Development and Validation of a Perceived Business Quality Measurement Instrument

October 2002
Volume 9 • Number 4


Development and Validation of a Perceived Business Quality Measurement Instrument

by Juan Carlos Bou Llusar and Cesar Camison Zornoza, Universitat Jaume I

The objective of this article is to propose a methodology for the construction and validation of a measurement instrument for perceived business quality (PBQ). To this end, the authors first suggest the development of the PBQ concept. Second, based on the methods of service quality measurement, the authors develop and validate a measurement instrument through the use of the structural equations models methodology.

Final results point to the existence of three dimensions on PBQ: perceived product quality, perceived service quality, and business orientation toward quality. These three dimensions, although correlated, appear as differentiated traits of PBQ. Furthermore, with regard to the measurement instrument, several different tests show the existence of a high degree of reliability and construct validity, which justifies its suitability as a measurement of PBQ. It will allow the practitioner to evaluate the business quality level and use it to test the influence of business quality on customer behavior (consequents) and the determinants of business quality (antecedents).

Key words: construct validity, perceived business quality, reliability, structural equations models


There has been a major increase in the number of studies into service quality over the last decade. The introduction of subjective quality measurement instruments such as those based on clients’ perceptions (for example, SERVQUAL, SERVPERF) has led to the creation of an important line of research into the concept of quality service. Nevertheless, the application of these subjective quality measurements (as perceived quality) only to service activities leads to unnecessary limitations. It presupposes the omission of a third element: the diverse effects that quality exerts on business results.

Many authors have identified two generic ways in which quality influences business results (Garvin 1984; Juran and Gryna 1988; Reed, Lemak, and Montgomery 1996; Hardie 1998). One is called internal effects, which is related to the influence of quality on cost reduction and productivity increase (for example, W. Edwards Deming’s chain reaction). The second is external or market effects, which is related to the influence of quality on the capability of businesses to increase income and is based on the influence of quality on client behavior (Anderson and Fornell 1994, 241-268). Given their nature, these types of effects must be evaluated through measurement based on information from external agents, such as the clients (Bolton and Drew 1994, 173-200). In the case of quality, the evaluation of external effects should be based, therefore, on the measurement of the perceived quality, which is understood as an external measurement of quality from the client’s point of view (Steenkamp 1990).

The consideration of these two effects bears the following argumentation: It is the type of effect (internal or external) that determines the kind of measurement to be used, and not the nature of the business activity (service or manufacture). Therefore, the association between service quality and the measurement of perceived quality is unnecessarily restrictive, since effects through the market are not limited to service activities and, thus, can be applied to all kinds of businesses.

This is the scope of this article. More specifically, the authors are trying to develop a measurement instrument of a subjective quality, based on clients’ perceptions. This instrument will be applicable to the study of the external effects of quality in any kind of business. For this purpose, the authors will devise a suitable concept of perceived quality to be applied to the business, and make it operative through the construction of a multi-item measurement scale. This measurement instrument will be validated through structural equations models (SEM) methodology.

To this end, this article is arranged in four parts. First, the most important literature on the concept of perceived quality is reviewed. Its main characteristics are defined and its adaptation to the present work put forward. Second, the applied methodology for the construction of the measurement instrument of perceived business quality (PBQ) is described. This is followed by the evaluation of the measurement instrument and the identification of its construct validity through the dimensionality analysis, and an examination of its reliability, and convergent and discriminant validity. Finally, the authors’ initial conclusions are set out, together with some recommendations for future research.


“Perceived quality” is usually defined in literature as an evaluative judgment of an attitudinal nature (Parasuraman, Zeithaml, and Berry 1988; Carman 1990; Cronin and Taylor 1992) that has the following characteristics:

  1. It is formulated by the client (Steenkamp 1990; Holbrook 1994, 21-71).
  2. It is an overall judgment of a global character, although shaped by the object’s characteristics and attributes (Olshavsky 1985, 3-29).
  3. It is relative, since it is determined by the interaction between the object and the subject who evaluates it (Steenkamp 1990).

In this sense, customer perceptions on the quality of a firm are not limited to the evaluation of the characteristics of the product or service, but include all elements that are susceptible to being perceived and evaluated by the client, such as price, image, reputation, and so on (Olson and Jacoby 1972; Olson 1977, 267-286; Grönroos 1982; Olshavsky 1985, 3-29; Parasuraman, Zeithaml, and Berry 1988; Bitner and Huber 1994).

Furthermore, the perceptual process of quality is explained by Steenkamp (1990) as a process structured in three stages:

  1. Acquisition and categorization of quality cues, which are defined as the stimulus related to object (business, product, service, and so on) features.
  2. Quality attribute beliefs formation, that is, the informational and/or inferential beliefs that the consumer forms about the functional and psychosocial benefits the object might offer. These quality attributes can be based on both the quality cues and the experience of the object during its use or enjoyment.
  3. Integration of the quality attribute beliefs throughout the formation of an overall assessment of the quality of the object. In the case of an organization, this judgment represents the PBQ of the client.

This process of perceiving, categorizing, evaluating, and forming the judgment or attitude requires increasingly greater levels of abstraction (Zeithaml 1988). This enables the step to be taken from quality cues to the PBQ. Thus, this global valuation considers all the features perceived by the client, and it is conditioned by a set of personal (for example, knowledge of the product, previous experience, perceived risk), situational (for example, type of use, availability of time, moment of the evaluation) and comparative factors (for example, available alternatives) (Steenkamp 1990; Holbrook 1994).

According to this definition of perceived quality and the quality perception process, its attitudinal nature justifies its possible use in all kinds of businesses, since clients can form their own judgments on the quality of any “attitude object.” Nevertheless, several authors argue that, given the characteristics of services (intangibility, heterogeneity, and inseparability between production and consumption), objective measurements based on tangible attributes or characteristics are not applicable to services, and suggest the need to employ different evaluation processes from those used on tangible objects (Zeithaml 1981, 39-47; Parasuraman, Zeithaml, and Berry 1985). In the authors’ opinion, this differentiation does not prevent perceived quality and its operationalization being used to evaluate objects of tangible nature, although it may have to be adapted to the attitude object.

In this case, the main question is not whether the perceived quality approach is valid, but what types of adaptations must be implemented so they can be applied to the evaluation of the business as a whole. These adaptations refer not only to the variety and extent of the construct domain elements, but also to the type of attributes to be considered and their relative importance (Rust and Oliver 1994, 1-19). These aspects are discussed next.

The Dimensions of PBQ

The processes for evaluating PBQ use a wide set of indicators or variables, which combine to form a global judgment. One way of grouping these variables homogeneously is by identifying the dimensions of the construct to be analyzed. The components or dimensions are the elements of comparison that individuals use to evaluate the different objects and allow the identification of the most relevant characteristics for the definition of the concept (Lazarsfeld 1958).

Many authors have identified a set of dimensions for evaluating quality, which are defined from different perspectives. Thus, Garvin (1987) and Brucks and Zeithaml (1987) identify a set of dimensions for perceived product quality. Parasuraman, Zeithaml, and Berry (1985; 1988) and Grönroos (1982) identify dimensions for perceived service quality. Other studies, such as those by Lau and Anderson (1998), the Malcolm Baldrige National Quality Award, and the European Foundation of Quality Management (EFQM) Excellence Model evaluate the dimensions of company management quality.

These studies, however, represent a partial approach to the problem of evaluating PBQ, since so many elements concerning the service, the product, and the business as a whole should be considered. Thus, it is necessary to widen the set of aspects to be considered in the definition of PBQ.

Rust and Oliver (1994, 1-19) synthesize these aspects in three dimensions or basic components of PBQ:

  1. Perceived product quality, which gathers the attributes of the products of the firm
  2. Perceived service quality, which gathers the characteristics of the service supplied with the product (lead times, availability of the product, and so on)
  3. Firm orientation toward quality, either external orientation (image, reputation, price) or internal orientation (quality organization, creation of customer information systems, employee orientation to quality)

All elements related to quality management, internal and external to the business, that may have an effect on the client’s perception of quality were included in this last dimension (Rust and Oliver 1994).


To construct the measurement instrument, the authors applied the methodology to develop measurement scales in social sciences (Churchill 1979; DeVellis 1991). In general, the procedure that allows one to move from the concept to its measurement requires a four-stage process: literary definition of the concept, specification of dimensions, selection of observed indicators, and synthesis of indicators or elaboration of indexes (Lazarsfeld 1958). A summary of the steps followed in the construction of the measurement instrument for this project is shown in Figure 1.

Stage 1 All of the attributes that may influence client perception of quality were identified from within the domain specification. This was done by reviewing the most relevant literature on quality (Churchill 1979), which enabled the authors to identify a wide set of elements grouped within the three components or basic dimensions of PBQ explained previously: perceived product quality, perceived service quality, and orientation toward quality.

Stage 2 The items generation was based on three different information sources: 1) revision of literature on quality; 2) revision of specialized journals on the ceramic industry (the subject of the research); and 3) a previous exploratory study that involved 15 in-depth interviews with clients and managers of ceramic companies. The service-related items were based on the SERVQUAL scale (Parasuraman, Zeithaml, and Berry 1988), although, following the recommendation of the authors, they were adapted to fit the characteristics of the ceramic companies. The product-related items were grouped using the product quality dimensions proposed by Garvin (1987). The items related to the orientation toward quality were based on the most important quality management models such as the Malcolm Baldrige National Quality Award criteria (George 1992), EFQM Award criteria, and the work of the most important TQM advocates (such as Crosby 1979; Deming 1986; Garvin 1987; Juran 1992). The result of this process was a list of 158 items that had to be reduced because of its large size.

Stage 3 A two-phase procedure was established for the reduction of the scale. First, using the Delphi methodology, a group of 16 experts on subjects related to quality in the ceramic industry was selected. This group was made up of two quality academics (university professors), three professionals belonging to various institutions and associations from the ceramic sector, four directors of ceramic companies, and seven directors of ceramic product distribution companies. They were asked to express their degree of agreement/disagreement (on a Likert scale from 1 to 5) on the inclusion of items to make up the scale. The result of this procedure, after two rounds of consultation, allowed the authors to reduce the scale to 27 items.

Second, a pilot sample of 50 questionnaires was carried out, divided into two equal sub-samples in order to check the results of the scale when applied to the target population (for example, degree of difficulty, extension, total answer rate, and so on), and to determine the most suitable operationalization method: difference scores or direct scores (see Peter, Churchill, and Brown 1993; Teas 1993). The trial resulted in a reduction of the scale to 22 items (five items were eliminated since interviewed clients claimed they were too ambiguous or did not offer sufficient information for their evaluation), shown in Appendix A, and a formulation by means of difference scores. This method of operationalization provided the authors with more information and improved understanding for the respondents.

Stage 4 The collection of data was achieved, as shown in Table 1. Distributors of ceramic products (in contrast to other types of client, such as the final consumer) were selected because of their in-depth knowledge of the company, which was necessary for the evaluation of business perceived quality. In selecting clients, a minimum of one year as a client of the company being studied was required, as a guarantee of their knowledge about it. Respondents were selected through a random sampling from the total number of Spanish ceramic distributors. Each selected distributor assessed three ceramic companies. To reduce variability produced by the specific nature of each of the companies assessed (and to differentiate them from the variation in customer quality perception), they were chosen from a group of 15 similar companies from the same strategic group.

In the final questionnaire, both ideal expectations of quality (Teas 1993) and perceptions of the company under study were present, thus two operation methods were used: the SERVPERF model (Cronin and Taylor 1992) and the model of Evaluated Performance (EP) (Teas 1993). EP was employed as it is already established as a general model, and, therefore, SERVQUAL was simply a specific case (Teas 1993; 1994; Parasuraman, Zeithaml, and Berry 1994). The Likert scale was applied to all questions with an answer range of 1 to 7, where 7 means “completely agree” and 1 means “completely disagree.”


To determine reliability and validity, the authors chose the structural equations model (SEM) methodology. SEM shows several advantages over traditional techniques (based on the analysis of correlations between the measurement of study and other measurements) when the model to be evaluated is not observable and there are measurement errors (Bagozzi and Phillips 1982; Bollen 1989).

To evaluate validity and reliability, the following tests were performed: 1) item analysis; 2) evaluation of the proposed measurement model adjustment to the data collected by the scale application, which identifies its dimensionality; 3) assessment of reliability and validity of estimated parameters; and 4) assessment of convergent and discriminant validity.

Items Analysis

Table 2 shows the matrix of correlations between items, the most significant statistics used for this analysis (mean, variance, and correlations item-total scale), and the “alpha” coefficient of both the total scale and the scale where the item was not included (“alpha without item”). In this table, it is observed that inter-item correlations are generally high and all are positive. This means that it is not necessary to reverse the score of any item. The correlations item-total scale shows high values in general (all are above 0.5 except for variable P6). Both the alpha coefficient (0.953), and the “alpha” without item coefficients also show high values.

Factorial Validity

The existence of a high alpha coefficient does not guarantee that item loadings are caused by the influence of only one latent variable (DeVellis 1991). Such a coefficient does not indicate what the factorial structure is and, therefore, what the number of latent variables is that influences the items. In fact, the inter-item average correlation can be high (and consequently, so can the alpha coefficient), even when a multifactorial structure exists.

The model from which the construction of the measurement instrument was deduced was applied to check the dimensionality of the measurement instrument. This was done using confirmatory factor analysis (CFA). This procedure enabled the authors to test the measurement model, that is, the number of dimensions (or factors) in the measurement and the relationship between dimensions and items. A main feature of CFA is that the measurement model is set by the analyst in advance (Bollen 1989). In this case, the measurement model implies the following hypotheses:

  1. PBQ is made up of three dimensions or factors (perceived product quality, perceived service quality, and business orientation toward quality).
  2. Each observable variable (item) must have a unique positive factorial loading on the objective factor and null factorial loadings for the other two factors.
  3. The three factors (dimensions) that form PBQ are correlated.
  4. The measurement errors of the items are not correlated.

A graphic representation of the model is shown in Figure 2.

Because of the discreet character of the variables, the model estimate was shaped from the polichorical correlations matrix, which was obtained, at the same time, from the raw data through the PRELIS processor. As far as the estimation method employed was concerned, the weighted least square (WLS) was used. This method has the advantage that under minimum requirements of observable variables distribution (the existence of multinormality is not necessary), it offers consistent estimators and an adequate chi-square statistic (Bollen 1989).

Results are shown in Table 3, where one can observe that all main parameters ( and ) are positive and statistically significant, that there are no correlations greater than 1, that all matrixes are positive-definite, and that standard errors are generally low (ranging between 0.023 and 0.093). These criteria indicate that there are no improper solutions and that the estimated parameters obtain feasible values.

Numerous criteria and indices can be found to assess the goodness of fit for structural equation models. Overall, the fit indices fall into the category of model fit, model comparison, or model parsimony. In this case, the goodness of fit was determined by the indices that are offered in the LISREL program, summarized in Table 4.

  • Three indices allow one to evaluate the goodness of fit model: , GFI, and AGFI. Goodness of fit determines the degree to which the structural equation model fits the sample data. First, the shows a high value but is not statistically significant. However, this statistical test has some limitations related to its dependence on a set of statistical assumptions (for example, the existence of normality or its sensitivity to sampling size), or the need to obtain models that, because of their simplicity, adjust well to data (in comparison to more complex models that will logically adjust better, but that contradict the parsimony principle) (Mueller 1996). These features suggest that a chi-square statistic should be used more as an indicator of how well the model reproduces the observed covariance matrix than as a formal statistical contrast (Long 1983). On the other hand, the GFI and AGFI indices show suitable values (0.950 and 0.939, respectively) close to the unit.
  • Alternately, Table 4 shows the Bentler-Bonett normed fit index (NFI) (Bentler and Bonett 1980), which is used for model comparison. These criteria typically compare a proposed model with a null model, that is, a model that proposes an equal number of factors and items. Its value (0.926), superior to 0.9, indicates that the model fits into the acceptable interval (Byrne 1989). This is a sign of a good marginal fit.
  • Finally, for the parsimony evaluation of the model, the index normed chi-square (NC) is proposed (Jöreskog 1969). Parsimony refers to the number of estimated coefficients required to achieve a specific level of fit. The final results in Table 4 indicate a value equal to 2.15, within the acceptable intervals (Mueller 1996).

Since this research is exploratory, a group of alternative models following the modification indices criteria proposed by the LISREL program were computed with the objective of improving the adjustment of the theoretical model. These indices allow one to determine the extent to which the adjustment can be improved if some imposed restrictions on the estimated model are lifted. The final adjusted model is shown in Figure 3. This model is made up of 17 indicators, as five indicators that present loadings on a second factor were eliminated.

The estimated parameters of the adjusted model are shown in Table 5. One can see that all main parameters are positive, statistically significant, and that there are no improper solutions (for example, negative variances, correlations smaller than the unit, high standard errors).

The fit indices of the adjusted model are shown in Table 4. Results indicate a better adjustment than the initial theoretical model on all indices used.

Finally, the authors proceed to analyze the goodness of fit of all individual parameters, with the purpose of identifying the ones that lead to poorer adjustments. For this analysis, the t-value and the sensitivity analysis of parameters were employed (Byrne 1989). With regard to t-values, which indicate the statistical significance of the parameters (as shown in Table 5), all of them are statistically significant and, therefore, they should not be eliminated from the model. In reference to the sensitivity analysis, this procedure enables one to assess the stability of main parameters ( and in Figure 3) with respect to the introduction of new parameters on the model. For this, six nested models were estimated (the freed parameters were ) until an adjusted model with a statistically significant was achieved (Table 4, post-hoc adjusted model). The correlations between parameters l and f estimated by the adjusted initial and final models were 0.999 and 0.931, respectively. This high correlation implies that modifications made to improve the adjustment of the model do not change the main parameter values of the model substantially. This means that it shows stability in its results and, therefore, the post-hoc modifications do not have to be introduced (Byrne 1989). Furthermore, the improvement on the NFI index between both models (initial and adjusted) was only 0.012, which confirms this stability.

In summary, the various indices analyzed show a good model fit. These results lead people to accept the adjusted measurement model (Figure 3), which evaluates PBQ by identifying the three differentiated but correlated dimensions: perceived product quality, perceived service quality, and business orientation toward quality.

Dimensionality Test

To check the dimensionality or PBQ, the adjusted model (Figure 3) was compared to an alternative model that presents PBQ as a unidimensional construct. The high correlation between factors (see Table 5) leads one to think that a unidimensional model could better fit the data than the proposed three-factor model.

Table 4, however, reflects the adjustment indices of both models. The unidimensional model shows a significantly poorer fit than the adjusted model. The chi-square value of the difference ( = 352.39) is statistically significant (p<0.05). These results confirm the hypothesis that the perceived quality is not a unidimensional construct.

Parameter Reliability and Validity

Reliability is concerned with the ability of a measure to be consistent, that is, it measures the amount of measurement error. The reliability of the items was determined by square multiple correlations (see Table 6) and the total coefficient of determination for observed variables (Bollen 1989). Results show that, in general, the observed variables are a good measure of the three factors. The square multiple correlations are greater than 0.5 in most items. On the other hand, the determination coefficient, which measures the reliability of all indicators together, has a value of 0.998 (close to 1), indicating that reliability of the overall model is high. An alternative test is the calculation of alpha coefficient. The value obtained (0.953) was similar to that obtained by the determination coefficient.

Convergent and Discriminant Validity

Following Campbell and Fiske (1959), validity is represented in the agreement between two methods to measure the same trait through maximally different methods. The two components of validity are worth highlighting: 1) convergent validity is represented in the agreement between two methods to measure the same concept through maximally different methods; 2) discriminant validity, as the extent to which a measurement of a concept differs from the measurement of other concepts. In order to evaluate the convergent and discriminant validity, a confirmatory multitrait-multimethod factor model (MTMM) was applied (Figure 4). This model suggests the existence of the three dimensions (or trait factors) of PBQ: product, service, and orientation; and three alternative measurement methods (method factors): SERVPERF, EP, and the evaluation through a single item of global valuation (GLOBAL).

In the estimation of the model, item scores were substituted by a single variable, obtained from the addition of these scores. This procedure, which is very common in the MTMM models (Schumacker and Lomax 1996), enables the analysis of the convergent and discriminant validity to be simplified. The suggested model is therefore composed of nine (three alternative measurements of the three dimensions that form the perceived business quality) observable variables (PROSP, PROEP, PROGL, SERSP, SEREP, SERGL, ORSP, OREP, ORGL), three trait factors or dimensions 1, 2 and 3 (perceived product quality, perceived service quality, and business orientation toward quality, respectively), and three measurement methods 4, 5 and 6 (SERVPERF, EP, and global valuation, respectively). The existence of a correlation between trait factors and between method factors is assumed, but not between trait and method factors. Finally, each observable variable has a measurement error term .

Final results of the estimate MTMM model are shown in Table 7. The magnitude of indicator loadings on correspondent dimensions implies the existence of a convergent validity. It can be observed that coefficients are statistically significant and reach high values (between 0.90 and 0.95). Moreover, the calculation of the partitioning variance among traits, methods, and errors (Bagozzi and Phillips 1982) indicates that the measurement variation caused by the three trait factors is 89.4 percent, 88.7 percent, and 80.2 percent, respectively.

Discriminant validity was determined by comparing the suggested model with an alternative MTMM model that proposes the existence of a perfect correlation between trait factors, and three freely correlated measurement methods. The results obtained from the initial model, the alternative model, and the difference between both are shown in Table 8. Here, one can observe statistically significant differences in 101.35 (p<0.001), which leads one to conclude that the three perceived quality dimensions, in spite of the high correlation between them, are different trait factors. These results justify the existence of a discriminant validity.


This article has focused on two main issues. The first is the definition and specification of the PBQ concept. The second is the development and validation of a measurement instrument for this construct. The application of this instrument enables one to go to the study of the quality external effects in any depth and helps managers gain a more thorough knowledge into the determining factors of business quality from the customer point of view.

Consequently, the most important implications to come out of this research refer to substantive and methodological issues. With regard to the former, it emphasizes the feasibility of applying the perceived quality concept to the evaluation of overall business quality, and the specification of its main dimensions. Methodological issues refer to the importance of research oriented toward measurement in quality management; the process and methodology used for the development and validation of the measurement instrument; and finally, the reliability and validity of the PBQ measurement instrument developed. Finally, the authors will conclude with some practical recommendations for practitioners.

The definition of PBQ as an attitudinal concept, besides agreeing with the conceptualization adopted by most previous literature (Parasuraman, Zeithaml, and Berry 1988; Zeithaml 1988; Bitner 1990; Bolton and Drew 1991; Carman 1990; Bitner and Hubbert 1994, 72-94), allows one to expand its scope of application to determine perceived quality for all types of businesses. By accomplishing this, one will be able to develop valid measurement instruments and investigate relations between quality and business competitiveness through external effects in any depth.

Concerning the characteristics of the PBQ construct, the final results allow one to identify it as a multidimensional construct made up of three components or main dimensions: PBQ, perceived product quality, and business orientation toward quality. The results obtained show that these are different, although correlated, dimensions. This means that customers differentiate between product quality, service quality, and business orientation toward quality. This differentiation has significant implications for quality improvements (antecedents), since these can be different according to whether they refer to improvement in product features, service, or business orientation toward quality.

With regard to the methodological issues, the approach of this article has been to construct validation research (Schwab 1980), which, in the quality management field, requires further development (Venkatraman and Grant 1986; Montgomery, Wernerfelt, and Balakrishnan 1989; Snow and Thomas 1994). In this vein, the authors present a methodology that allows them to move from the definition and specification of the PBQ construct to its measurement. The generality of this methodology enables it to be applied to other common constructs used in quality management (for example, customer satisfaction, perceived value, customer expectations, customer behavioral intention, and so on) that, as perceived quality, are unobservable and multidimensional.

The use of structural equations models methodology to develop the measurement instrument means that the limitations of classic validation procedures can be overcome, by incorporating the existence of measurement errors and latent or unobservable variables (Bollen 1989). Thus, one will be able to differentiate the variation because of differences between theoretical constructs, measurement methods, and random error.Finally, the results obtained in the application of the PBQ measurement instrument show the existence of a high reliability and construct validity. The estimated parameters show high reliability coefficients, and the different tests applied confirm the existence of a high construct validity. The confirmatory MTMM factor model allows the authors to confirm the existence of convergent and discriminant validity. As a whole, these results show that the PBQ measurement instrument meets the main requirements for measurement instruments in social sciences, and therefore, is suitable for application to the evaluation of perceived quality in all business types.

Concerning recommendations to practitioners, the use of this measurement instrument for the subjective PBQ has important practical implications. Quality is essential for company competitive advantage and organizational success. As Coyne (1986) and Porter (1985) state, businesses that pursue a differentiation strategy must focus their attention on differentiating the product and the service in the mind of the target client. This differentiation can only be reached in terms of the characteristics of the organization perceivable to the client. Thus, a key issue is to observe what aspects of business performance are perceived by the client, and this is precisely what the instrument developed in this article attempts to evaluate.

Furthermore, the methodology applied in its development avoids the problems highlighted by Harari (1997), who warns of the danger of only being concerned with developing a series of indicators on the internal processes of the company. According to this author, in a quality management initiative, some external indicators should be determined that inform managers of the changes in client perception and preferences. Customers’ perceptions of quality and their satisfaction levels are not static, and thus the study of their evolution is the first step toward a continuous improvement process (Deming 1986). The regular application of the instrument will inform managers on the evolution of customer perception of the quality of the business.

In addition to the assessment of the external effects of quality, the PBQ measurement instrument allows managers to focus their efforts on improving dimensions indicated as being of prime importance to the customer. This is an important subject if one considers that the improvement of product quality can require the adoption of improvements other than those recommended for the improvement of service quality.

Finally, with regard to future research, the results obtained suggest the need to take an in-depth look at the influence of PBQ on company ability to increase income (external effects). The instrument developed here can be used as a valid measurement to identify the existence of such effects. Nevertheless, limiting this study to a single industrial sector means that the relative importance of aspects related to service, product, and orientation toward quality in other industrial sectors must be analyzed, in order to determine how generalizable these results are, and if the scale items show stability when the measurement instrument is applied to other industries and/or clients.


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Juan Carlos Bou Llusar is a professor of management and organization at Universitat Jaume I. He earned a doctorate in business administration from the Universitat Jaume I. He has been a visiting professor at the University of Modena and Reggio Emilia (Italy). His primary areas of research cover total quality management and business strategy. In this area he has focused on the relationship between quality strategy and competitive business strategies, the development of measurement instruments of main quality constructs (this article belongs to this research line), and the influence of the implementation of quality policies on manufacturing, organizational, strategic, and human resource flexibility. He is the main researcher of the Total Quality Management and Strategy Research Group at the Universitat Jaume I. He has published in several international quality journals such as International Journal of Quality and Reliability Management and Total Quality Management. He may be contacted at the Department of Business Administration and Marketing, Universitat Jaume I, Campus Riu Sec S/N, 12002, Castellón, Spain, or by e-mail .

Cesar Camison has a doctorate in economic and business sciences. He is currently a professor at the Universitat Jaume I in Spain. He has been a visiting professor at the University of Texas, Universitá Commerciale Luigi Bocconi de Milán, and the University of Surrey. Camison is director of strategy, Knowledge Management and Organizational Learning Research Group. He is also director and founding member of Valencian Knowledge Management and Innovation Club, an interest group of the most principal Valencian enterprises as Lladró, Panama Jack, or Chocolates Valor. He has managed and developed more than 40 European and Spanish research projects. Camison is the author, coauthor, or coordinator of 26 books and more than 150 articles and papers. He can be reached at Universitat Jaume I, Department of Business Administration and Marketing, Campus Riu Sec, 12071 Castellón, Spain, or by e-mail at .


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