Quality management practicesand their impact on performance

Lassaad LakhalFaculte de Droit et des Sciences Economiques et Politiques de Sousse-Tunisia,

Sousse, Tunisia

Federico PasinHEC Montreal, Canada, and

Mohamed LimamISG Tunis, Tunisia

Abstract

Purpose – This paper aims to explore the relationship between quality management practices andtheir impact on performance.

Design/methodology/approach – First, critical quality management practices are identified andclassified in three main categories: management, infrastructure, and core practices. Then, a modellinking these practices and performance is proposed and empirically tested. The empirical data wereobtained from a survey of 133 Tunisian companies from the plastic transforming sector.

Findings – The results reveal a positive relationship between quality management practices andorganizational performance. Moreover, the findings show a significant relationship betweenmanagement and infrastructure practices. In addition, the results illustrate a direct effect ofinfrastructure practices on operational performance and of core practices on product quality.

Research limitations/implications – The conceptual model proposed and tested in this study canbe used by researchers for developing quality management theory. In addition, this model may offer aflow chart to practitioners for effective quality management implementation.

Originality/value – The proposed model is the first one to distinguish the direct effects ofinfrastructure practices on performance from the indirect effects of these practices through the corepractices. Besides, the use of path analysis method to study the direct and indirect relationshipsbetween quality management practices and their effect on performance dimensions.

Keywords Quality management, Performance measurement (quality), Modelling

Paper type Research paper

1. IntroductionQuality gurus have put forth several approaches to improve company performance.These approaches are embodied in a set of quality management practices, known astotal quality management (TQM). Several authors have attempted to clarify theconcept of TQM (Dean and Bowen, 1994; Dean and Evans, 1994; Hackman andWageman, 1995). TQM is generally described as a collective, interlinked system ofquality management practices that is associated with organizational performance(GAO, 1991; Tornow and Wiley, 1991; Waldman, 1994; Madu et al., 1995).

In this respect, several studies have attempted to identify the key quality managementpractices on which the success of a TQM process is based (Saraph et al., 1989; Flynn et al.,1994; Ahire et al., 1996). However, these studies have not considered possibleinteraction between practices. Recent studies, especially those of Cua et al. (2001),Sousa and Voss (2002) and Kaynak (2003), underline the importance of causal relations

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0265-671X.htm

Qualitymanagement

practices

625

Received September 2004Revised April 2005

International Journal of Quality &Reliability Management

Vol. 23 No. 6, 2006pp. 625-646

q Emerald Group Publishing Limited0265-671X

DOI 10.1108/02656710610672461

between quality management practices. Furthermore, many authors (Anderson et al.,1995; Flynn et al., 1995a; Mohrman et al., 1995; Choi and Eboch, 1998; Terziovski andSamson, 1999; Cua et al., 2001; Douglas and Judge, 2001; Kaynak, 2003) suggested apositive association between TQM practices and organizational performance. However,conflicting reports have been published regarding the effectiveness of TQM programs.For instance, Rategan (1992) reported a 90 percent improvement rate in employeerelations, operating procedures, customer satisfaction, and financial performance,whereas Burrows (1992) reported a 95 percent failure rate for initiated TQM programs.Authors diverge in the way they perceive the links between quality management practicesand performance. Some authors think that there is a hierarchy in the quality managementpractices and that infrastructure practices may only have a positive effect on performanceif core practices have also been established (Flynn et al., 1995a; Anderson et al., 1995). Inopposition, other authors (Powell, 1995; Dow et al., 1999; Samson and Terziovski, 1999)have suggested that each practice can improve performance even without the corepractices.

As we just seen, many important questions have recently been raised in the field ofquality management theory. In our study, we have focused on the three followingresearch questions:

(1) Which quality management practices are critical?

(2) How different quality management practices are related?

(3) What is the nature of the relationship between quality management practicesand performance?

In this paper, we propose and empirically test a conceptual model that links differentquality management practices and performance. The definitions of TQM andperformance retained in this study are consistent with those adopted by Hendricks andSinghal (1996, 1997), Easton and Jarrell (1998) and Douglas and Judge (2001). Note thatwe operationalisze TQM as a multidimensional construct, in contrast to the studiesmentioned above, which all use a single construct. Performance will here be defined inrelation to the quality of the organization’s results. Moreover, performance will beconsidered as a multidimensional construct.

2. Theoretical background2.1 Quality management practicesQuality management practices have been investigated extensively (Saraph et al., 1989;Flynn et al., 1994; Waldman, 1994; Powell, 1995; Ahire et al., 1996; Anderson and Sohal,1999; Najmi and Kehoe, 2000; Zhang et al., 2000; Sun, 2001; Sila and Ebrahimpour,2002; Kaynak, 2003). Although a plethora of practices have been described, similaritiesamong practices can be discerned. To generate distinct generic practices, we firstdefined a list of all the practices proposed in a large set of articles. We then took eachpractice, one at the time, analyzing it and questioning ourselves whether it wasdifferent or similar to the practices previously analyzed. This process resulted with theten following distinct generic practices: top management commitment and support,organization for quality, employee training, employee participation, supplier qualitymanagement, customer focus, continuous support, improvement of quality system,information and analysis, and statistical quality techniques use. Table I presents, foreach generic practice, a list of similar practices proposed by other authors. This list

IJQRM23,6

626

illustrates the foundations of our generic practices, and has strongly inspired thedefinition of items that will operationalize each practice. Table I also establishes linksbetween practices examined in our research and those described in other studies.Appendix 1 displays the specific scales and the 43 items used in our study to measurethe quality management practices.

After selecting ten generic practices, we grouped them into three main categoriesfollowing the classification of Flynn et al. (1995a), Pannirselvam and Ferguson (2001)and Sousa and Voss (2002), namely:

(1) management practice: issued from the top management;

(2) infrastructure practices: intended to support core practices; and

(3) core practices: based on tools and techniques specifically related to quality.

Practice Related practices

Top management commitmentand support

Top management commitment (Ahire et al., 1996; Powell, 1995;Tamimi, 1998), top management team involvement (Douglas andJudge, 2001), leadership (Anderson and Sohal, 1999; Sun, 2001;Zhang et al., 2000)

Organization for quality Quality management design (Ahire et al., 1996), open organization(Powell, 1995), cross-functional teams (LaHay and Noble, 1998),control and improvement of processes (Zhang et al., 2000)

Employee training Training (Saraph et al., 1989), education and/or training(Ahire et al., 1996; Kannan et al., 1999; Powell, 1995; Tamimi, 1998;Zhang et al., 2000), emphasis on TQM-oriented training(Douglas and Judge, 2001)

Employee participation Participation (Zhang et al., 2000), delegation (Ahire et al., 1996;Powell, 1995), employee involvement (Ahire et al., 1996), employeerelations (Saraph et al., 1989)

Supplier quality management Supplier quality management (Ahire et al., 1996; Saraph et al., 1989;Zhang et al., 2000), supplier management (Tamimi, 1998),suppliers (Najmi and Kehoe, 2000; Sun, 2001), supplier relations(Forza and Filippini, 1998; Powell 1995)

Customer focus Customer focus (Ahire et al., 1996; Anderson and Sohal, 1999;LaHay and Noble, 1998; Zhang et al., 2000), strong relations withcustomers (Powell, 1995), customer satisfaction (Forza andFilippini, 1998), customer driven (Douglas and Judge, 2001)

Continuous support Continuous improvement (Douglas and Judge, 2001), recognitionand rewards (Zhang et al., 2000)

Quality system improvement Quality system improvement (Zhang et al., 2000)Information and analysis Information and analysis (Anderson and Sohal, 1999; Choi and

Eboch, 1998), information (Sun, 2001), information flow (Kannanet al., 1999), quality information system (Najmi and Kehoe, 2000),process measurement (LaHay and Noble, 1998), use of internalinformation on quality (Ahire et al., 1996), Quality data(Saraph et al., 1989), measurement of quality (Powell, 1995),benchmarking (Ahire et al., 1996; Powell, 1995)

Statistical quality techniquesuse

Use of statistical procedure (Ahire et al., 1996), total qualitymethods (Douglas and Judge, 2001)

Table I.Links between practices

retained and literature

Qualitymanagement

practices

627

This classification constitutes the basis of our model, and highlights the links betweenquality management practices and firm performance. Table II presents theclassification of practices into each of the three categories.

2.2 Organizational performanceFollowing a literature review on strategic management, marketing and operationsmanagement, we have chosen three performance related dimensions: financialperformance, operational performance and product quality. The choice of keyindicators of financial performance is based on the research of Kaplan and Norton(1992). Moreover, the operational performance indicators are inspired from Grandzoland Gershon (1998), while the product quality indicators are based on studies carriedout by Garvin (1987), Forker et al. (1996), Curkovic et al. (1999) and Kelada (1996).Appendix 1 shows a detailed list of the retained indicators.

3. Conceptual model and hypotheses3.1 Conceptual modelFigure 1 shows a model of relations between top management practice, infrastructureand core practices, product quality, operational and financial performance.

Some other similar models have already been proposed in the qualitymanagement literature. These models can be divided in two groups. In the firstgroup (Flynn et al., 1995a; Anderson et al., 1995; Pannirselvam and Ferguson, 2001;Kaynak, 2003), infrastructure practices act indirectly on performance through corepractices. In the second group (Powell, 1995; Dow et al., 1999; Samson andTerziovski, 1999), infrastructure practices can improve performance even withoutcore practices. Our model can be viewed as a combination of the two groups ofmodels. More importantly, our model is the first one to distinguish the direct effectsof infrastructure practices on performance from the indirect effects of these practicesthrough the core practices.

3.2 HypothesesThe selected hypotheses stem directly from the model. More precisely, we have defineda hypothesis for each link that appears in the model. These links symbolize a directrelation between two elements of the model.

H1. Management practice is directly related to infrastructure practices.

The effect of management practice on the various components of infrastructurepractices were highlighted in the management literature. For instance, Adam et al. (1997)show through their research that leadership has a significant impact on training.

Management practice Infrastructure practices Core practices

Top management commitmentand support

Organization for qualityEmployee training

Quality system improvementInformation and analysis

Employee participation Statistical quality techniques useSupplier quality managementCustomer focusContinuous support

Table II.Classification of qualitymanagement practices

IJQRM23,6

628

In addition, several studies confirm a statistically significant correlation betweenmanagement practice and infrastructure practices (Ahire et al., 1996; Zhang et al., 2000).

Recently, some studies found statistically significant causal relations betweenmanagement practice and infrastructure practices (Flynn et al., 1995a; Handfield et al.,1998; Pannirselvam and Ferguson, 2001; Kaynak, 2003):

H2. Infrastructure practices is directly related to core practices.

Many studies have found statistically significant causal relations betweeninfrastructure practices and core practices (Anderson et al., 1995; Flynn et al., 1995a;Ho et al., 2001; Pannirselvam and Ferguson, 2001; Rahman and Bullock, 2002).Moreover, other studies have shown the existence of statistically significantcorrelations between specific infrastructure practices and some core practices (Ahireet al., 1996; Zhang et al., 2000):

H3. Infrastructure practices is directly related to operational performance.

This hypothesis is consistent with the study of Samson and Terziovski (1999) whichreveals that several infrastructure practices: workforce commitment, shared vision,and customer focus, are significantly related to operational performance. Thishypothesis also corroborates the findings of Adam (1994), Flynn et al. (1995a, b, c),Forza (1995), Madu et al. (1996), Adam et al. (1997) Anderson and Sohal (1999), Dow et al.(1999), Terziovski and Samson (1999), Cua et al. (2001) and Rahman and Bullock (2002),which all show a statistically significant link between infrastructure practices andoperational performance components.

Similarly, in a US defense contractors survey, Grandzol and Gershon (1997) foundthat employee fulfillment, cooperation, and customer focus were significantly related toperceived customer satisfaction:

Figure 1.Relations between

management practice,infrastructure practices,

core practices andperformance

Qualitymanagement

practices

629

H4. Infrastructure practices is directly related to financial performance.

Several empirical studies have measured the relationship between infrastructurepractices and financial performance. For instance, Adam (1994) found a statisticallysignificant relationship between human resources management practices and theprevious year’s return on assets (ROA). Powell (1995) found that having a zero-defectmentality, empowering employees, using cross-functional teams, top managementcommitment to quality, and working more closely with suppliers to accomplish qualitymanagement objectives were statistically related to perceived financial performance.

This hypothesis has also been tested by Ittner and Larcker (1996), Anderson andSohal (1999), Terziovski and Samson (1999), Najmi and Kehoe (2000), Sun (2000) andNilsson et al. (2001), who reported a link between infrastructure practices and financialperformance indicators. Moreover, Barker and Cagwin (2000) confirm the positiveeffect on financial performance of specific infrastructure practices, namely: generalmanagement commitment, customer focus, supplier relations, employee training,employee participation, internal cooperation, and open organization.

Adam et al. (1997) demonstrate a weak but statistically significant relationshipbetween infrastructure practices, namely: top management implication, employeeinvolvement, employee satisfaction, employee selection and development,compensation and customers with financial profitability.

The work of Hendricks and Singhal (1997) has provided evidence of a relationbetween the financial performance and the effectiveness of the implementation ofTQM. Many factors were taken into account in this research, notably the firm size, thedegree of capital intensity, the degree of firm diversification, the maturity of the TQMimplementation and the timing of the TQM implementation:

H5. Core practices is directly related to operational performance.

Adam et al. (1997) have shown a positive impact of certain core practices on differentaspects of operational performance. In addition, Choi and Eboch (1998) found asignificant direct link between core practices: “process quality and information” and“customer satisfaction”. This hypothesis is consistent with Anderson et al. (1995) andFlynn et al. (1995a), who report a statistically significant causal relation between corepractices and operational performance. This hypothesis also relies on studiesconducted by Adam (1994), Flynn et al. (1995b) Ittner and Larcker (1996) Anderson andSohal (1999) Terziovski and Samson (1999) and Rahman and Bullock (2002), who founda link between core practices and operational performance:

H6. Core practices is directly related to financial performance.

Pannirselvam and Ferguson (2001) identified statistically significant direct linkbetween core practice: “product and process management” and financial results. Inaddition, Barker and Cagwin (2000) revealed a positive relationship between corepractices: “continuous improvement tools” “design and improvement of processes” andthe ROA indicator of financial performance.

This hypothesis is also consistent with several studies which found a link betweencore practices and financial performance indicators (Adam, 1994; Adam et al., 1997;Easton and Jarrell, 1998; Anderson and Sohal, 1999; Terziovski and Samson, 1999;Najmi and Kehoe, 2000; Sun, 2000; Nilsson et al., 2001):

IJQRM23,6

630

H7. Core practices is directly related to product quality.

This hypothesis is consistent with the empirical studies conducted by Forza (1995) andChoi and Eboch (1998), which confirm the relationship between quality managementpractices and product quality. This hypothesis is also consistent with Ahire et al. (1996),who show that product quality has a statistically significant correlation with thefollowing core practices: “statistical quality techniques use” and “internal qualityinformation”.

4. Research methodologyIn this section we explain the methodology used to collect the data. We also draw thestatistical techniques used to test the research framework, the identification of criticalquality management practices and the construct validity of the measurement instrument.

4.1 Data collectionThe data used in this study was obtained using a survey questionnaire filled byTunisian managers from the plastic transforming sector. This sector was chosenbecause it reflects that of the Tunisian manufacturing sector, mainly by its widevariability of quality management implementation levels. Also, it is a manageablesector in terms of size of the study. First, a list of 133 Tunisian companies was definedon the basis of the industrial and commercial guide of Tunisia, which includes all themanufacturing industries. Then, these 133 companies were classified into threecategories (strong, medium and weak performance) based on ROI, ROA and growth ofsales indicators, and following the methodology proposed by Kotter (1992). The finalsample includes 92 companies with high and medium financial performance. Table IIIpresents the classification of studied companies into three categories.

To collect the data we have used a questionnaire. As Madu (1998) and Bavagnoliand Perona (2000) assert, the questionnaire is a popular data collection method instudies of quality management. The data collection instrument was pre-tested in tencompanies. The pre-tests included structured interviews with the general manager,quality manager, process engineer, human resources manager, several supervisors andworkers. All of them were asked:

. whether the questions were easy to understand;

. whether there were any other questions that needed to be included; and

. who was the right person to contact for the “real” study.

Feedback from the pilot study was used to clarify some questions. Based on thefeedback, some items in a few scales were dropped or added. In addition, to confirmthat the main aspects of quality management practices were covered, the draft

Performance Number of companies

High 28Medium 64Low 41Total 133

Table III.Company classification

Qualitymanagement

practices

631

questionnaire was reviewed by academicians and practitioners experts. Finally,because of their familiarity with both quality management practices and performance,general managers were considered as the most appropriate respondents.

Face-to-face interview was the method used to collect the data from each company’srespondent. The availability of the research team helped clarify any ambiguityconcerning definitions or issues related to the survey instrument.

4.2 Statistical techniquesThe causal relations shown in Figure 1 will be tested by means of the path analysismethod. Path analysis is a multivariate analytical methodology for empiricallyexamining sets of relationships represented in the form of a linear causal model(Duncan, 1966; Li, 1975). Mathematically, path analysis decomposes empiricalcorrelations or covariances among measured variables to estimate the path coefficientsin a path diagram (Neumann, 1978). Path analysis use simple bivariate correlations toestimate causal relations in a structural equation system (Hair et al., 1998).

One advantage of path analysis over conventional regression analyses is the abilityto extend the single-multiple-regression-equation treatment to a network of equationsinvolving more than one equation (Li, 1975). In addition, this method can differentiatedirect and indirect effects (Duncan, 1966; Pannirselvam and Ferguson, 2001).

4.3 Identification of critical quality management practicesThe critical quality management practices are determined according to the followingtwo analyses: principal factor analysis, and confirmatory factor analysis (Goodhue,1998; Grandzol and Gershon, 1998). Principal factor analysis is used in order to bring tothe foreground a factorial structure. Confirmatory factor analysis is applied in order toconfirm the factorial structure. Table IV displays the results of the principal componentanalysis (factors 1 and 2) and the confirmatory factor analysis (factor loadings).

Principal factor analysis show that some items should be deleted from the analysis,either because of very low factor loadings or because of multiple factor loadings toohigh to ignore (Grandzol and Gershon, 1998). Besides, confirmatory factor analysisconfirms these results. In fact, items indicated as potentially problematic according tothe principal factor analysis surfaced again in the confirmatory factor analysis. Moreprecisely, items with factor loadings below the suggested cut-off of 0.6 (Hatcher, 1994)were considered problematic. Given all these considerations, several items were deletedfrom the analysis (Table IV) and three practices (supplier quality management,continuous support, and statistical quality techniques use) were eliminated from theanalysis. Therefore, the final measurement instrument consists of seven qualitymanagement practices and 24 items.

4.4 Construct validity of the measurement instrumentTo empirically test the construct validity of the measurement instrument based on theseven quality management practices that emerged from the previous analysis, we haveapplied the three steps proposed by O’Leary-Kelly and Vokurka (1998):

(1) unidimensionality analysis;

(2) reliability analysis; and

(3) validity analysis.

IJQRM23,6

632

Quality management practices Itemsa Factor 1 Factor 2 Factor loadings

Top management commitment and support (TMCS) TMCS 1 0.779 0.096 0.75TMCS 2 0.681 0.195 0.63TMCS 3 0.811 0.08 0.85TMCS 4 0.744 0.184 0.7TMCS 5 0.006 0.972 0.07

Organization for quality (OFQ) OFQ 1 0.903 0.201 0.88OFQ 2 0.221 0.861 0.44OFQ 3 0.186 0.707 0.44OFQ 4 0.003 0.853 0.14OFQ5 0.902 0.196 0.9

Employee training (ET) ET 1 0.711 0.7ET 2 0.742 0.82ET 3 0.736 0.64ET 4 0.343 0.38ET 5 0.654 0.61

Employee participation (EP) EP 1 0.822 0.68EP 2 0.821 0.71EP 3 0.868 0.91

Suppliers quality management (SQM) SQM 1 0.019 0.893 0.17SQM 2 0.605 0.659 0.39SQM 3 0.848 0.779 0.45SQM 4 0.801 0.608 0.36

Customer focus (CF) CF 1 0.773 0.78CF 2 0.818 0.82CF 3 0.801 0.68CF 4 0.709 0.61

Continuous support (CS) CS 1 0.358 0.735 0.32CS 2 0.657 0.745 0.37CS 3 0.802 0.652 0.36CS 4 0.164 0.897 0.43

Quality system improvement (QSI) QSI 1 0.109 0.42QSI 2 0.799 0.76QSI 3 0.793 0.75QSI 4 0.778 0.75

Information and analysis (IAA) IAA 1 0.735 0.66IAA 2 0.751 0.66IAA 3 0.778 0.73IAA 4 0.855 0.88

Statistical quality technique use (SQTU) SQTU 1 0.099 0.996 0.12SQTU 2 0.378 0.716 0.23SQTU 3 0.244 0.836 0.39SQTU 4 0.745 0.673 0.42SQTU 5 0.853 0.703 0.41

Note: a Items in italics are deleted from the analysis

Table IV.Selection criteria for

critical practices

Qualitymanagement

practices

633

Unidimensionality implies satisfaction of two implicit conditions (O’Leary-Kelly andVokurka, 1998), namely:

(1) an empirical indicator must be significantly associated with a latent variable;and

(2) an empirical indicator must be associated with one and only one latentvariable.

Unlike the studies carried on by Fynes and Voss (2002) and Kaynak (2003), theunidimensionality analysis was performed on the basis of a principal componentanalysis of each of the seven practices. The results of this analysis reveal that theitems, for each practice, allow the extraction of a single factor. The unidimensionalityof the measurement scale is therefore confirmed.

The reliability analysis was performed based on Joreskog’s r of internalconsistency (Joreskog et al., 1999). The convergent validity analysis was verifiedaccording to Fornell and Larcker (1981) approach. Table V illustrates the results ofthese analyses.

The results confirm the reliability of the quality management practices retained.Specifically, Joreskog’s r are all higher than the threshold of (0,8), with the exception ofthe r representing the “employee training” practice, which is nonetheless very close tothat level (0, 78). Moreover, Table V illustrates good convergent validity. All factorloadings are greater than the threshold of (0, 6). Moreover, the r of convergent validityexceeds the threshold of 50 percent for all measurement scales, except “employeetraining”.

To examine the discriminant validity, the Chi-squares of the constrained andunconstrained models were compared (Anderson and Gerbing, 1988). Significantlylower Chi-squares for the unconstrained model would indicate that each correlationbetween pairs is less than 1.0 (Bagozzi et al., 1991), and that the constructs areempirically distinct, rendering support for the discriminant validity of the constructs.In our case, the Chi-squares (x 2) of the unconstrained model (x 2 ¼ 410.15; df ¼ 231)was significantly lower than the Chi-squares of the constrained model (x 2 ¼ 963.85;df ¼ 252). Given the differential of 21 degrees of freedom (Ddf ¼ 252 2 231 ¼ 21) anda type 1 risk of 1 percent, the tabulated value of the Chi-squares is 39.932. As thedifference in Chi-squares (Dx 2 ¼ 963.85 2 410.15 ¼ 553.7) largely exceeds thetabulated value, the discriminant validity is confirmed.

Latent variables Joreskog’s r r of convergent validity

Top management commitment and support 0.81 0.53Organization for quality 0.90 0.81Employee training 0.78 0.49Employee participation 0.81 0.60Customer focus 0.83 0.56Information and analysis 0.82 0.54Quality system improvement 0.80 0.57

Table V.Reliability andconvergent validityanalyses

IJQRM23,6

634

5. Empirical findingsAfter testing construct validity, we then tested the causal relations between the latentvariables in order to confirm or refute the hypothesis presented earlier. Thus,consistently with Swamidass and Newell (1987), Anderson et al. (1995) and Flynn et al.(1995a), we tested our conceptual model by means of the path analysis approach. Theconceptual model can also be represented by equations (see Appendix 2).

The model does not deny the existence of variables such as organizational context,industry, structure and technology, which may play an important role in theexplanation of organizational performance. Despite that, these variables are notincluded in our model explicitly. Their effects will be considered by the nine errorterms specified in the model.

Before testing the hypothesis, we will validate for the absence of themulticolinearity problem. To do so, we applied the methodology proposed by Flynnet al. (1995a), which consists in studying the diagonal of the inverse of the correlationmatrix. This diagonal includes elements called variance inflation factors (VIF) equal to1/(1-R 2), where R 2 measures the portion of variance of each variable explained by theother variables. Neter et al. (1990) assert that the VIF values must not exceed therecommended threshold of ten. Since, all the coefficients (VIF) are situated between1.11 and 3.70, the multicolinearity is not a problem in this case.

To calculate the path coefficient, in order to indicate the explanatory power of eachantecedent variable on the dependent variable, we used LISREL software, with thecorrelation matrix as the input, and we applied the methodology used by Andersonet al. (1995). The results are presented in Table VI.

Of the 26 relations tested, 18 were found to be significantly different from zero withan error risk below 1 percent, 2 are different from zero with a risk below 5 percent, and6 relations were not statistically significant.

Table VI shows that “top management commitment and support” practice has astatistically significant direct effect ( p-value , 0.01) on the following practices:“organization for quality” “employee training” “employee participation” and “customerfocus”. This finding confirms the first hypothesis.

The practices “organization for quality” “employee training” and “employeeparticipation” have a statistically significant direct effect ( p-value , 0.01) on the“information and analysis” practice. Moreover, the practices “employee training”“employee participation” and “customer focus” have a statistically significant directeffect ( p-value , 0.01) on the “quality system improvement” practice. “Customerfocus” practice does not have a significant effect on the “information and analysis”practice. “Organization for quality” practice does not have a significant direct effect on“quality system improvement” practice. To summarize, the infrastructure practices donot all have a significant effect on the core practices; yet overall, their effect issignificant. This partly confirms the second hypothesis.

The results show that all infrastructure practices have a statistically significantdirect effect on operational performance. This confirms the third hypothesis. Note thatin addition to having a direct effect on operational performance, “organization forquality” and “employee training” practices also have a significant indirect effect onoperational performance via core practices.

The results also illustrate that the practices: “organization for quality” and“customer focus” have a statistically significant direct effect ( p-value , 0.05) on

Qualitymanagement

practices

635

Ind

epen

den

tV

aria

ble

Dep

end

ent

var

iab

leD

irec

tef

fect

Ind

irec

tef

fect

Tot

alef

fect

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Org

aniz

atio

nfo

rq

ual

ity

0.52

**

0.00

0.52

**

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Em

plo

yee

trai

nin

g0.

43*

*0.

000.

43*

*

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Em

plo

yee

par

tici

pat

ion

0.74

**

0.00

0.74

**

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Cu

stom

erfo

cus

0.31

**

0.00

0.31

**

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Info

rmat

ion

and

anal

ysi

s0.

000.

57*

*0.

57*

*

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Qu

alit

ysy

stem

imp

rov

emen

t0.

000.

59*

*0.

59*

*

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Fin

anci

alp

erfo

rman

ce0.

000.

30*

*0.

30*

*

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Pro

du

ctq

ual

ity

0.00

0.40

**

0.40

**

Top

man

agem

ent

com

mit

men

tan

dsu

pp

ort

Op

erat

ion

alp

erfo

rman

ce0.

000.

42*

*0.

42*

*

Org

aniz

atio

nfo

rq

ual

ity

Info

rmat

ion

and

anal

ysi

s0.

46*

*0.

000.

46*

*

Em

plo

yee

trai

nin

gIn

form

atio

nan

dan

aly

sis

0.36

**

0.00

0.36

**

Em

plo

yee

par

tici

pat

ion

Info

rmat

ion

and

anal

ysi

s0.

27*

*0.

000.

27*

*

Cu

stom

erfo

cus

Info

rmat

ion

and

anal

ysi

s2

0.06

ns

0.00

20.

06n

sO

rgan

izat

ion

for

qu

alit

yQ

ual

ity

syst

emim

pro

vem

ent

20.

007n

s0.

002

0.00

7ns

Em

plo

yee

trai

nin

gQ

ual

ity

syst

emim

pro

vem

ent

0.41

**

0.00

0.41

**

Em

plo

yee

par

tici

pat

ion

Qu

alit

ysy

stem

imp

rov

emen

t0.

44*

*0.

000.

44*

*

Cu

stom

erfo

cus

Qu

alit

ysy

stem

imp

rov

emen

t0.

31*

*0.

000.

31*

*

Org

aniz

atio

nfo

rq

ual

ity

Fin

anci

alp

erfo

rman

ce0.

19*

0.22

*0.

41*

*

Org

aniz

atio

nfo

rq

ual

ity

Pro

du

ctq

ual

ity

0.00

0.15

*0.

15*

Org

aniz

atio

nfo

rq

ual

ity

Op

erat

ion

alp

erfo

rman

ce0.

68*

*0.

26*

*0.

94*

*

(con

tinued

)

Table VI.Structural model analysis

IJQRM23,6

636

Ind

epen

den

tV

aria

ble

Dep

end

ent

var

iab

leD

irec

tef

fect

Ind

irec

tef

fect

Tot

alef

fect

Em

plo

yee

trai

nin

gF

inan

cial

per

form

ance

20.

06n

s0.

19*

0.13

ns

Em

plo

yee

trai

nin

gP

rod

uct

qu

alit

y0.

000.

26*

*0.

26*

*

Em

plo

yee

trai

nin

gO

per

atio

nal

per

form

ance

0.60

**

0.20

*0.

80*

*

Em

plo

yee

par

tici

pat

ion

Fin

anci

alp

erfo

rman

ce2

0.12

ns

0.15

*2

0.03

ns

Em

plo

yee

par

tici

pat

ion

Pro

du

ctq

ual

ity

0.00

0.24

*0.

24*

Em

plo

yee

par

tici

pat

ion

Op

erat

ion

alp

erfo

rman

ce0.

82*

*0.

09n

s0.

91*

*

Cu

stom

erfo

cus

Fin

anci

alp

erfo

rman

ce0.

20*

20.

02n

s0.

18*

Cu

stom

erfo

cus

Pro

du

ctq

ual

ity

0.00

0.09

ns

0.09

ns

Cu

stom

erfo

cus

Op

erat

ion

alp

erfo

rman

ce0.

51*

*2

0.07

ns

0.44

**

Info

rmat

ion

and

anal

ysi

sF

inan

cial

per

form

ance

0.50

**

0.00

0.50

**

Info

rmat

ion

and

anal

ysi

sP

rod

uct

qu

alit

y0.

34*

*0.

000.

34*

*

Info

rmat

ion

and

anal

ysi

sO

per

atio

nal

per

form

ance

0.57

**

0.00

0.57

**

Qu

alit

ysy

stem

imp

rov

emen

tF

inan

cial

per

form

ance

0.03

5ns

0.00

0.03

5ns

Qu

alit

ysy

stem

imp

rov

emen

tP

rod

uct

qu

alit

y0.

34*

*0.

000.

34*

*

Qu

alit

ysy

stem

imp

rov

emen

tO

per

atio

nal

per

form

ance

20.

13n

s0.

002

0.13

ns

Notes:

* p,

0.05

;*

* p,

0.01

;th

esy

mb

oln

sm

ean

sn

otsi

gn

ifica

nt

Table VI.

Qualitymanagement

practices

637

financial performance. “Employee training” and “employee participation” practices arenot significantly linked to financial performance. In this sense, the fourth hypothesis ispartly confirmed.

Moreover, the practices: “organization for quality” “employee training” and“employee participation” have a significant indirect effect ( p-value , 0.05) onfinancial performance via core practices. Overall, we can assert that the infrastructurepractices have a notable indirect effect on financial performance.

“Information and analysis” practice have a statistically significant direct effect( p-value , 0.01) on both operational and financial performance. In contrast, the“quality system improvement” practice does not have a significant direct effect neitheron operational nor on financial performance. This implies that the fifth and sixthhypotheses are partly confirmed.

The “information and analysis” and the “quality system improvement” practiceshave a statistically significant direct effect ( p-value , 0.01) on product quality. Thisimplies that the seventh hypothesis is confirmed.

Lastly, note that some relations, which were not covered by the hypotheses, werehighlighted during the analysis. This is the case of the indirect effects of the practice“top management commitment and support” on the practices “information andanalysis” “quality system improvement” and “product quality” and on operational andfinancial performance. It is also the case of indirect effect between the infrastructurepractices (except for the “customer focus” practice) and product quality.

6. Discussion6.1 Discussion of resultsIn this study we have provided empirical evidence that quality management practiceshave a positive impact on organizational performance. This result corroborates the studiesof Douglas and Judge (2001), Easton and Jarrell (1998) and Hendricks and Singhal (1996,1997). Note that all of these studies operationalize TQM as a single construct, in contrastwith our study, which operationalizes it as a multiple construct. Our approach is thereforeinspired by Palich et al. (2000), who underscored the importance of obtaining consistentresults among multiple studies that use different methodologies.

Besides, the results highlight the crucial role played by top managementcommitment and support. Specifically, the management practice has a statisticallysignificant direct and indirect link with all the other practices of our model. Theseconclusions corroborate previous studies by Adam et al. (1997), Ahire et al. (1996),Ahire and O’Shaughnessy (1998), Flynn et al. (1995a) Pannirselvam and Ferguson(2001) and Wilson and Collier (2000).

Moreover, the results of our empirical study clarify the relative importance and theinterplay between infrastructure, core practices and organizational performance. Thesefindings notably show that infrastructure practices have a statistically significantdirect effect on operational performance. These results support the ones of Powell(1995), Dow et al. (1999) and Samson and Terziovski (1999).

The findings also illustrate that infrastructure practices act on product quality and onoperational and financial performance by means of core practices, in that the indirecteffect of infrastructure practices on performance dimensions is statistically significant.These results are consistent with the empirical studies of Flynn et al. (1995a), Andersonet al. (1995), Pannirselvam and Ferguson (2001) and Kaynak (2003). In addition, theresults clarify the direct and significant effect of core practices on product quality. They

IJQRM23,6

638

also reveal that the core practice “information and analysis” is the only one that has adirect and significant effect on both operational and financial performance.

Besides, the results also support the interdependence between quality managementpractices. This finding is in line with the studies of Flynn et al. (1995a), Anderson et al.(1995), Pannirselvam and Ferguson (2001) and Kaynak (2003).

From a manager’s point of view, the empirically validated positive effects of qualitymanagement practices on organizational performance is encouraging for those whotake the initiative to implement TQM. Besides, the measurement instrument developedand validated empirically in this paper can be used by managers to evaluate their TQMimplementation and to target improvement areas.

6.2 Limitations of findingsSeveral limitations of this study should be discussed in this section. First, the datacollection method was based on managers’ perceptions. As reported by Dearborn andSimon (1958), managers respond to questionnaires from their own local environment,which may or may not reflect what is going on in the organization as a whole.Consequently, it is rather dangerous to readily assume that an individual response is areliable and valid indicator of an organization-level construct (Venkatraman andGrant, 1986). Nevertheless, the use of manager’s perceptions is frequently used inquality management research (Madu, 1998). Second, based on the type of data used inthe analysis, our study was not able to capture the effects of other mutually supportiveprocess management techniques (e.g. business process reengineering and JIT) on firmperformance (Flynn et al., 1995b).

In addition to the limitations already mentioned, we acknowledge the fact that thesample size is relatively small. The results of this study should not be generalized beyondwhat is reasonable, given the nature of the sample. Future studies should considersubstantially larger samples including greater representation of industries and countries.Given the cross-sectional nature of the data, we should exercise caution in drawing causalinferences from the findings of this study. Despite this caveat, we observe an associationbetween management practice, infrastructure and core practices, and performance. Moredetailed longitudinal studies may be appropriate for assessing causality.

7. Conclusion and future directionsThe primary objective of this paper is to study the relationship between qualitymanagement practices and their impact on performance. This research attempts tocontribute to the development of a quality management theory.

To carry out this research, we started by identifying a set of practices and items ofquality management presented in the literature. Then, we used this set to formulate ourconceptual model that links management practice, infrastructure and core practices,product quality, operational and financial performance. Seven hypotheses regardingthe relations between the elements of the model were specified. An empirical studythen enabled us to select, among all the practices and items proposed in the literature,24 items that made up the following seven practices: top management commitment andsupport, organization for quality, employee training, employee participation, customerfocus, information and analysis, and quality system improvement.

The hypotheses regarding the relationships in the model were then empiricallytested on a sample of 92 companies in the plastics transformation industry using thepath analysis method. The results highlight the crucial role played by top management

Qualitymanagement

practices

639

commitment and support and clarify the relative importance and the interplay betweeninfrastructure, core practices and organizational performance.

The majority of the research related to TQM theory can be classified into the“mapping and relationship” stage of the scientific inquiry process as defined byHandfield and Melnyk (1998). Further research must advance beyond this stagetowards the last two stages of the scientific inquiry process: “theory validation” and“theory extension/refinement”. In this respect, future research that focus on applyingthe proposed model in new industrial and national contexts would be helpful. Besides,there is also a need to refine and clarify the relative importance and the interplaybetween core and infrastructure practices in determining performance.

References

Adam, E.E. Jr (1994), “Alternative quality improvement practices and organizationperformance”, Journal of Operations Management, Vol. 12 No. 1, pp. 27-44.

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 approachand firm performance”, International Journal of Operations & Production Management,Vol. 7 No. 9, pp. 842-73.

Ahire, S.L. and O’Shaughnessy, K.C. (1998), “The role of top management commitment in qualitymanagement: an empirical analysis of the auto parts industry”, International Journal ofQuality Science, Vol. 3 No. 1, pp. 5-37.

Ahire, S.L., Golhar, D.Y. and Waller, M.A. (1996), “Development and validation of TQMimplementation constructs”, Decision Sciences, Vol. 27 No. 1, pp. 23-56.

Anderson, J. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review andrecommended two-step approach”, Psychological Bulletin, Vol. 103, pp. 411-23.

Anderson, J.C., Rungtusanatham, M., Schroeder, R.G. and Devaraj, S. (1995), “A path analyticmodel of a theory of quality management underlying the Deming management method:preliminary empirical findings”, Decision Sciences, Vol. 26 No. 5, pp. 637-58.

Anderson, M. and Sohal, A.S. (1999), “A study of the relationship between quality managementpractices and performance in small businesses”, International Journal of Quality &Reliability Management, Vol. 16 No. 9.

Bagozzi, R., Yi, Y. and Phillips, L. (1991), “Assessing construct validity in organizationalresearch”, Administrative Science Quarterly, Vol. 36 No. 3, pp. 421-58.

Barker, K.J. and Cagwin, D. (2000), “New evidence relating TQM to financial performance: anempirical study of manufacturing firms”, working paper, The University of Texas,Brownsville.

Bavagnoli, G. and Perona, M. (2000), “Surveying quality management: a new measure-orientedapproach”, International Journal of Quality & Reliability Management, Vol. 17 No. 8,pp. 839-57.

Burrows, P. (1992), “TQM reality check: it works, but it’s not cheap or easy”, Electronic Business,Vol. 18, pp. 8-22.

Choi, T.Y. and Eboch, K. (1998), “The TQM paradox: relations among TQM practices, plantperformance, and customer satisfaction”, Journal of Operations Management, Vol. 17,pp. 59-75.

Cua, K.O., Mc Kone, K.E. and Schroeder, R.G. (2001), “Relationships between implementation ofTQM, JIT, and TPM and manufacturing performance”, Journal of OperationsManagement, Vol. 19, pp. 675-94.

IJQRM23,6

640

Curkovic, S., Vickery, S.K. and Droge, C.L.M. (1999), “Quality and business performance: anempirical study of first-tier automotive suppliers”, Quality Management Journal, Vol. 6No. 2.

Dean, J.W. and Bowen, D.E. (1994), “Management theory and total quality management:improving research and practice through theory development”, Academy of ManagementReview, Vol. 19 No. 3, pp. 392-418.

Dean, J.W. and Evans, J.R. (1994), Total Quality: Management, Organization, and Strategy,West Publishing, St Paul, MN.

Dearborn, D.C. and Simon, H.A. (1958), “Selective perception: a note on the departmentalidentification of executives”, Sociometry, Vol. 21, pp. 140-4.

Douglas, T.J. and Judge, W.Q. Jr (2001), “Total quality management implementation andcompetitive advantage: the role of structural control and exploration”, Academy ofManagement Journal, Vol. 44, pp. 158-69.

Dow, D., Samson, D. and Ford, S. (1999), “Exploding the myth: do all quality managementpractices contribute to superior quality performance”, Production and OperationsManagement, Vol. 8 No. 1, pp. 1-27.

Duncan, O.D. (1966), “Path analysis: sociological examples”, American Journal of Sociology,Vol. 72 No. 1, pp. 1-16.

Easton, G.S. and Jarrell, S.L. (1998), “The effects of total quality management on corporateperformance: an empirical investigation”, The Journal of Business, Vol. 71 No. 2.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1994), “A framework for quality managementresearch and an associated measurement instrument”, Journal of Operations Management,Vol. 11 No. 4, pp. 339-66.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1995a), “The impact of quality managementpractices on performance and competitive advantage”, Decision Sciences, Vol. 26 No. 5,pp. 659-91.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1995b), “Relationship between JIT and TQM:practices and performance”, Academy of Management Journal, Vol. 38 No. 5, pp. 1325-60.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1995c), “Determinants of quality performance inhigh and low quality plants”, Quality Management Journal, Winter, pp. 8-25.

Forker, L.B., Vickery, S.K. and Droge, C.L.M. (1996), “The contribution of quality to businessperformance”, International Journal of Operations & Production Management, Vol. 16No. 8, pp. 44-62.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equations models with unobservablevariables and measurement error”, Journal of Marketing Research, Vol. 18, pp. 39-50.

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.

Forza, C. and Filippini, R. (1998), “TQM impact on quality conformance and customer satisfaction:a causal model”, International Journal of Production Economics, Vol. 55, pp. 1-20.

Fynes, B. and Voss, C. (2002), “The moderating effect of buyer-supplier relationships on qualitypractices and performance”, International Journal of Operations & ProductionManagement, Vol. 22 No. 6, pp. 589-613.

GAO (1991), Management Practices: US Companies Improve Performance through QualityEfforts, General Accounting Office, Washington, DC.

Garvin, D.A. (1987), “Competing on the eight dimensions of quality”, Harvard Business Review,Vol. 5 No. 6, pp. 101-9.

Qualitymanagement

practices

641

Goodhue, D.L. (1998), “Development and measurement validity of a task-technology fitinstrument for user evaluations of information systems”, Decision Sciences, Vol. 29 No. 1,pp. 105-38.

Grandzol, J.R. and Gershon, M. (1997), “A survey instrument for standardizing TQM modelingresearch”, International Journal of Quality Science, Vol. 3 No. 1, pp. 80-105.

Grandzol, J.R. and Gershon, M. (1998), “Which TQM practices really matter: an empiricalinvestigation”, Quality Management Journal, Vol. 4 No. 4, pp. 43-59.

Hackman, J.R. and Wageman, R. (1995), “Total quality management: empirical, conceptual, andpractical issues”, Administrative Science Quarterly, Vol. 40 No. 2, pp. 309-42.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, 5thed., Prentice-Hall, Upper Saddle River, NJ.

Handfield, R. and Melnyk, S. (1998), “The scientific theory-building process: a primer using thecase of TQM”, Journal of Operations Management, Vol. 16 No. 4, pp. 321-39.

Handfield, R., Ghosh, S. and Fawcett, S. (1998), “Quality-driven change and its effects on financialperformance”, Quality Management Journal, Vol. 5 No. 3, pp. 13-30.

Hatcher, L. (1994), A Step-By-Step Approach to Using the SAS System Factor Analysis andStructural Equation Modeling, SAS Institute, Cary, NC.

Hendricks, K.B. and Singhal, V.R. (1996), “Quality awards and the market value of the firm: anempirical investigation”, Management Science, Vol. 42 No. 3, pp. 415-36.

Hendricks, K.B. and Singhal, V.R. (1997), “Does implementing an effective TQM programactually improve operating performance: empirical evidence from firms that have wonquality awards”, Management Science, Vol. 44 No. 9, pp. 1258-74.

Ho, D.C.K., Duffy, V.G. and Shih, H.M. (2001), “Total quality management: an empirical test formediation effect”, International Journal of Production Research, Vol. 39, pp. 529-48.

Ittner, C.D. and Larcker, D.F. (1996), “Total quality management and the choice of informationand reward systems”, Journal of Accounting Research, Vol. 33, pp. 1-34.

Joreskog, K.G., Sorbom, D., Du Toit, S. and Du Toit, M. (1999), LISREL 8: New StatisticalFeatures, Scientific Software International, Moorsville, IN.

Kannan, V.R., Tan, K.C., Handfield, R.B. and Ghosh, S. (1999), “Tools and techniques of qualitymanagement: an empirical investigation of their impact on performance”, QualityManagement Journal, Vol. 6 No. 3, pp. 34-50.

Kaplan, R.S. and Norton, D.P. (1992), “The balanced scorecard – measures that driveperformance”, Harvard Business Review, January-February.

Kaynak, H. (2003), “The relationship between total quality management practices and theireffects on firm performance”, Journal of Operations Management, Vol. 34 No. 2, pp. 1-31.

Kelada, J.N. (1996), Integrating Reengineering with Total Quality, Editions QSIC.

Kotter, J.P. (1992), Corporate Culture and Performance, Free press, New York, NY.

LaHay, C.W. and Noble, J.S. (1998), “A framework for business system and quality managementintegration”, International Journal of Quality & Reliability Management, Vol. 15 No. 6.

Li, C.C. (1975), Path Analysis: A Primer, The Boxwood Press, Pacific Grove, CA.

Madu, C.N. (1998), “An empirical assessment of quality: research considerations”, InternationalJournal of Quality Science, Vol. 3 No. 4, pp. 348-55.

Madu, C.N., Kuei, C.H. and Jacob, R.A. (1996), “An empirical assessment of the influence ofquality dimensions on organizational performance”, International Journal of ProductionResearch, Vol. 34 No. 7, pp. 1943-62.

IJQRM23,6

642

Madu, C.N., Kuei, C.H. and Lin, C. (1995), “A comparative analysis of quality practice inmanufacturing firms in the US and Taiwan”, Decision Sciences, Vol. 26, pp. 621-35.

Mohrman, S.A., Tenkasi, R.V., Lawler, E.E. III and Ledford, G.G. Jr (1995), “Total qualitymanagement: practice and outcomes in the largest US firms”, Employee Relations, Vol. 17No. 3, pp. 26-41.

Najmi, M. and Kehoe, D.F. (2000), “An integrated framework for post-ISO 9000 qualitydevelopment”, International Journal of Quality & Reliability Management, Vol. 17 No. 3,pp. 226-58.

Neter, J., Wasserman, W. and Kutner, L. (1990), Applied Linear Statistical Models, Irwin,Homewood, IL.

Neumann, Y. (1978), “Assessing causality in organizational studies: the path-analytic approach”,Academy of Management Review, Vol. 3 No. 2, pp. 366-9.

Nilsson, L., Johnson, M.D. and Gustafsson, A. (2001), “The impact of quality practices oncustomer satisfaction and business results: product versus service organizations”, Journalof Quality Management, Vol. 6, pp. 5-27.

O’Leary-Kelly, S.W. and Vokurka, R.J. (1998), “The empirical assessment of construct validity”,Journal of Operations Management, Vol. 16, pp. 387-405.

Palich, L.E., Cardinal, L.B. and Miller, C.C. (2000), “Curvilinearity in the diversification andperformance linkage: an examination of over three decades of research”, StrategicManagement Journal, Vol. 21, pp. 155-74.

Pannirselvam, G.P. and Ferguson, L.A. (2001), “A study of the relationships between the Baldrigecategories”, International Journal of Quality & Reliability Management, Vol. 18 No. 1,pp. 14-37.

Powell, T.C. (1995), “Total quality management as competitive advantage: a review andempirical study”, Strategic Management Journal, Vol. 16, pp. 15-37.

Rahman, S. and Bullock, P. (2002), Relationships Between Soft TQM, Hard TQM, andOrganisational Performance, Working Paper No. 10, Institute of Transport Studies,The University of Sydney.

Rategan, C. (1992), “Total quality management”, Journal of Property Management, Vol. 57,pp. 32-4.

Samson, D. and Terziovski, M. (1999), “The relations between total quality managementpractices and operational performance”, Journal of Operations Management, Vol. 17,pp. 393-409.

Saraph, J.V., Benson, P.G. and Schroeder, R.G. (1989), “An instrument for measuring the criticalfactors of quality management”, Decision Sciences, Vol. 20 No. 4, pp. 810-92.

Sila, S. and Ebrahimpour, M. (2002), “An investigation of the total quality management surveybased research published between 1989 and 2000: a literature review”, InternationalJournal of Quality & Reliability Management, Vol. 19 No. 7, pp. 902-70.

Sousa, R. and Voss, C.A. (2002), “Quality management re-visited: a reflective review and agendafor future research”, Journal of Operations Management, Vol. 20, pp. 91-109.

Sun, H. (2000), “Total quality management, ISO 9000 certification, and performanceimprovement”, International Journal of Quality & Reliability Management, Vol. 17 No. 2,pp. 168-79.

Sun, H. (2001), “Comparing quality management practices in the manufacturing and serviceindustries: learning opportunities”, Quality Management Journal, Vol. 8 No. 2, pp. 53-71.

Swamidass, P.M. and Newell, W.T. (1987), “Manufacturing strategy, environmental uncertaintyand performance: a path analytic model”, Management Sciences, Vol. 33 No. 4, pp. 509-24.

Qualitymanagement

practices

643

Tamimi, N. (1998), “A second-order factor analysis of critical TQM factors”, International Journalof Quality Science, Vol. 3 No. 1, pp. 71-9.

Terziovski, M. and Samson, D. (1999), “The link between total quality management practice andorganizational performance”, International Journal of Quality & Reliability Management,Vol. 16 No. 3.

Tornow, W.W. and Wiley, J.W. (1991), “Service quality and management practices: a look atemployee attitudes, customer satisfaction, and bottom-line consequences”, HumanResource Planning, Vol. 14, pp. 105-15.

Venkatraman, N. and Grant, J.H. (1986), “Construct measurement in organizational strategyresearch: a critique and proposal”, Academy of Management Review, Vol. 11 No. 1,pp. 71-87.

Waldman, D.A. (1994), “The contribution of TQM to a theory of work performance”, Academy ofManagement Review, Vol. 19 No. 3.

Wilson, D.D. and Collier, D.A. (2000), “An empirical investigation of the Malcolm Baldrigenational quality award causal model”, Decision Sciences, Vol. 31 No. 2, pp. 361-90.

Zhang, Z., Waszink, A. and Wijngaard, J. (2000), “An instrument for measuring TQMimplementation for Chinese manufacturing companies”, International Journal of Quality &Reliability Management, Vol. 17 No. 7.

Further reading

Nunnally, J.C. (1978), Psychometric Theory, McGraw-Hill, New York, NY.

Appendix 1. TQM practicesFor the seven TQM practices, respondents indicated the extent to which the items representedpractices in their organizations (1 ¼ “very low” to 5 ¼ “very high”). Items in italic type wereeliminated from the analysis:

(1) Top management commitment and support:

General management is actively involved in quality improvement.

Management provides the necessary resources to carry out activities efficiently.

General management encourages employees to consider customers’ needs andexpectations.

Management quality objectives are disseminated to all employees.

Top management pursues long-term objectives.

(2) Organization for quality:

The organization has a process management method.

Interdepartmental groups are common.

Processes are continuously improved.

The organization uses quality circles.

There is a little bureaucracy (formal hierarchy, procedures and detailed rules) in theorganization.

(3) Employee training:

The company provides continuous training for its managerial personnel.

The company provides continuous training for its non-managerial personnel.

Training needs are always evaluated.

IJQRM23,6

644

Employees can take training leave.

The company measures employee satisfaction with training received.

(4) Employee participation:

Employees are encouraged to be totally involved.

Management lets employees participate in achieving organizational objectives.

Employees are responsible for the tasks they perform, and inspect their own work.

(5) Suppliers quality management:

The company purchases raw materials only from suppliers with ISO 9,000 certification.

The company works in close collaboration with suppliers to improve processes.

The company supplies technical assistance to suppliers.

The company is partnering with its suppliers.

(6) Customer focus:

Client is integrated in the product development process.

Company carries out studies to evaluate customer satisfaction.

Company carries out market studies to determine its customers’ needs and wants.

Company has a system to collect customers’ complaints.

(7) Continuous support:

Company has put in place a reward system to encourage new ideas.

Organization insists on continuous improvement of its products and services.

General management actively displays an ongoing commitment to quality improvement.

General management has appointed a coordinator who is in charge of operationalizingthe quality program within the company.

(8) Quality system improvement:

Company has a clear quality manual.

Quality system in our company is improved continuously.

Company has a clear documentation procedure.

Company has a clear set of work instructions.

(9) Information and analysis:

Important information is presented and transmitted to employees.

Company collects and analyzes data related to its activities.

Company harnesses information to improve its key processes, products and services.

Company has precise data about the competition used to identify areas of improvement.

(10) Statistical quality techniques use:

Cards and graphs are used to measure and control quality.

General management encourages the use of statistical methods.

Statistical techniques are used intensively in the company.

Employees participate in training programs related to statistical techniques for quality.

Statistical techniques are effective at improving product quality.

Qualitymanagement

practices

645

Organizational performanceThe scale represents the organization’s relative performance (1 ¼ “much worse than thecompetitors” to 5 ¼ “much better than the competitors”).

Financial performance Operational performance Product quality

Return on investments Wastelevel Reliability

Return on assets Productivity Durability

Sales growth Cycle time Tenacity

Regularity

Appendix 2

OFQ ¼ p21TMCS þ e1

ET ¼ p31TMCS þ e2

EP ¼ p41TMCS þ e3

CF ¼ p51TMCS þ e4

IA ¼ p62OFQ þ p63ET þ p64EP þ p65CF þ e5

QSI ¼ p72OFQ þ p73ETþp74EP þ p75CF þ e6

FP ¼ p82OFQ þ p83ET þ p84EP þ p85CF þ p86IA þ p87QSI þ e7

PQ ¼ p96IAþp97QSI þ e8

OP ¼ p102OFQ þ p103ET þ p104EP þ p105CF þ p106IA þ p107QSI þ e9

with, TMCS: Top management commitment and support, OFQ: Organization for quality, ET:Employee training, EP: Employee participation, CF: Customer focus, IA: Information andanalysis, QSI: Quality system improvement, FP: Financial performance, PQ: Product quality, OP:Operational performance, pij ¼ Path coefficient, ei ¼ Errors outside the model

Several points of the model are worth noting:

All paths are included in the figure. There is no return path. Our model is thus recursive.

The only exogenous variable is top management commitment and support.

The conceptual model is made up of nine endogenous variables. Each endogenousvariable is explained by one or more variables plus an error term. One endogenousvariable can influence another endogenous variable.

The first variable is not explained by any other variable in the model.

In the path analysis approach “e” refers to “lost causes” or “causes outside the model.”

Linear relations between the variables are additive.

Each variable is taken to be in standard form; that is, if Vi is the ith variable as measured,then Xi ¼ ðV i 2 �VÞ=svi: The same convention holds for the residuals.

Corresponding authorLassaad Lakhal can be contacted at: lassaad_lakhal@yahoo.com

IJQRM23,6

646

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.