Jossey-Bass Publishers, SanFrancisco.

Leal, A. (1997), “Total Quality Management in Spanish Companies: An Culturaland Performance Analysis”, Revista Europea de Dirección y Economía de la Empresa,Vol. 6, nº 1, pp. 37-56.

Macduffie, J.P.(1995), “Human Resource Bundles and ManufacturingPerformance: Organizational Logic and Flexible Production Systems in the World AutoIndustry”, Industrial and Labor Relations Review, Vol. 48, nº 2, pp. 197-221.

Maddala, G.S. (1983), Limited Dependent and Qualitative Variables inEconometrics, Cambridge University Press, Nueva York.

Madu, C.N.; Kuei, C.H. and Jacob, R.A. (1996), “An Empirical Assessment ofthe Influence of Quality Dimensions on Organizational Performance”, InternationalJournal of Production Research, Vol. 34, nº 7, pp. 1943-1962.

Mann, R. and Kehoe, D. (1994), “An Evaluation of the Effects of QualityImprovements Activities in Business Performance”, International Journal of Qualityand Reliability Management, Vol. 11, nº 4, pp. 29-44.

Martínez, A.M. (1996), Quality Management in Operations. Theoric Review andthe Analysis of his Implantation and Performance in Spain. Doctoral thesis unpublished.University of Murcia.

Neely, A.; Gregory, M. and Platts, K. (1995), “Performance MeasurementSystem Design. A Literature Review and Research Agenda”, International Journal ofOperations & Production Management, Vol. 15, nº 4, pp. 80-116.

New, C. (1992), “World Class Manufacturing Versus Strategic Trade-Offs”.International Journal of Operations & Production Management. Vol. 12, nº 6, pp. 19-31.

Nunnally, J.C. (1978), Psychometric Theory, Mc Graw Hill, Nueva York.Powell, T.C. (1995), “Total Quality Management as Competitive Advantage: A

Review and Empirical Study”, Strategic Management Journal, Vol. 16, pp. 15-37.Pritchard, R.D. and Karasick, B.W. (1973), “The effects of Organizational

Climate on Managerial Job Performance and Job Satisfaction”, OrganizationalBehaviour & Human Performance, Vol. 9, nº 1, pp. 126-146.

Samson, D. and Terziovski, M. (1999), “The Relations Between Total QualityManagement Practices and Operational Performance”, Journal of OperationsManagement, 17, pp. 393-409.

Saraph, J.V.; Benson, P.G. and Schroeder, R.G. (1989), “An Instrument forMeasuring the Critical Factors of Quality Management”, Decision Sciences, Vol. 20, nº4, pp. 810-829.

Stalk, G. (1988),. “Time: The Next Source of Competitive Advantage”, HarvardBusiness Review, July-August, pp. 41-51.

Terziovski, M.; Samson, D. and Dow, D. (1997), “The Business Value ofQuality Management Systems Certification. Evidence from Australia and NewZealand”, Journal of Operations Management, 15, pp. 1-18.

U.S. General Accounting Office (1991), Management Practices: U.S. CompaniesImprove Performance Through Quality Efforts, U.S. General Accounting Office,Gaithersburg, MD.

Venkatraman, N. and Ramanujan, V. (1986), “Measurement of BusinessPerformance in Strategy Research: A Comparison of Approaches”, Academy ofManagement Review, Vol. 11, nº 4, pp. 801-814.

Venkatraman, N. y Ramanujan, V. (1987), “Measurement of Business EconomicPerformance: An Examination of Method Convergence”, Journal of Management, Vol.13, nº 1, pp. 109-122.

Wilkinson, A.; Redman, T.; Snape, E. and Marchington, M. (1998), Managingwith Total Quality Management. Theory and Practice, MacMillan Business, Londres.

APPENDIX 1

Distribution of the sample by sector and sizeSECTOR 50-199 200-499 500 or

moreTotal

Food, drinks and tobacco 100 31 15 146Textiles, clothing, leather and footwear 97 15 6 118Wood and cork 25 2 0 27Paper, publishing and graphic arts 52 14 5 71Chemical industry 50 12 8 70Rubber and plastics 46 8 4 58Non-metallic mineral products 54 8 4 66Primary metal industries and fabricatedmetal products

91 13 14 118

Machinery and mechanical equipment 52 12 8 72Electrical material and equipment,electronics and optics

38 19 13 70

Transport material 40 23 27 80Miscellaneous manufacturing industries 49 8 2 59Total 694 160 96 965

Percentage of population in the sample by sector and sizeSECTOR 50-199 200-499 500 or

moreTotal

Food, drinks and tobacco 12,53 16,67 28,85 14,09Textiles, clothing, leather and footwear 13,94 19,48 54,55 15,05Wood and cork 17,36 16,67 17,31Paper, publishing and graphic arts 12,65 20,29 55,56 14,52Chemical industry 14,75 10,17 22,86 14,23Rubber and plastics 14,56 24,24 28,57 15,98Non-metallic mineral products 13,27 11,76 40 13,61Primary metal industries and fabricatedmetal products

15,88 13,13 48,28 16,83

Machinery and mechanical equipment 13,83 19,05 42,11 15,72Electrical material and equipment,electronics and optics

13,67 19,59 39,39 17,16

Transport material 17,70 26,44 48,21 24,39Miscellaneous manufacturing industries 20,33 32,26 21,69Total 14,44 17,55 39,55 16,05

APPENDIX 2

ITEMS INCLUDED

Design and new products development (DESPROD)

1. The extent to which the customers’ requirements are taken into account(CUSDES)

2. The extent to which the suppliers’ suggestions are taken into account.(SUPDES)3. The extent to which technical difficulties in production are taken into account(DESMANF)4. The degree of implementation of the value analysis. (VALAN)

Processes (PROC)

1. The processes are statistically controlled. (SPC)2. There are standardised instructions for the workers. (NORMA)3. Systems to prevent errors are used (“poka-yoke”) (POKA)4. Emphasis is placed on the maintenance of order and cleanliness in the plant.(ORDER)5. The plant has a system of preventive maintenance. (PREVENT)6. Instruments for control and quality improvement are used (INSTRU)

Suppliers (SUP)

1. We put quality before any other criterion of selection (QSUP)2. Audits are regularly carried out to evaluate the company (EVSUP)3. We collaborate in technical aspects related to production (COLSUP)4. We have established systems for the elimination of the inspection of suppliedparts. (QAGSUP)

Customers (CUST)

1. Questionnaires are carried out to determine the level of satisfaction of ourproducts (SURCUS)2. Audits are regularly carried out to evaluate our company (EVCUS)3. We collaborate in technical aspects related to production (COLCUS)4. We have established systems for the elimination of the inspection of suppliedparts (QAGCUS)

Human Resources (HUMRES)

1. The number of training hours per worker, per year (TRAIN)2. Involvement (INVOL)

2.1. There is a suggestion system in the plant (SUG)2.2 There are improvement groups in the company (GRUP)2.3. The ability to work in teams is given priority as a criterion ofpersonnel selection (SELEC)2.4. Training related to work group techniques and problem solving isgiven (TRAINQ)

3. Empowerment(EMPOW)3.1. They prepare the machines they use (PREP)

3.2. They do the maintenance in the plant (MAINT)3.3. They analyse the data obtained in their job (ANADAT)3.4. They plan and organise their work in an autonomous way (PLAN)

4. Information sharing (INFO)4.1. Attitude surveys (SURV)4.2. Informative meetings held with employees (MEET)4.3. Open days (OPEN)4.4. Information boards about data production (BOARD)