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
Campus de Arrosadía, 31006 Pamplona, Spain
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.
Mr Javier Merino Díaz de Cerio
Departamento de Gestión de Empresas
Campus de Arrosadía
I thank Fundación BBVA for the financing provided
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
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.
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 ). 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,
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
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
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.
Flynn et al. (1994) 42 American Seven QM dimensions Operational performance Canonical correlation Positive
manufacturing plants (quality performance)
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.
Powell (1995) 54 American Twelve QM dimensions Financial performance Correlation analysis Positive (intangibles
manufacturing and specially)
Flynn et al. (1995a) 42 American Eight QM dimensions Operational performance Multiple regression Positive
manufacturing plants analysis
706 valid responses
Flynn et al. (1995b) 42 American Eight QM dimensions Operational performance Path analysis Positive
706 valid responses
Flynn et al. (1995c) 42 American Four QM dimensions Operational performance Discriminant analysis Non lineal
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
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
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
Madu et al. (1996) 165 American Three QM indexes Operational performance Correlation analysis Positive
manufacturing and service
Leal (1997) 113 Spanish Ten QM dimensions Finance performance Correlation analysis No with one measure, yes
manufacturing and service with others.
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
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
Wilkinson et al. Summary of six studies in Several Several Descriptive statistics Various results, although
(1998) UK there are more positive
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
THE CONCEPTUAL FRAMEWORK AND THE ESTABLISHMENT OF
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.
Figure 1. Conceptual Framework for Quality Management Practices.
Adapted from Flynn et al. (1995c)
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).
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
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
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
population under study and constitutes the initial sample about which we have
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 . The choice of these items was based on existing literature as
well as on the experience of professionals, experts in the implementation of Quality
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”, 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.
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
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 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).
Table 3. Results of the Principal Component Analysis for each index
QM INDEX EINGENVALUE ITEMS FACTOR LOADINGS
DESPROD 1,643 CUSDES 0,478
PROC 2,790 SPC 0,694
SUP 1,814 QSUP 0,470
CUST 2.047 SURCUS 0,400
HUMRES 1,86 TRAIN 0,532
QM 2,53 DESPROD 0,598
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, 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**
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
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
The level of automation (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.
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. 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
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.
Table 6. Results of the logit analysis for EFIC and RETURN
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
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
Table 7. Results of the logit analysis for QFP and 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
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
Table 8. Results of the logit analysis for PUNCT and 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
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 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
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
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
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
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
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
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
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.
 Cited by Powell (1995)
 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).
 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.
 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.
 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.
 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)
 The rank of the initial variables (in the questionnaire) is one to five.
 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.
 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).
 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
 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.
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Distribution of the sample by sector and size
SECTOR 50-199 200-499 500 or Total
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
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
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
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
Design and new products development (DESPROD)
1. The extent to which the customers’ requirements are taken into account
2. The extent to which the suppliers’ suggestions are taken into account.
3. The extent to which technical difficulties in production are taken into account
4. The degree of implementation of the value analysis. (VALAN)
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.
5. The plant has a system of preventive maintenance. (PREVENT)
6. Instruments for control and quality improvement are used (INSTRU)
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
1. Questionnaires are carried out to determine the level of satisfaction of our
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
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
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)