Published on

Published in: Business, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. D O C U M E N T O D E T R A B A J O W O R K I N G P A P E R S S E R I E S ••••••••••••••••••••••••••••••••••••••••••••••••••• QUALITY MANAGEMENT PRACTICES AND OPERATIONAL PERFORMANCE: EMPIRICAL EVIDENCE FOR SPANISH INDUSTRY Javier Merino DT 40/00 ••••••••••••••••••••••••••••••••••••••••••••••••••• Universidad Pública de Navarra Nafarroako Unibersitate Publikoa Campus de Arrosadía, 31006 Pamplona, Spain Tel/Phone: (+34)948169400 Fax: (+34)948169404 E-mail: working.papers.dge@unavarra.es
  2. 2. 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: jmerino@unavarra.es ∗ I thank Fundación BBVA for the financing provided
  3. 3. Quality Management Practices and Operational Performance: Empirical Evidence for Spanish Industry The progressive implantation of the ideas and techniques related to the concept of Quality Management is perhaps the most patent expression of the change and innovation which has taken place in organisations in recent years. The aim of the companies is to improve their competitiveness by improving their operational performance. Is this in fact the case? The purpose of our study is to provide an answer to this question. Our study is based on information we have obtained from an extensive sample of industrial plants (965), employing more than fifty workers and belonging to all sectors of the manufacturing industry in Spain. After a thorough revision of the literature relevant to this issue, we contrasted the hypotheses using logit models. We also used a multiple regression analysis in order to ascertain which of the QM practices had the biggest influence. Our results are consistent with those of the majority of the studies carried out to date. They demonstrate that there is a significant relationship between the level of implementation of QM practices and the improvement in operational performance, in terms of cost, quality and flexibility. The QM practices related to product design and development, together with human resource practices, are the most significant predictors of operational performance. Key Words: Quality Management, Performance, Manufacturing, Empirical Analysis INTRODUCTION Spanish manufacturing businesses have been very favourable to the adoption of different practices related to QM over the last few years. In an increasingly competitive global marketplace, Spanish businesses have needed to make greater efforts to improve their 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 the 80’s and despite the abundant literature on QM, empirical studies of the relationship between QM practices and operational performance did not start to appear until after 1994. Moreover, few of them use large samples of manufacturing businesses from different sectors. The purpose of this study, which is based on extensive fieldwork, is to provide new evidence to consolidate previous findings as well as guidance for managers working in this field. 3
  4. 4. The study seeks to discover whether the implementation of QM practices has an impact on the improvement in operational performance and to find out in which areas the influence is greatest. In order to do so, we have firstly carried out a revision of the literature related to this issue. Next, the conceptual framework was established in order to define the indicators which measure the implementation of QM practices. Once the hypotheses were established, the variables were set in the empirical model in which they were to be contrasted. Finally, the results obtained were analysed and the conclusions of the study were reached. The large sample of industrial plants from all sectors means that the results have a greater solidity and lend themselves to a greater generalisation. They further consolidate the findings in this field. The geographical area of the study, (different from others, which are, in the majority in the Anglo-Saxon countries), the incorporation of the results taken from the whole of the manufacturing industry, as well as the introduction of other explanatory variables, not previously included in other studies, (the level of automation, the organizational climate etc.) contribute in an important way to the field of QM. THE RELATIONSHIP BETWEEN QUALITY AND PERFORMANCE: A REVISION OF THE LITERATURE The relationship between the adoption by companies of certain practices and performance is the subject of constant interest among researchers in the field of business management. The implementation of any kind of practice represents a cost for the company, both in terms of human and material resources. If the efforts made with regard 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 to distinguish between QM practices (input) and quality results (output). Over the last few years, there have been a number of studies which have tried to relate QM practices with different operational results, including quality results, among others. In the majority of the different empirical studies on QM this issue appears, in one form or another, (Ebrahimpour and Johnson 1992; Flynn, Schroeder and Sakakibara, 1994,1995a, 1995b and 1995c; Adam, 1994; Hendricks and Singhal, 1997, etc.). There are several studies of an empirical nature, undertaken by different institutions or firms of consultants for informative purposes, the conclusions of which indicate that the companies which adopt total quality models obtain better results (U.S. GAO, 1991; American Quality Foundation, 1991[1] ). However, precaution must be shown in regard to these conclusions as these studies can be partial and not scientifically 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 to quality and the role of quality in strategic planning, the resulting operational performance were of a diverse nature. Later, Flynn et al. (1994) in their attempt to create 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 quality performance and found a strong relationship between them. From that moment on, 4
  5. 5. different empirical studies have appeared over the years. Table 1 shows these studies and includes the names of the authors, the samples’ characteristics, the explanatory variables, the measurements used for the results, the methodology and the main conclusions. These studies have been published recently, after 1994, except for one particular case. On the whole, they are based on data from American companies and almost all the cases centre exclusively on one sector of manufacturing. The explanatory variables, although in a different way, are measurements of QM (several dimensions of the concept). No other control variables are introduced in the majority of the studies. All the cases use measurements of operational performance and, what is more, in 50% of the studies economic-financial results are used, with a predominance of measurement performance of a subjective nature. The methodology used for the analysis is varied, although the multiple regression analyses are most common. In general, the relationship between the QM practices and the results is positive, that is to say, the greater the level of implementation, the better the results obtained. Nevertheless, the significance of some dimensions is not the same as for others, nor is the repercussion the same on the operational performance or the economic-financial results. All the studies present transversal type data and have the methodological limitations peculiar to this type of study. 5
  6. 6. Table 1. Summary of papers about the relation between QM practices and performance SAMPLE EXPLANATORY PERFORMANCE METHODOLOGY ASSOCIATION WITH VARIABLES MEASURES PERFORMANCE Ebrahimpour 222 American and Commitment and quality Operational and financial Causal model Positive for Japanese, no y Johnson (1992) Japanese manufacturing strategy performance for American. companies (three industries) Flynn et al. (1994) 42 American Seven QM dimensions Operational performance Canonical correlation Positive manufacturing plants (quality performance) (three industries) 706 valid responses Adam (1994) 187 American Five QM dimensions Operational and financial Stepwise regression Some significative manufacturing companies performance relations. Mann y Kehoe 211 English Six quality improvements Operational and financial Descriptive statistics Positive (1994) manufacturing practices performance. companies. Powell (1995) 54 American Twelve QM dimensions Financial performance Correlation analysis Positive (intangibles manufacturing and specially) service firms Flynn et al. (1995a) 42 American Eight QM dimensions Operational performance Multiple regression Positive manufacturing plants analysis (three industries) 706 valid responses Flynn et al. (1995b) 42 American Eight QM dimensions Operational performance Path analysis Positive manufacturing plants (three industries) 706 valid responses Flynn et al. (1995c) 42 American Four QM dimensions Operational performance Discriminant analysis Non lineal manufacturing plants (three industries) 706 valid responses Forza (1995) 34 manufacturing plants Five QM practices and Operational performance Canonical correlation Positive in Italie (two industries) eight variables related to analysis information systems Lawler III et al. 279 Fortune 1000 Three groups QM Operational and financial Corrrelation analysis Positive with some (1995) manufacturing and practices (14 practices) performance Regression analysis exceptions service companies 7
  7. 7. Table 1. (Continuation) SAMPLE EXPLANATORY PERFORMANCE METHODOLOGY ASSOCIATION WITH VARIABLES MEASURES PERFORMANCE Ittner y Larcker 249 companies in Twelve QM factors and Operational and financial Multiple regression No association found (1996) Germany, Canada, USA seven information and performance and Japan (two industries) reward systems variables Martínez (1996) 217 Spanish One QM index Operational and financial Non parametric tests Positive in some cases manufacturing plants performance (Kruskal-Walli, Mann and Whitney) Madu et al. (1996) 165 American Three QM indexes Operational performance Correlation analysis Positive manufacturing and service companies Leal (1997) 113 Spanish Ten QM dimensions Finance performance Correlation analysis No with one measure, yes manufacturing and service with others. companies Forker (1997) 264 American companies Seven QM dimensions Operational performance Multiple regression Positive (one industriy) and relative efficiency Adam et al. 977 manufacturing and Nine QM dimensions. Operational and financial Stepwise regression Stronger with operational (1997) service companies in nine performance than financial countries. performance Terziovski (1997) 1341 manufacturing firms ISO 9000 Certification Operational and financial MANOVA and Negative in Australia and New TQM environment performance ANCOVA Zealand (all industries) Size Choi y 339 American Four TQM dimensions Operational performance Structural equations Positive Eboch(1998) manufacturing firms (two Customer satisfaction model industries) Wilkinson et al. Summary of six studies in Several Several Descriptive statistics Various results, although (1998) UK there are more positive impacts Samson y 1024 manufacturing firms Six TQM dimensions Operational performance Regression analysis Three dimensions Terziovski (1999) in Australia and New Customer satisfaction positively related Zealand (all industries) Employers satisfaction (Leadership, Human Resources and Customer Focus), the rest not related 8
  8. 8. THE CONCEPTUAL FRAMEWORK AND THE ESTABLISHMENT OF THE HYPOTHESES Quality Management Practices In order to carry out the empirical investigation we have established the conceptual framework shown in figure 1 as our basis. Within this framework, there are five dimensions or five sets of practices, associated with product design, the transformation process, relations with the suppliers, relations with the customers, and human resource management. They are considered to be primary dimensions in the sense that they are directly related to improvement in the quality of the product. The same dimensions appear, more or less explicitly, in almost all of the studies on quality management, (Deming 1986; Saraph et al., 1989; Hackman and Wageman, 1995; Flynn et al, 1994 and 1995; Powell, 1995; Black and Porter, 1996; Ahire and Golhar, 1996 etc.). Based on the study by Flynn et al. (1995c) the difference is that we incorporate one of their dimensions (“workforce management”) and part of another (“information management”) into a single dimension of human resource practices in relation to quality. The other part of the last dimension is incorporated into Processes. The framework we have established reflects the idea expressed by Ahire and Golhar (1996). They are of the opinion that commitment on the part of management must be seen by implementing a set of strategies which take into account three important stakeholders in the operations of the organisation: customers, suppliers and employees. Customer attention is very important for an efficient QM initiative. The quality of the material supplied by suppliers who are competent, reliable and flexible is a prerequisite for the quality of the finished product. The strategies which allow the company to produce high quality products starting from supplies of quality comprise of the following; the introduction of quality in the design of the products, quality assurance in the processes through the use of different instruments and the judicious use of external and internal information. Nevertheless, the key to success lies in human resource management through the empowerment of employees and the creation of a structure which promotes their participation and training. 9
  9. 9. Figure 1. Conceptual Framework for Quality Management Practices. Adapted from Flynn et al. (1995c) SUPPLIERS DESIGN PROCESS HUMAN RES. CUSTOMERS Operational Performance The subject of the measurement, evaluation and conceptualising of operational performance in a company is a recurrent theme in the different sections of the academic literature. One of the first general classifications, which has been widely used, is that of Venkatraman and Ramanujan (1986). This adopts a Strategic Management perspective and focuses on the measurement to establish a division between financial and operational performance, with the emphasis on the latter. Following a similar line, Kaplan and Norton (1992) believe that the traditional measurements of financial performance are no longer valid for today’s business demands. Therefore, they consider that operational measurements for management are needed in relation to; customer satisfaction, internal processes and the activities concerning improvement and innovation in the organisation, which lead to future financial returns. Manufacturing performance, which include part of the operational performance previously mentioned, are commonly used in the field of Operations Management. This type of results takes into account the company’s performance in reaching its basic objectives, that is, productivity, quality and service. There are several studies which aim to establish a classification of this kind of results (Corbett and Van Wassenhove, 1993; Neely et al., 1995; Filippini et al., 1998). 10
  10. 10. It is important to explain the two characteristics of the measurements of the performance we have used in this study. First of all, they are relative measurements of the 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 of heterogeneous plants, even when the sector variable is introduced as a control variable. The other noteworthy characteristic is the subjectivity of the information used. Results of a subjective kind are often used in research on organisation. Some studies have shown that there is an important relationship between the objective economic-financial performance and the same measured in a subjective way. This may serve as a justification for the use of this kind of performance (Venkatraman and Ramanujuan, 1986; Powell, 1995). For the manufacturing performance we have adopted Corbett and Van Wassenhove’s model (1993), which considers the measurements of the performance in three dimensions (cost, quality and time) although we use other individual measurements. The indicator for cost results we used is the percentage of productive hours in relation to the total number of hours the workforce is directly present. It reflects the waste and inefficiency of the productive system and states the unproductive moments owing to organisational problems (lack of material, breakdowns, problems with quality etc.). The three indicators of improvement in quality performance correspond 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 external perspective (percentage of returned products). The time factor has been considered as representing a competitive advantage over the last few years, and is a fundamental measurement of manufacturing performance (Stalk, 1988; Blackburn, 1991; Azzone et al., 1991). The reduction in the time taken from the moment the material is received to the moment the product is delivered to the customer serves as an indicator of the speed of the processes. In the same way, the percentage of delivery dates complied with is a typical measurement of punctuality (Filippini et al., 1998), considered a basic aspect of customer attention. The Establishment of the Hypotheses From a theoretical point of view, it is reasonable to assume that the adoption on the part of the company of QM practices contributes to an improvement in the performance, above all in those of an operational nature. The purpose of this type of practices is error prevention. This work on prevention will result in fewer errors, which will immediately lead to a reduction in the number of defective products (the quality as conformance to specifications improves). If conformance to specifications is achieved with no great difficulty, then, undoubtedly, the processes will be more efficient as the number of stoppages to adjust the process will decrease and resources, both material and human will be saved. Moreover, the speed of the process will also increase, which, in 11
  11. 11. 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 studies leads us to contrast the following hypothesis, stated in a generic way : Hypothesis: The plants which have undertaken a greater implementation of QM practices have achieved a greater improvement in their manufacturing performance than those with a lower level of implementation. This hypothesis leads to a set of hypotheses to the same effect, as many as the number 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. The concept of manufacturing industry is clearly defined in the National Classification of Economic Activity (NACE), which includes all the manufacturing industries (from code 15 to code 37) with the exception of oil refining (code 23). The sector of production and distribution of electricity, gas and water (codes 40 and 41), as well as the mining industries (from 10 to 14) are also excluded. Fixing the unit of analysis was an important matter to settle. Two possibilities were initially open to us: to choose the company or plant as the organisation to be considered under study. We opted for the latter. In the industrial sector, the plant constitutes the business unit which is of strategic importance for the implementation of the practices which make up our study. These practices are adopted in the plant, and therefore, 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 reliable when taken from the plant; since the knowledge of these issues is greater even if only because 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 limit and 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 was formed by 6013 units, a thousand units being the aim of the sample, these were stratified according to sector and size. In order to carry out the investigation a questionnaire was made up and after the corresponding 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 than three-quarters of them) were filled in by either the plant manager of the production manager. The questionnaire covered different issues, all linked to production. It was meant to be filled in by a person with a broad understanding of the organisational aspects 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 by any of the plant managers with a knowledge of the areas under study. After making 3246 telephone calls to make the necessary appointments, 965 valid interviews were undertaken. This number represents 16,04 percent of the total 12
  12. 12. population under study and constitutes the initial sample about which we have information. Measurement of the Variables The Creation of the Indexes We 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 of QM into operation. The following indexes were used, associated with the practices of design and development of new products (DESPROD), the production process (PROC), relations with the suppliers (SUP), relations with the customers (CUST) and human resource management (HUMRES). Each index incorporates a series of items, as can be seen in the appendix 2 [2]. The choice of these items was based on existing literature as well as on the experience of professionals, experts in the implementation of Quality Management programmes. The different items which make up the five key indexes are measured on different scales. The variables were standardised and converted into z scores before combining them additively to form the indexes and in this way unify the unit of measurement. In order to simplify the interpretation, a linear transformation was applied to the z scores, the totals of which were obtained for each of the five indexes. With the result that a 0 value for the indexes is given to the plant which has the lowest score of the 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 obtained the average of the z scores for the five indexes and this was then transformed onto a scale of 0 to 100. Fiability and validity of the indexes In 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 are redundant, each measuring the same thing from a different vantage point. Cause indicators assume their are multiple, objectively different manifestations of an underlying phenomenon which need not correlate. Bollen and Lennox (1991) argued that “we have no recommendations for the magnitude of correlations for causal indicators, 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, all indicators that form it should be included”. Nonetheless, we provide Cronbach alpha values for the indexes in the table. 13
  13. 13. Table 2. Descriptive statistics, Cronbach´s α and correlations for QM indexes INDEX Mean Std. Dev. α 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 way a specific concept, which cannot be measured in a direct way. There is no single way of determining the validity of a measuring instrument. In our case, we have fixed three types of validity (Nunnally, 1978; Flynn et al., 1990): content validity, construct validity and criterion-related validity. The content validity refers to whether the set of items which make up the scale is suitable for the evaluation of the construction (De Vellis, 1991). The items used to create the indexes have been taken from the literature on QM and from interviews with experts in the field. The same rigorous approach has been taken with regard to the creation of these instruments. The validity of the content of this instrument is thus demonstrated. A way of demonstrating the construct validity is to carry out a principal component analysis for each of the indexes. If the information on the predetermined items for each of the indexes can be summarised in a single factor, we can consider that all the items are measuring the same concept and we can confirm the validity of the construction. A principal component analysis[4] with a varimax rotation for each of them, has been carried out in order to confirm their one-dimensional nature. The results of 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 considered significant. The principal component analysis carried out for each concept indicates that, in every case, only one factor with a associated eigenvalue superior to one can be seen. This corroborates the one-dimensional nature of the established concepts. It can also be seen that the factor weights of the items are high in each case (>0,4). 14
  14. 14. 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 the criterion variables. To this end we have used QSIS as a criterion variable, this is a dichotomous variable which indicates whether the company has adopted a quality assurance system or not. Statistically significant differences in the value of the QM indexes are to be expected. The validity of the QM measurement is seen between the companies which have a quality assurance system and those which do not; the values are expected to be higher for the former. In order to contrast this we have used an analysis of variance[5], the results of which are shown in table 4, these are very significant 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** 15
  15. 15. The Measurements of performance The 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 of the results related to cost. The indicators of improvement in product quality are; the improvement in the percentage of returned products over sales (RETURN), the improvement in the percentage of defective finished products (QFP), and the improvement in the percentage of defective products in process (QPP). Finally, in order to determine the level of improvement in the results related to time, the percentage of delivery dates complied with (PUNCT), and the time taken from the moment the material is received to the moment the product is delivered to the customer (SPEED) are used. The Empirical Model In order to contrast the hypotheses originating from the generic hypothesis previously established, six similar models are used (as many as there are variables of manufacturing results), their only difference being, therefore, the dependent variable. The basis of the established empirical model is the reasoning put forward by Hansen and Wernerfelt (1989). These authors establish a model to determine company’s performance (of an economic-financial nature in this case) in which they integrate the economic and organisational models. On the one hand, variables related to the sector, competition and the company’s resources (the economic model) are included, and on the other, variables related to human resource management, the organisational climate etc. (the organisational model). The model we have established here is far from being a replica of the one put forward by these authors, however it shares with them the fact of including variables which refer to the two models already mentioned and its character is therefore, one of integration. The independent variables included in the models are, in the first place , the natural 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 of product), which define whether the market for the product is made up of other companies or consumers, are included, as well as COMPET (the increase in competition in the last three years). In part it could be said that the plants which have more demanding customers (the industrial products market), have had to make a greater effort to improve than those which produce consumer goods. It is also to be expected that those plants which are faced with increasing competition have to make a greater effort to improve their manufacturing results. This improvement is expected to be more obvious. The level of automation[6] (AUTOMAT) being the variable most related to the resources of the companies is included in the model. This is expected to have an important impact on the behaviour of the manufacturing results (Mac Duffie, 1995). The efficiency of the production system, together with the percentage of defective products and the flexibility of the production system are, in general, better in the plants with a greater degree of automation. These should presumably present a more positive development in their manufacturing performance. 16
  16. 16. The organisational climate (CLIMA) variable is included as a variable of an organisational nature. There is a series of studies which analyse the impact of different aspects of the organisational climate on performance (Lawler et al., 1974; Pritchard and Karasick, 1973; Capon et al., 1992), the relation they discover is a positive one. In this study, is measured as the average of three items which define the relations between management and employees, the degree of employee identification with the company and the degree of employee satisfaction. The explanatory variable, which constitutes the centre of our interest, that is to say, the QM index, can be considered to have the same character. The purpose of this study is precisely to determine whether the effort companies make in implementing this type of practices is reflected in the results. The dependent variable in each model is a dichotomous variable, which has the value of “0” for those plants whose manufacturing results have not improved in the last three years, the variable takes the value “1” for those plants whose results have improved[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 dependent variable has a dichotomous nature and many of the independent variables show an absence of normality[]. Finally, we have contrasted the model by replacing the QM index and introducing in its place the five dimensions of the index as explanatory variables. The factor scores of the six initial measurements of results are used as the dependent variable RESUL in the regression analysis. This will give us more information as to which of the five dimensions under study are associated with the improvement in operational performance RESULTS Table 5 shows the average, the standard deviation and the correlations of all the variables included in the established models. We can observe that, between 59%, in the case of EFIC and 73% in the case of SPEED, of the plants in the sample show an improvement in their results in relation to those obtained three years earlier. Moreover, all the results variables show an important correlation between each other, which suggests that the improvements in results are common to all of them. We have also observed the important correlation of QM with all the results variables. AUTOMAT is also seen to be highly correlated. 17
  17. 17. 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 18
  18. 18. 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 19
  19. 19. Table 7. Results of the logit analysis for QFP and QPP QFP QPP Model 1 Model 2 Model 1 Model 2 b s.d. b s.d. b s.d. b s.d. 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 20
  20. 20. Table 8. Results of the logit analysis for PUNCT and SPEED PUNCT SPEED Model 1 Model 2 Model 1 Model 2 b s.d. b s.d. b s.d. b s.d. 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 21
  21. 21. Tables 6, 7 and 8 show the results obtained for each of the six established models. For each dependent variable we established two models, the central explanatory variable (QM) was not included in the first model but was in the second. Our aim was to show the impact of this variable on the explanatory capacity of the model. Finally, table 9 presents the results of the multiple regression analysis which shows the impact of each of the dimensions on the global indicator of the improvement in manufacturing performance. The results we have obtained fully confirm each of the hypotheses, as can be seen in tables 6,7 and 8. It can be stated that a statistically significant relationship exists between 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 all the cases). In other words, the plants with a greater degree of implementation of QM practices, have a greater probability of improving their manufacturing performance. These results coincide with the majority of the studies which have dealt with this subject in one way or another (see table 1). The relationship between QM and performance is significant in all the established models, but the level of significance is higher for the relationship with the results based on time( in all cases below five per thousand). This is somewhat surprising as, in the beginning, one assumes that the association with the quality results and even the cost results, is going to be the most significant. The R2 values, although discreet, are considerably better than those obtained in similar 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 et al. (1996) and Black and Porter (1996). In all cases, the value of χ2 indicates that the value of the coefficients is significantly different to zero in its entirety. The introduction of the QM variable ostensibly improves the explanatory capacity of the model (R2), in all cases[]. If the rest of the variables included in the model are analysed, few significant relationships with the improvement in results are seen, except in the case of the AUTOMAT variable (level of automation). For the majority of the initial models (except for EFIC), a statistically significant association with the improvement of the results can be seen, as well as a positive coefficient sign. This means that, those businesses with a greater degree of automation have an increased probability of improving their manufacturing performance. Nevertheless, in all cases, when the QM variable is introduced into the model, this is reduced or even disappears, which is a consequence of the important correlation between the two variables. There are some significant associations for the other variables[]. Thus, we find a positive relationship between the organisational climate and the improvement in the indicator of productive efficiency, which was to be expected. On the other hand, no significant relationship with the other variables has been seen. The increase in competition over the last few years has served to improve two of the quality performance under study (QFP and QPP). Nevertheless, we have found no relationship for the other performance variables The results of the regression analysis of the five QM elements on performance
  22. 22. provide some interesting insights (table 9). Two of the variables, Desing and Human Resources Management, proved to be strongly significant and positively related to performance. 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 be ignored but rather to note that these weaker dimensions of QM did not powerfully distinguish 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 “human resource management practices” is consistent with the findings of Ahire and Golhar (1996) and Samson and Terziovski (1999). These result suggest that the key to performance lies not in Process and Suppliers and Customers tools and techniques but in the different aspects of Human Resource Management like involvement, empowerment, training and information sharing. The R2 value reported by our model is lower than the reported by Samson and Terziovski (1999) in their study. Nonetheless this 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 of models. CONCLUSIONS The purpose of this study was to analyse the relationship between the QM practices and the results. The information we have obtained is taken from almost one thousand manufacturing plants employing more than fifty workers in Spain. Firstly, we have undertaken a revision of the studies which analyse the relationship between the
  23. 23. adoption of specific QM practices and the results. These studies, which have only recently appeared, are now becoming more common. They are of a heterogeneous nature, both in terms of the QM practices in the field of application and in terms of the measurements of results used. Six hypotheses are contrasted (one for each performance measurement) and a logit model is estimated for each one. In all cases, the global variable which represents the total implementation of quality practices (QM) is significant in relation to the improvement in performance. The introduction of this variable into the model ostensibly improves their explanatory capacity. In the same way, the relationship is more significant with the based on time performance than with those related to quality or cost. Finally, we established a multiple regression analysis in order to determine which of the components of the QM index influence the improvement results. We have found that the practices related to the design and development of the product as well as the human resource management practices are the ones which have a statistically significant impact. One limitation is the study´s cross-sectional research design. Although the data showed a significant QM-performance correlation, they did not strictly prove that QM caused performance to increase, but only that an association existed. High performance improvement may give rise to QM programs, or QM and performance may both be caused by some third factor not measured in this study (although, based on previous research, the most powerful known explanatory factors were included in the study). The most probable explanation is that a relation between QM and performace exist but a longitudinal study would be required to support a causal inference strictly. This study contains findings useful to both practising managers and other researchers. The message for the managers is that they must insist on the implementation of quality programmes. Efforts must be made in a dual sense. On the one hand, they must develop good projects to design and develop products in order to avoid problems later on at the production stage. In the same way, they must encourage human resource practices such as, empowerment, involvement, training and information sharing, 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 to the 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 help businesses to further establish improvement processes, resulting from the application of the 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 other places. Secondly, it studies the relationship with the different types of measurements of operational performance in greater depth. Moreover, the consideration of other explanatory variables apart from those related to QM, means that the results obtained have a greater validity. Notes [1] Cited by Powell (1995)
  24. 24. [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 the dichotomous variables (PRINCALS in SPSS 7.5). [1] Bollen (1989) gives some examples for cause indicators. Time spent with family and time spent with friends are cause indicators of the latent variable of time in social interaction and race and sex are cause indicators of exposure to discrimination. In the first example, time spent with family and friends need not correlate (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 cause indicators 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 that the data is sufficiently correlated (Hair et al., 1995). The two tests we undertook, Bartlett’s spherical test and 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: the independence of sample observations, the normality of the independent variables, and the equality of variances between the groups (Hair et al., 1995). The first condition is satisfied. The non-fulfilment of the normality 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), the non-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 the value of the four items in which the following are indicated: the extent of implementation of robots or PLC’s, automatic systems for storing and handling material, CIM and information webs for the treatment of production data. The value of the Cronbach a coefficient is 0,78 and the principal component analysis we 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 of the discriminating analysis is the true estimator of maximum likelihood and therefore, is asymptotically more efficient than the true estimator of maximum likelihood of the logit. However, if the independent variables are not normal, the estimator of the discriminating analysis is not consistent, whereas the logit estimator 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, the relationship 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 in which 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 Quality Improvement 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 and Organization 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 Small Firms”. Journal of Small Business Management, Abril , pp.1-13.
  25. 25. American Quality Foundation and Ernst & Young (1991), International Quality Study: The Definitive Study of the Best International Quality Management Practices, Ernst & Young, Cleveland. Azzone, G.; Masella, C and Bertelé, U. (1991): “Design of Performance measures 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 of TQM”, Decision Sciences, Vol. 27, nº 1, pp. 1-21. Blackburn, J.D. (1991), Time -Based Competition: The Next Battle-Ground in American 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 of Product 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 TQM practices, Plant Performance and Customer Satisfaction”, Journal of Operations Management, 17, pp. 59-75. Corbett, C. and Van Wassenhove, L. (1993): “Trade-offs? Whay Trade-offs?. Competence and Competitiveness in Manufacturing Strategy”. California Management Review, Vol. 35, nº 4, pp. 107-122. De Vellis, R.F. (1991), Scale development: Theory and Applications, Sage Publications, Newbury Park, California. Deming, W.E. (1982), Out of the crisis. Quality, Productivity and Competitive Position, University Press, Cambridge. Ebrahimpour, M. and Johnson, J.L. (1992), “Quality, Vendor Evaluation and Organizational Performance: A Comparison of U.S. and Japanese Firms”, Journal of Business Research, 25, pp. 129-142. Filippini, R.; Forza, C. and Vinelli, A. (1998), “Trade-off and Compatibility Between Performance: Definitions and Empirical Evidence”, International Journal of Production 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 of Operations Management, Vol. 9, nº 2, pp. 250-284. Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1994), “A Framework for Quality Management Research and an Associated Measurement Instrument”, Journal of Operations Management, 11, pp. 339-366. Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1995a), “Relationship Between JIT 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 of Quality Management Practices on Performance and Competitive Advantage”, Decision Sciences, Vol. 26, nº 5, pp. 659-691.
  26. 26. Flynn, B.B.; Schroeder, R.G. and Sakakibara, S. (1995c), “Determinants of Quality Performance in High and Low Quality Plants”, Quality Management Journal, Winter, pp. 8-25. Forker, L.B. (1997), “Factors Affecting Supplier Quality Performance”, Journal of 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), Multivariate data 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”, Strategic Management Journal, Vol. 10, pp. 399-411. Hendricks, K.B. and Singhal, V.R. (1997), “Does Implementing an Effective TQM Program Actually Improve Operating Performance? Empirical Evidence from Firms 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 the Choice 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 that Drive Performance”, Harvard Business Review, Enero - Febrero, pp. 71-79. Lawler III, E.E. and Hall, D.T. (1974), “Organizational Climate: Relationship to Organizational 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 High Performance Organizations: Practices and Results of Employee Involvement and Total Quality Management in Fortune 1000 Companies, Jossey-Bass Publishers, San Francisco. Leal, A. (1997), “Total Quality Management in Spanish Companies: An Cultural and 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 Manufacturing Performance: Organizational Logic and Flexible Production Systems in the World Auto Industry”, Industrial and Labor Relations Review, Vol. 48, nº 2, pp. 197-221. Maddala, G.S. (1983), Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press, Nueva York. Madu, C.N.; Kuei, C.H. and Jacob, R.A. (1996), “An Empirical Assessment of the Influence of Quality Dimensions on Organizational Performance”, International Journal of Production Research, Vol. 34, nº 7, pp. 1943-1962. Mann, R. and Kehoe, D. (1994), “An Evaluation of the Effects of Quality Improvements Activities in Business Performance”, International Journal of Quality and Reliability Management, Vol. 11, nº 4, pp. 29-44.
  27. 27. Martínez, A.M. (1996), Quality Management in Operations. Theoric Review and the Analysis of his Implantation and Performance in Spain. Doctoral thesis unpublished. University of Murcia. Neely, A.; Gregory, M. and Platts, K. (1995), “Performance Measurement System Design. A Literature Review and Research Agenda”, International Journal of Operations & 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”, Organizational Behaviour & Human Performance, Vol. 9, nº 1, pp. 126-146. Samson, D. and Terziovski, M. (1999), “The Relations Between Total Quality Management Practices and Operational Performance”, Journal of Operations Management, 17, pp. 393-409. Saraph, J.V.; Benson, P.G. and Schroeder, R.G. (1989), “An Instrument for Measuring the Critical Factors of Quality Management”, Decision Sciences, Vol. 20, nº 4, pp. 810-829. Stalk, G. (1988),. “Time: The Next Source of Competitive Advantage”, Harvard Business Review, July-August, pp. 41-51. Terziovski, M.; Samson, D. and Dow, D. (1997), “The Business Value of Quality Management Systems Certification. Evidence from Australia and New Zealand”, Journal of Operations Management, 15, pp. 1-18. U.S. General Accounting Office (1991), Management Practices: U.S. Companies Improve Performance Through Quality Efforts, U.S. General Accounting Office, Gaithersburg, MD. Venkatraman, N. and Ramanujan, V. (1986), “Measurement of Business Performance in Strategy Research: A Comparison of Approaches”, Academy of Management Review, Vol. 11, nº 4, pp. 801-814. Venkatraman, N. y Ramanujan, V. (1987), “Measurement of Business Economic Performance: 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), Managing with Total Quality Management. Theory and Practice, MacMillan Business, Londres.
  28. 28. APPENDIX 1 Distribution of the sample by sector and size SECTOR 50-199 200-499 500 or Total more Food, drinks and tobacco 100 31 15 146 Textiles, clothing, leather and footwear 97 15 6 118 Wood and cork 25 2 0 27 Paper, publishing and graphic arts 52 14 5 71 Chemical industry 50 12 8 70 Rubber and plastics 46 8 4 58 Non-metallic mineral products 54 8 4 66 Primary metal industries and fabricated 91 13 14 118 metal products Machinery and mechanical equipment 52 12 8 72 Electrical material and equipment, 38 19 13 70 electronics and optics Transport material 40 23 27 80 Miscellaneous manufacturing industries 49 8 2 59 Total 694 160 96 965 Percentage of population in the sample by sector and size SECTOR 50-199 200-499 500 or Total more Food, drinks and tobacco 12,53 16,67 28,85 14,09 Textiles, clothing, leather and footwear 13,94 19,48 54,55 15,05 Wood and cork 17,36 16,67 17,31 Paper, publishing and graphic arts 12,65 20,29 55,56 14,52 Chemical industry 14,75 10,17 22,86 14,23 Rubber and plastics 14,56 24,24 28,57 15,98 Non-metallic mineral products 13,27 11,76 40 13,61 Primary metal industries and fabricated 15,88 13,13 48,28 16,83 metal products Machinery and mechanical equipment 13,83 19,05 42,11 15,72 Electrical material and equipment, 13,67 19,59 39,39 17,16 electronics and optics Transport material 17,70 26,44 48,21 24,39 Miscellaneous manufacturing industries 20,33 32,26 21,69 Total 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)
  29. 29. 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 supplied parts. (QAGSUP) Customers (CUST) 1. Questionnaires are carried out to determine the level of satisfaction of our products (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 supplied parts (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 of personnel selection (SELEC) 2.4. Training related to work group techniques and problem solving is given (TRAINQ) 3. Empowerment(EMPOW) 3.1. They prepare the machines they use (PREP)
  30. 30. 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)