The current issue and full text archive of this journal is available at www. emeraldinsight. com/0263-5577. htm IMDS 111,6 Effect of IT and quality management on performance ? ? ? Cristobal Sanchez-Rodr? guez School of Administrative Studies, York University, Toronto, Canada, and 830 Received 21 January 2011 Revised 3 March 2011 Accepted 3 March 2011 ? Angel Rafael Mart? nez-Lorente Facultad de Ciencias de la Empresa, ? Universidad Politecnica de Cartagena, Murcia, Spain Abstract
Purpose – The present study aims to draw on operations management and information technology literature to examine the effect of three information technology resources (electronic data interchange (EDI), computer-aided design and manufacturing (CAD/CAM), and enterprise resource planning (ERP) systems) and three related quality management capabilities (customer and supplier relations, product and process management, and quality data and workforce management) and their effect on a ? m’s quality performance. Design/methodology/approach – Hypotheses derived from the key features of quality management and information technology presented by previous authors are tested using structural equation modeling through ? eld research on a sample of 229 manufacturing companies in Spain. Findings – Findings from this study indicate that there is signi? ant evidence to support the hypothesized model in which information technology resources (EDI, ERP systems, and CAD/CAM systems) have a direct impact on related quality management capabilities (customer and supplier relations, product and process management, and quality data and workforce management) as well as an indirect impact on quality performance mediated through quality management capabilities. Originality/value – The discrepant ? dings in the literature suggest the need to identify contingencies that may govern the IT-performance relationship. This study focuses on the interplay between information technology, quality management, and quality performance. Keywords Information technology, Quality management, Survey research, Structural equations, Spain, Company performance Paper type Research paper Industrial Management & Data Systems Vol. 111 No. 6, 2011 pp. 830-848 q Emerald Group Publishing Limited 0263-5577 DOI 10. 1108/02635571111144937 1.
Introduction Quality has been typically regarded as a key strategic component of competitive advantage and the enhancement of product quality is still a matter of prime concern for today’s ? rms (Soltani et al. , 2011; Li et al. , 2011). Moreover, a frequent concern is that product quality no longer provides enduring competitive advantage but instead it may have become primarily a competitive prerequisite (Dunk, 2002). Hence, the assessment of how information technology (IT) can lead to improvements in quality performance (QP) is likely to be of considerable interest to both practitioners and academics.
The IT-performance relationship has received considerable attention in the IT literature and there is a common agreement that the adoption of a particular technology often does not provide a sustained competitive advantage for the adopting ? rms because it can be easily duplicated by other ? rms (Powell and Dent-Micalef, 1997). Consequently, the IT literature has suggested the need to identify contingencies that may govern the IT-performance relationship (Das et al. , 2000; Cagliano and Spina, 2000) and to uncover which factors are synergistic with which types of IT and in what contexts (Melville et al. 2004). Given that much of the attention that IT has received in the operations literature today is due to the diffusion of the total quality management (TQM) principles (Gunasekaran and Ngai, 2004) and that quality management is also one of the most important management philosophies directed towards improving QP, this paper will focus on the mediating role of quality management in the relationship between IT and QP. For the purposes of this study, quality management is de? ned as a “set of mutually reinforcing principles, each of which is supported by a set of practices and techniques” (Dean and Bowen, 1994).
Arguments for the relationship between IT and quality management can be found in the resource-based view of the ? rm (Peteraf, 1993; Barney, 1991, 1986), and the notion of resource complementarity. Complementarity represents an enhancement of resource value, and arises when a resource produces greater returns in the presence of another resource than it does alone. Thus, we argue that IT and quality management are complementary resources and that makes IT have a positive effect on QP. Consequently, this paper will try to answer the following research questions: are IT and quality management complementary resources?
In other words, does quality management play a signi? cant role in the relationship between IT and QP? In order to respond to these questions we hypothesize a research model linking IT, quality management and QP. The research model is then tested using structural equation modeling (SEM) and survey data from 229 manufacturing ? rms in Spain. The rest of this paper is organized as follows: we begin with review of the resource-based view related to IT and quality management then, a framework that links IT to quality management and QP is presented.
This is followed by a discussion of the survey methodology, empirical ? ndings, managerial implications, and limitations. 2. Literature review and theoretical background Previous literature has devoted valuable interest to the relationship between IT and quality management studying such issues as how speci? c IT applications impact various aspects of quality management (Kock and McQueen, 1997), the role of IT in a quality management system (Dewhurst et al. , 2003; Forza, 1995); and the development of measurement instruments to assess the level of IT use to support quality management ? (Ang et al. , 2001; Martinez-Lorente et al. , 2004; Sanchez-Rodr? guez et al. , 2006). However, some authors have suggested that technology as an external driver of TQM still needs more studies (McAdam and Henderson, 2004). Arguments for the value of IT to support quality management capabilities ? nd a basis in the resource-based view of the ? rm (Peteraf, 1993; Barney, 1991, 1986), which argues that, to confer competitive advantage, an organization should acquire or develop resources and/or capabilities that contribute positively to performance, are not possessed by all competing ? ms, and are dif? cult to imitate or duplicate (Barney, 1986). These resources and capabilities can either be acquired in factor markets and/or developed inside the ? rm. IT, as part of a ? rm’s resource portfolio, may not meet the resource-based view criteria when acting alone. Owing to the relatively low barriers to imitation and acquisition by other ? rms, an IT-based advantage tends to diminish fairly quickly. In contrast, the resource-based view has emphasized sustainability protected Effect of IT and quality management 831 IMDS 111,6 832 by resource embeddedness, i. e. esource complementarity and co-specialization (Powell and Dent-Micalef, 1997). As mentioned earlier, complementarity represents an enhancement of resource value, and arises when a resource produces greater returns in the presence of another resource than it does alone. Based on this de? nition of resource complementarity, one could argue that quality management and IT are complementary resources. Previous research supports this view. For example, Schniederjans and Kim (2003) concluded that ? rms implementing both enterprise resource planning (ERP) and TQM would achieve predominant success.
Laframboise and Reyes (2005) found that ERP implementation positively affects a ? rm’s performance when the enterprise information system implementation directly interacts with quality improvement systems. Mjema et al. (2005) showed that the introduction of IT on quality management has contributed greatly to the enhancement of quality awareness in the improvement of product quality and in the reduction of quality costs. And Brah and Lim (2006) found that TQM and technology play important and complementing roles in improving performance.
Their analysis showed that both high technology ? rms and high technology TQM ? rms perform signi? cantly better than their low technology peers. Therefore, we propose that through embedding IT in a ? rm’s quality management efforts, IT can facilitate the development of higher-order organizational capabilities, which are ? rm speci? c and hard to duplicate across organizations. As such, the relationship between IT and performance would be indirect and mediated by quality management as it is portrayed in the research model by the absence of any direct link between IT and QP (Figure 1).
The proposed research model is described next. 3. Research model and hypotheses Quality management has been de? ned in the literature as a “set of mutually reinforcing principles, each of which is supported by a set of practices and techniques” (Dean and Bowen, 1994) and comprising a set of key dimensions (Flynn et al. , 1994; Saraph et al. , 1989) (Table I). One could argue that the combination of these dimensions according to the nature could give rise to a set of quality management capabilities.
Quality management capabilities refer to the ability of an organization to identify, utilize, and assimilate both internal and external resources/information to facilitate the completion of quality management activities in order to develop products and services that satisfy or exceed customer expectations. As such, we could identify three distinct Information technology resources EDI H3 H1 H7 H10 H6 Quality management capabilities Customer/supplier relationships Performance ERP H4 Quality data/ workforce management H12 H13 Quality performance Figure 1. Research model hypotheses H2 H5 CAD/M H9 H8 H11 Product/process management
TQM dimensions Description Customer relationships The needs of customers and consumers and their satisfaction have always to be in the mind of all employees. It is necessary to identify these needs and their level of satisfaction Supplier relationships Quality is a more important factor than price in selecting suppliers. Longterm relationship with suppliers has to be established and the company has to collaborate with suppliers to help improve the quality of products/services Workforce Workforce management has to be guided by the principles of: training, management empowerment of workers and teamwork.
Adequate plans of personnel recruitment and training have to be implemented and workers need the necessary skills to participate in the improvement process Product design process All departments have to participate in the design process and work together to achieve a design that satis? es the requirements of the customer, according to the technical, technological and cost constraints of the company Process ? ow Statistical and non-statistical improvement instruments should be applied as management appropriate. Processes need to be mistake proof.
Self-inspection undertaken using clear work instructions. The process has to be maintained under statistical control Quality information has to be readily available and the information should Quality data and reporting be part of the visible management system. Records about quality indicators have to be kept, including scrap, rework, and cost of quality Effect of IT and quality management 833 Table I. Quality management key dimensions quality management capabilities: customer and supplier relations (CSR), quality data and workforce management (QDWM), and product and process management (PPM).
These three quality management capabilities represent all the important activities involved in quality management and consistent with previous literature ( Jung et al. , 2009). Each of the three dimensions re? ects an ability to perform cross-functional as well as interorganizational activities which are required in quality management. CSR deals with collaborative relations with external stakeholders (customers and suppliers). QDWM main focus is on people and entails the collection and analysis of quality data for decision making and the empowerment of employees through teamwork, training, and recognition.
PPM deals with the design and manufacturing of reliable products that meet and exceed the needs of customers. In the present study, we de? ne IT in terms of adoption and use and for the purposes of this study we identi? ed three ITs that were complementary to the identi? ed quality management capabilities; electronic data interchange (EDI) has been a common technology used in managing the information ? ow with customer and suppliers and still is one of the most widely used technologies among trading partners ( Johnson et al. , 2007).
Computer-aided design (CAD) and computer-aided manufacturing (CAM) are widely used technologies in product design and manufacturing therefore one of the most appropriate technologies to understand the relationship between IT and PPM. ERP systems was also chosen because of its ability to manage multiple areas of a ? rm including sales and procurement, process design, production planning and scheduling, inventory management, quality control and human resource management (Gupta and Kohli, 2006), thus, making it an appropriate IT tool to analyze the relationship between IT and QDWM.
Similar to quality management, QP has been re? ected and measured in various ways in past empirical studies. However, product quality is often used to measure IMDS 111,6 QP because it is often considered to contribute to the development of competitive advantage (Ahire et al. , 1996; Dow et al. , 1999). Thus, we de? ne QP in terms of product quality. 3. 1 EDI, CAD/CAM, and ERP One of the major shortcomings of manufacturing information systems has been their inability to integrate and to enhance different information and related functionalities (Montaldo et al. 2003). In response to this situation, ? rms are adopting ERP systems to be integrated with other company systems. For example, EDI systems tend to be one of the most common systems to be integrated with ERP systems (Themistocleous et al. , 2001). Also, organizations are integrating their CAD and CAM systems with new ERP implementations in order to maintain or gain a competitive advantage (Soliman et al. , 2001). This leads us to propose the following hypotheses: H1. The use of EDI is directly and positively associated with ERP. H2.
The use of computer-aided design and manufacturing (CAD/CAM) systems is directly and positively associated with ERP. 3. 2 IT and quality data and workforce management Quality management requires the feedback of QP indicators for the purpose of continuous improvement. Therefore, appropriate performance data must not only be collected but also communicated which can be facilitated by the use of IT systems (Chang, 2006). Since communication is inherent to quality management, this climate of open, two-way communication has pervasive associated bene? s, affecting the attitude to organizational life for all employees, and promoting employee empowerment, teamwork, motivation, training, and general industrial relations (Cua et al. , 2001; Fok et al. , 2001). As such we would expect all three IT (EDI, ERP, and CAD/CAM) to contribute to QDWM capabilities by collecting and communicating QP and employee empowerment. This discussion leads us to the following hypotheses: H3. EDI is directly and positively associated with QDWM. H4. ERP is directly and positively associated with QDWM. H5. CAD/CAM is directly and positively associated with QDWM. . 3 IT and customer and supplier relations The literature contains early evidence supporting the resource complementarity between EDI and quality management capabilities (Sandelands, 1994). More recently, supply managers have also reported that the integration between quality management and ERP systems is also essential for managing customers and supplier relationships (Foster and Ogden, 2008) allowing a ? rm to integrate major supply chain processes, plan production, logistics and marketing promotions (Overby and Min, 2001). Therefore, we propose the following hypotheses: H6.
EDI is directly and positively associated with CSR. H7. ERP is directly and positively associated with CSR. 834 3. 4 IT and product and process management ERP systems such as SAP R/3 system include functionality to speci? cally support operations activities such as process design, production planning and scheduling, inventory management, and quality control among other (Gupta and Kohli, 2006). The literature is also supportive of ERP and PPM as complementary resources. For example, early survey studies on TQM found evidence of ? rms implementing CAD and CAM technologies along with TQM (Czajkiewicz and Wielicki, 1994).
Jiang and Chiu (2002) demonstrated how CAD and CAM technologies can be used for statistical process control purposes. Madu (2005) also developed a company-wide reliability information system and suggested that it should be integrated within an ERP to manage design and manufacturing quality management tools such as statistical process control, Pareto charts, and failure mode and effect analysis. This discussion leads us to propose the following hypotheses: H8. ERP is directly and positively associated with PPM. H9. CAD/CAM is directly and positively associated with PPM. 3. Quality management capabilities Recent evidence from the quality management literature suggests that data quality and workforce management capabilities are necessary to develop successful relationships with customers (Sila and Ebrahimpour, 2005; Kaynak and Hartley, 2008) and suppliers (Sila and Ebrahimpour, 2005; Kaynak and Hartley, 2008). Similarly, the collection and analysis of QDWM have been suggested as antecedents of effective product design (Sila and Ebrahimpour, 2005; Kaynak and Hartley, 2008) and process control activities (Sila and Ebrahimpour, 2005; Kaynak and Hartley, 2008).
Therefore, the following hypotheses were formulated: H10. QDWM is directly and positively associated with CSR. H11. QDWM is directly and positively associated with PPM. The literature also suggests that close collaborative relationships with suppliers facilitates their involvement in the company’s new product design (Kaynak and Hartley, 2008) and process control activities (Kaynak and Hartley, 2008). Similarly, the involvement of customers in new product development would be facilitated by closer collaborative relationships with customers (Flynn et al. , 1994). Therefore, we propose the following hypothesis: H12.
CSR is directly and positively associated with PPM. 3. 6 Quality management capabilities and quality performance The design and manufacture of products tailored to meet customer requirements should enhance QP (Flynn et al. , 1994). Recent evidence in the literature has found that only product design and process control have a direct effect on QP (Kaynak and Hartley, 2008). On the other hand, employee relations, training, and quality data and reporting, customer focus, and supplier quality management did not have a direct impact on QP but the effect was mediated by product design and process control (Kaynak and Hartley, 2008).
Therefore, we propose the following hypothesis: H13. PPM is directly and positively associated with QP. Effect of IT and quality management 835 IMDS 111,6 4. Research methodology We tested the foregoing hypotheses using data surveyed from manufacturing ? rms implementing quality management. In the following sections we describe the sample and data collection procedures and the validation of measures. 4. 1 Sample and data collection The study utilized a cross-sectional mail survey of a sample of Spanish manufacturing companies drawn from “Fomento de la Produccion” company directory.
About 1,949 respondents were selected from a list of the 3,000 largest manufacturing ? rms. The title of the speci? c respondent sought was primarily quality manager or quality director. The questionnaire was developed in Spanish and was pretested with quality managers from a sample of 14 large Spanish manufacturers. In an effort to increase the response rate, a modi? ed version of Dillman’s (1978) total design method was followed. Survey questionnaires were sent to respondents via ? rst-class mail during the month of October 2001; each survey included a cover letter and postage-paid return envelope.
Two weeks after the initial mailing, reminder postcards were sent to all potential respondents. For those who did not respond a second wave of surveys, cover letters, and postage-paid return envelopes were mailed approximately six weeks after the initial mailing. A cover letter was sent with the questionnaire presenting the objective of the research and provided respondents with the de? nitions of the quality management dimensions included in Table I. The resulting sample included 442 ? rms which resulted in an initial response rate of 22. percent and was comparable to similar studies in the literature (Frohlich and Dixon, 2001). Of those 442 respondents, 52. 9 percent of companies (n ? 234) identi? ed themselves as having adopted a quality management program. Of the 234 cases, ? fteen had incomplete data for the purposes of the data analysis, resulting in a ? nal sample in 229 complete responses yielding a de? nitive 11. 7 percent response rate. In order to assess the validity of the self identi? cation of ? rms as having adopted quality management, companies were asked to report which quality assurance program they had implemented.
ISO-registered organizations would be expected to implement effective TQM practices compared with non-ISO-registered organizations as a result of their orientation towards ISO 9000 (Sila, 2007). As it can be seen in Table II almost all ? rms had implemented at least one quality assurance program among the ISO 9001, ISO 9002, and ISO 14001. This is a good indication that almost all the companies in the sample had a functioning quality management program adding validity to the sample responses.
To test for non-response bias, we compared the responses of early and late waves of returned surveys based on the assumption that the opinions of late respondents Number of ? rms Firms with a quality assurance program (ISO 9001, 9002, 14001, or other)a No quality assurance program Total 214 15 229 Percentage 93. 5 6. 5 100 836 Table II. Firms with quality assurance programs Notes: aISO 9001: 110 ? rms; ISO 9002: 104 ? rms; ISO 14001: 79 ? rms; other: 54 ? rms are representative of the opinions of non-respondents.
We performed t-tests comparing early and late respondents on key demographic variables, namely number of employees and sales volume. We found no signi? cant differences between early and late respondents. This suggests that nonresponse would not likely bias the ? ndings. We used Harman’s one-factor test to address the issue of common method variance. We performed factor analysis on items related to the predictor variables and no general factor was apparent in the unrotated factor structure. Therefore, no common method variance problem was detected.
Most respondents are from the consumer goods manufacturing industry, original equipment manufacturing and parts and components (Table III). Product quality is of key importance for all these three industries granting additional validity to the sample responses. Key informants in the sample consisted of quality managers (70. 5 percent), quality department representatives (10. 5 percent), and plant directors (3. 4 percent). Some 60 percent of the companies in the sample were made up of Spanish-owned ? rms, 21 percent of other European Union (EU) countries, and 19 percent from non-EU countries. . 2 Construct measurement EDI, ERP, and CAD/CAM were measured by using a ? ve-point scale (1 ? no use, 5 ? intensive use) similar to other studies in the literature ( Johnson et al. , 2007). Quality management was also measured using a ? ve-point scale (1 represented “no use” and 5 represented “intensive use”) and asking respondents about the use of quality management in customer relations, supplier relations, employee relations, data quality ? ? and reporting, product design, and process management (Gutierrez and Perez, 2010). QP was measured in terms of defect rate at ? al assembly (Fynes and de Burca, 2005), a ? rm’s product quality relative to its competition (Lo et al. , 2007), and overall plant quality. Respondents were asked to report the comparative position of their ? rm with respect to competitors using a ? ve-point scale, where 1 represented “not competitive at all” and 5 represented “very competitive. ” Table IV shows the descriptive statistics for all the indicators in the study. 5. Analytical procedures The hypotheses were tested using SEM. SEM is a statistical technique that combines elements of both multiple regression and factor analysis.
SEM is often used to specify the phenomenon under study in terms of linkage between constructs and their indicators, and provides the researcher with a straightforward method of dealing with multiple relationships simultaneously while providing statistical ef? ciency. Thus, if the model Manufacturing industry Consumer products Original equipment manufacturer Parts and components Raw materials Capital equipment Energy Total Number 67 57 52 28 21 4 229 Percentage 29. 2 24. 7 22. 8 12. 3 9. 1 1. 8 100. 0 Cumulative percentage 29. 2 53. 9 76. 7 89. 0 98. 2 100. 0 Effect of IT and quality management 837
Table III. Industry sectors IMDS 111,6 Code EDI edi1 edi2 ERP erp1 erp2 CAD/CAM cadm1 cadm2 CSR csr1 csr2 QDWM qdwm1 qdwm2 PPM ppm1 ppm2 QP qp1 qp2 qp3 Construct/item Electronic data interchange EDI with customers/clients EDI with suppliers Enterprise resource planning systems Manufacturing requirements planning ERP for example SAP Computer-aided design and manufacturing CAD CAM Customer and supplier relations Customer relationships Supplier relationships Quality data and workforce management Information analysis Workforce management Product and process management Product design Process ? w management Quality performance Defective rates Product quality Plant QP in the last three years (reverse coded) Mean 3. 127 3. 227 3. 476 2. 921 3. 362 2. 721 3. 664 3. 996 3. 550 3. 467 3. 463 3. 930 3. 891 3. 886 1. 655 SD 1. 161 1. 203 1. 394 1. 412 1. 494 1. 519 0. 846 0. 769 0. 952 0. 948 1. 164 0. 819 0. 823 0. 672 0. 907 Std loads 0. 713 0. 902 0. 911 0. 752 0. 798 0. 615 0. 784 0. 803 0. 868 0. 684 0. 596 0. 712 0. 796 0. 575 20. 666 t-value 9. 946 12. 054 12. 362 10. 532 10. 551 8. 541 12. 474 12. 806 12. 382 9. 964 8. 454 9. 860 11. 196 8. 143 2 9. 452 838 Table IV. Descriptive statistics and measurement model results s correct, we will not reject the hypothesis that the model and observed covariance matrices are equal. A conceptual difference of SEM from regression analysis is that in a regression model the independent variables are themselves correlated (multi-co linearity) but in SEM the interactions amongst these variables are modeled, thus providing a more accurate coef? cients (Dion, 2008). In estimating a structural equations model it is important to determine the minimum sample size required in order to achieve a desired level of statistical power with a given model prior to data collection (McQuitty, 2004).
Although there is no single recommended sample size for SEM, several authors have suggested a sample size above 200 provides suf? cient statistical power for data analysis (Garver and Mentzer, 1999). 5. 1 Measurement model validation The analysis was carried out with LISREL 8. 5 using the maximum-likelihood estimation method. The assumptions of multivariate analysis – normality, linearity, and homoscedasticity – were tested for the variables used in the measurement model and the data showed high kurtosis statistics; thus, normal scores of variables were calculated ? sing PRELIS and these scores were used in the analyses (Joreskog et al. , 2000). A con? rmatory factor analysis was undertaken to address the validity and reliability of the measurement model. Table IV shows the factor loadings and t-values from the measurement model estimation. Multiple ? t criteria were employed to evaluate the measurement model (Hair et al. , 1995) and as it can be seen in Table V, ? t indices indicated an acceptable ? t of the measurement model to the data. Convergent validity addresses whether a set of alternative measures accurately represents the construct of interest and was assessed by reviewing the level
Measurement model Degrees of freedom x2 p-value x 2/DF RMSEA NFI NNFI CFI RMR GFI AGFI 69 89. 75 0. 049 1. 300 0. 036 0. 921 0. 970 0. 980 0. 044 0. 950 0. 913 Structural model 76 115. 66 0. 002 1. 522 0. 048 0. 901 0. 957 0. 965 0. 054 0. 937 0. 900 Recommended values – – . 0. 05 ,3b 0. 05b 0. 90 0. 90 0. 95b 0. 10a 0. 80a 0. 80a Effect of IT and quality management 839 Sources: aChau (1997); bByrne (2001) Table V. Test results of the measurement models and structural model of signi? cance for the factor loadings (Table I). As can be seen from Table I the coef? cients for all indicators were large and signi? ant (t-values . 1. 96; p , 0. 05 two-tailed). Scale reliability provides a measure of the internal homogeneity of the items comprising a scale and was calculated, as in Hair et al. (1995), by: A2 factor loading AP A2 AP A factor loading ? error variances The values for composite reliabilities of all scales exceed the threshold value of equal to or greater than 0. 60 (Bagozzi and Yi, 1988; Table III). Discriminant validity among the latent variables and their associated measurement variables can be assessed by ? xing (i. e. constraining) the correlation between pairs of constructs to 1. 0, re-estimating the modi? d model, and measuring the change in the x 2-statistic. The condition of discriminant validity is met if the difference of the x 2-statistics between the constrained and standard models is signi? cant (1 degree of freedom. ) The x 2 difference tests indicated that discriminant validity exists among all the constructs ( p , 0. 05. ) Table VI also reports correlations between the three IT resources, quality management capabilities, and QP. EDI EDI ERP CAD/CAM QDWM CSR PPM QP 0. 79 ; 0. 66 0. 249c, * 0. 500 * 0. 294 * 0. 385 * 0. 275 * 0. 081 a b AP ERP 0. 82; 0. 70 0. 471 * 0. 324 * 0. 316 * 0. 450 * 0. 064 CADM QDWM CSR PPM
QP 0. 67; 0. 51 0. 258 * 0. 117 0. 454 * 20. 053 0. 76; 0. 61 0. 627 * 0. 579 * 0. 368 * 0. 77; 0. 63 0. 737 * 0. 376 * 0. 60; 0. 43 0. 438 * a 0. 72; 0. 47 Notes: n ? 229; signi? cant at: *p , 0. 01 (two-tailed); values on the diagonal are composite reliabilities and bexplained variances; ccorrelations Table VI. Reliability, variance explained, and correlations IMDS 111,6 840 5. 2 Structural model For greater clarity, Figure 2 only includes the values of the structural equations, not the measurement model. The overall ? t for the estimated research model (shown in Figure 2) is shown in Table V. The indices indicated a good ? between the data and the proposed model. The test of hypotheses was based on the direct effects among the constructs as shown in Figure 2 and reported in Table VII. These coef? cients were tested at the signi? cance level p , 0. 05, one-tailed (t-value ? 1. 65). 5. 2. 1 Direct effects. According to the results shown in Figure 2, the path coef? cient from EDI to ERP was not signi? cant, thus, H1 was rejected (t ? 0. 011; p . 0. 10). In contrast, the results provided empirical support for H2 (t ? 4. 447; p , 0. 01), indicating that the adoption of CAD/CAM systems is positively associated with the adoption and use of ERP systems.
H3 and H4 were also supported (t ? 2. 245; p , 0. 01), thus, EDI and ERP systems directly support QDWM. However, the path between CAD/CAM and QDWM was not signi? cant (t ? 0. 158; p . 0. 10) leading to the rejection of H5. The results also showed that the path between EDI and CSR was positive and signi? cant (t ? 2. 231; p , 0. 01) but the path from ERP to CSR was not signi? cant (t ? 1. 111; p . 0. 10) suggesting the acceptance of H6 and rejection of H7 (t ? 1. 040; p . 0. 10). Similarly, ERP did not have a signi? cant positive direct effect on PPM (t ? 1. 040; p . 0. 10) but CAD/CAM showed a signi? ant positive direct effect on PPM (t ? 1. 865; p , 0. 05), thus rejecting H8 and accepting H9. H10 was supported (t ? 5. 308; p , 0. 01) thus, QDWM positively contributes to CSR. However, H11 was not supported (t ? 1. 361; p . 0. 10) indicating that QDWM does not directly support PPM. In contrast, H12 and H13 were supported indicating that CSR supports PPM (t ? 4. 683; p , 0. 01) and that PPM displays a signi? cant effect on QP (t ? 4. 668; p , 0. 01). 5. 2. 2 Indirect effects. We executed the effects analysis procedure in LISREL in order to examine the indirect and total effects within the model.
On the whole, the results indicate that EDI, CAD/CAM, and ERP have positive indirect effects on QP (t ? 2. 584, p , 0. 01; t ? 2. 772, p , 0. 01; and t ? 2. 335, p , 0. 01, respectively; Table VII). QDWM and CSR also showed signi? cant indirect positive effects on QP (t ? 2. 335, p , 0. 01 and t ? 2. 335, p , 0. 01, respectively; Table VII). Further, to assess the enabling effect of quality management on the relationships between the IT and QP, two alternative models were estimated. First, the three Information technology resources 0. 173** 0. 219** 0. 011 Quality management capabilities Customer/supplier relationships 0. 88 0. 543** Performance EDI ERP 0. 269** Quality data/ workforce management 0. 569** Quality performance 0. 479** 0. 019 0. 184* 0. 098 0. 153 0. 442** Figure 2. Structural model coef? cients CAD/M Notes: *p < 0. 05, * *p < 0. 01; one tail Product/process management Independent variable EDI CAD/CAM EDI ERP CAD/CAM EDI ERP QDWM ERP CAD/CAM QDWM CSR PPM EDI ERP CADM QDWM CSR Dependent variable ERP ERP QDWM QDWM QDWM CSR CSR CSR PPM PPM PPM PPM QP QP QP QP QP QP Std direct effect 0. 011 0. 479 * * 0. 219 * * 0. 269 * * 0. 019 0. 173 * * 0. 088 0. 543 * * 0. 098 0. 184 * 0. 153 0. 569 * * 0. 42 * * – – – – – Std indirect effect – – 0. 003 – 0. 129 * * 0. 122 * * 0. 146 * * – 0. 174 * * 0. 139 * * 0. 309 * * – – 0. 090 * * 0. 120 * * 0. 143 * * 0. 204 * * 0. 251 * * Std total effect 0. 011 0. 479 * * 0. 222 * * 0. 269 * * 0. 148 0. 295 * * 0. 234 * * 0. 543 * * 0. 272 * * 0. 323 * * 0. 462 * * 0. 569 * * 0. 442 * * 0. 090 * * 0. 120 * * 0. 143 * * 0. 204 * * 0. 251 * Hypothesis Conclusion H1 H2 H3 H4 H5 H6 H7 H10 H8 H9 H11 H12 H13 – – – – – Rejected Supported Supported Supported Rejected Supported Rejected Supported Rejected Supported Rejected Supported Supported – – – – –
Effect of IT and quality management 841 Notes: n ? 229; signi? cance at: *p , 0. 05, * *p , 0. 01 (one-tailed) Table VII. Summary of statistically signi? cant standardized effects and hypotheses tests constructs pertaining to quality management were removed and only the direct effects of EDI, ERP, and CAD/CAM on QP were estimated. In this model, the direct effects of IT variables on QP were not signi? cant at the 0. 10 level. Second, the direct effects of EDI, CAD/CAM, and ERP on QP were added to the original model, including the indirect effects, as mediated by quality management.
In this speci? cation, none of the direct effects of the IT variables on performance variables was signi? cant at the 0. 10 level with the exception of CAD/CAM showing a standardized signi? cant negative effect on QP (t ? 2 2. 565; p , 0. 01). This result indicates that in organizations with quality management, the adoption of CAD/CAM technology by itself renders a negative effect on QP, but when integrated into the ? rm’s overall quality management system it renders a positive effect on QP. Four of the hypothesized relationships were non-signi? cant. The structural coef? ient between CAD/CAM and EDI systems was positive but non-signi? cant. The reason for this result could be due to the widespread use of EDI technology among ? rms in the sample, that is, ? rms with high and low levels of ERP use are both using EDI technology intensively. Another non-signi? cant relationship was the direct relationship between CAD/CAM and QDWM. However, there was a positive indirect effect of CAD/CAM on QDWM through ERP (Table VII). This result suggests that those ? rms with a CAD/CAM system integrated with their ERP systems experience a signi? ant positive effect on QDWM. The results also showed that ERP had a direct positive effect on CSR and PPM but non-signi? cant, however the indirect effects of ERP on CSR and PPM were signi? cant. These results might be explained by the use that ? rms make of their ERP systems. Firms in the sample might be using their ERP systems mainly to collect data and disseminate information across the organization rather than supporting speci? c areas of the ? rm IMDS 111,6 842 such as CSR or PPM for which other specialized IT are better suited (e. g. EDI and CAD/CAM, respectively).
The only support that CSR and PPM receive from ERP is the role that the ERP plays in as much as collecting the data and providing information necessary for CSR and PPM. Consequently, the effect of ERP on CSR and PPM is mediated by QDWM. A future study could con? rm these ? ndings. The effect of QDWM on PPM was also in the hypothesized direction but non-signi? cant. This result was unexpected, since QDWM has been shown to have a positive direct effect on PPM. In contrast, the indirect effect of QDWM on PPM was signi? cant suggesting that QDWM contributes to PPM through CSR. . Discussion and practical implications The ? ndings of this study add to literature analyzing the factors that affect the relationship between IT and performance. Speci? cally, it adds to recent literature related to the contingency factors in the relationship between IT and QP (Lee et al. , 2010), by identifying quality management as an effective mediator in the relationship. Therefore, the emphasis on technology alone cannot singularly ensure high performance but it is the fusion of people, business, and technology resources, with the “management difference” (i. e. uality management) producing the critical distinctive advantage. Also the results of this investigation add to previous literature focused on the role of IT ? ? to support quality management (Ang et al. , 2001; Sanchez-Rodr? guez et al. , 2006). Previous studies only referred to the impact of IT on quality management in general terms but did not consider speci? c examples of IT, neither showed the link between these IT and their related quality management dimensions. As such, the results of our study showed that EDI directly supports CSR, ERP directly supports QDWM, and CAD/CAM directly supports PPM.
Also important was the indirect role played by EDI and CAD/CAM in QDWM. These ? ndings supports the view that IT in a quality management system act as a means to get rapid and more accurate information and as a feedback mechanism for the purposes of continuous improvement (Chang, 2006). In addition, this role of IT in quality management has pervasive associated bene? ts, affecting the attitude to organizational life for all employees, and promoting employee empowerment, teamwork, motivation, training, and general industrial relations (Cua et al. , 2001).
Our research also adds to research on ERP systems and quality management (Laframboise and Reyes, 2005; Forslund, 2010; Dezdar and Sulaiman, 2009) and con? rm the complementarity between ERP and quality management. Previous literature has argued that TQM is an appropriate antecessor of ERP adoption (McAdam and Galloway, 2005; Li et al. , 2008) since TQM emphasizes customer satisfaction, top management involvement, and life-long learning, all of which are building blocks of implementing enterprise IT. However, ERP could also be proposed as a predecessor of quality management (Li et al. , 2008).
The ? ndings of our study provide evidence in this direction suggesting that ERP adoption can impact quality management. Speci? cally, the results showed that ERP directly support QDWM, and indirectly CSR and PPM (Table IV. ) Thus, ERP systems provide quality management programs not only with an effective tool to collect and disseminate quality data and information and supporting staff empowerment, but also with a way to facilitate closer collaborative relationships with customers and suppliers, and enable a cross-functional approach to their product design and process control activities.
In addition, the results of our study provide evidence that the integration between ERP and quality management delivers results on QP. Conceptually, previous studies had argued that implementing both ERP and TQM would achieve predominant success (Schniederjans and Kim, 2003) and that ERP implementation positively affects a ? rm’s performance when the enterprise information system implementation directly interacts with quality improvement systems (Laframboise and Reyes, 2005). However, there was little empirical evidence. The ? ndings of our study con? m that ERP has a positive indirect effect on QP mediated by quality management. The results also add to literature on the effect of TQM on performance (Kumar and Antony, 2008; Sit et al. , 2009) and are in line with ? ndings of recent research (Kaynak and Hartley, 2008) indicating that QDWM and CSR do not have a direct effect on performance but their effect is through PPM. This ? nding does not mean that improvements in other quality management areas are irrelevant to QP but on the contrary, they positively contribute to QP indirectly through PPM.
As such, improvements in QDWM are carried through CSR that pass onto PPM to ? nally impact QP. Thus, these results con? rm that establishing an effective system for accumulating and disseminating data regarding customers’ requirements and feedback throughout the organization is crucial to improving product design and process management and, ultimately, performance (Kaynak and Hartley, 2008). The ? ndings of this research also offer some support to the literature on the role of quality management in knowledge creation (Linderman et al. 2004; Tan et al. , 2003; Moreno et al. , 2005). From a resourced-based view perspective, knowledge creation can be seen as part of the process to develop organizational resources and capabilities that are dif? cult to imitate by competitors. In this context, IT has a key role to play in this knowledge creation process as a key facilitator of organizational memory and the ability to capture and integrate explicit knowledge by making it easy to codify, communicate, assimilate, store, and retrieve.
Although this study does not measure the extent of knowledge creation in quality management, it shows how IT, a supporting factor of this knowledge creation, has a positive effect on quality management and QP. Therefore, within the limitations of this study, we could argue that IT allows organizations with quality management programs to be able to better manage their quality related knowledge, and that this relationship produces results on QP. The results of this study have also practical implications for managers. The results of this study can serve as evidence to management in ? ms with quality management programs that investment in IT pays off in increased QP when integrated with quality management efforts. Consequently, new investments in IT should be aligned with quality management and a cross-functional approach to IT selection where the voice of quality management is represented might be advisable. In addition, ? rms that are initiating the implementation of a quality management program or interested in advancing their existing one could do so by investing in new IT along the guidelines discussed here. There are also implications for ? rms with no quality management programs.
The results provide evidence that IT and quality management are complementary resources and consequently ? rms looking into obtaining further results from their investments in IT could do so by adopting quality management practices. For example, a ? rm with CAD/CAM technology could further improve QP by relating this technology with process design (e. g. Taguchi methods) and process control tools (e. g. statistical process control). Effect of IT and quality management 843 IMDS 111,6 844 7. Conclusions The main objective of this paper was to deepen our understanding of the relationship between ITs, quality management capabilities, and QP.
Using the resource-based view of the ? rm and data collected from 219 manufacturing ? rms we found that quality management capabilities (CSR, PPM, and QDWM) can help realize the bene? ts of IT (EDI, CAD/CAM, and ERP) and gain performance advantages. Therefore, the results of this study support the argument that IT and quality management are complementary resources and that the emphasis on technology alone cannot singularly ensure high performance. It is the fusion of people, business, and technology resources, with the “management difference” (i. e. uality management) producing the critical distinctive advantage. From a theoretical point of view this research has provided evidence that supports the existence of a positive effect of IT on QP. Nonetheless, the results suggest that this effect is produced as long as these IT are used to better implement or support a series of quality management capabilities. The fact that IT was related to quality management and QP indicates that investment in IT should be taken into consideration in the literature about quality management as a facilitator and in the IT literature as a mediator on performance.
At this point, it is important to acknowledge important limitations of our study that might provide opportunities for future research. Though the constructs developed in this study exhibit acceptable reliability for the purposes at hand, future research should re? ne them and consider adding new indicators. Also, inferences in this study are based on cross-sectional data which make causal claims dif? cult; a longitudinal study could help solve this problem. The study framework was tested primarily with a single informant from each organization.
A dataset with multiple informants from each organization can enhance the validity of the ? ndings. This study used perceived measures of QP by managers. Future work can attempt to blend objective data in an effort to validate the ? ndings. Also the data were collected in 2001 and the picture of the use of IT and quality management in ? rms is likely to have changed since the collection of the data. Despite these limitations, this study paves the way for researchers and managers to more fully capitalize on the potential of the integration between IT and quality management to foster a ? m’s QP and competitive position. References Ahire, S. , Golhar, D. and Waller, M. (1996), “Development and validation of TQM implementation constructs”, Decision Sciences, Vol. 27 No. 1, pp. 23-56. Ang, C. , Davies, M. and Finlay, P. (2001), “An empirical study of the use of information technology to support total quality management”, Total Quality Management, Vol. 12 No. 2, pp. 145-57. Bagozzi, R. and Yi, Y. (1988), “On the evaluation of structural equation models”, Academy of Marketing Science, Vol. 6 No. 1, pp. 74-93. Barney, J. 1986), “Strategic factor markets: expectation, luck, and business strategy”, Management Science, Vol. 32 No. 10, pp. 1231-41. Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, Vol. 17 No. 1, pp. 99-120. Brah, S. A. and Lim, H. Y. (2006), “The effects of technology and TQM on the performance of logistics companies”, International Journal of Physical Distribution & Logistics Management, Vol. 36 No. 3, pp. 192-209. Byrne, B. M. (2001), Structural Equation Modeling with AMOS: Basic Concept, Applications, and Programming, Lawrence Erlbaum Associates, Mahwah, NJ.
Cagliano, R. and Spina, G. (2000), “Advanced manufacturing technologies and strategically ? exible production”, Journal of Operations Management, Vol. 18 No. 2, pp. 169-90. Chang, H. H. (2006), “An empirical evaluation of performance measurement systems for total quality management”, Total Quality Management & Business Excellence, Vol. 17 No. 8, pp. 1093-109. Chau, P. (1997), “Reexamining a model for evaluating information center success using a structural equation modelling approach”, Decision Sciences, Vol. 28 No. 2, pp. 309-34. Cua, K. O. , McKone, K. E. and Schroeder, R. G. 2001), “Relationships between implementation of TQM, JIT, and TPM and manufacturing performance”, Journal of Operations Management, Vol. 19 No. 6, pp. 675-94. Czajkiewicz, Z. J. and Wielicki, T. R. (1994), “CIM – a journey to manufacturing excellence”, Computers & Industrial Engineering, Vol. 27 Nos 1-4, pp. 91-4. Das, A. , Hand? eld, R. B. , Calantone, R. J. and Ghosh, S. (2000), “A contingent view of quality management – the impact of international competition on quality”, Decision Sciences, Vol. 31 No. 3, pp. 649-90. Dean, J. and Bowen, D. (1994), “Management theory and total quality: mproving research and practice through theory development”, Academy of Management Review, Vol. 19 No. 3, pp. 392-418. ? ? Dewhurst, F. W. , Martinez-Lorente, A. R. and Sanchez-Rodr? guez, C. (2003), “An initial assessment of the in? uence of IT on TQM: a multiple case study”, International Journal of Operations & Production Management, Vol. 23 No. 4, pp. 348-74. Dezdar, S. and Sulaiman, A. (2009), “Successful enterprise resource planning implementation: taxonomy of critical factors”, Industrial Management & Data Systems, Vol. 109 No. 8, pp. 1037-62. Dillman, D. 1978), Mail and Telephone Surveys: The Total Design Method, Wiley, New York, NY. Dion, P. (2008), “Interpreting structural equation modeling results: a reply to Martin and Cullen”, Journal of Business Ethics, Vol. 83 No. 3, pp. 365-8. Dow, D. , Samson, D. and Ford, S. (1999), “Exploding the myth: do all quality management practices contribute to superior quality performance? ”, Production and Operations Management, Vol. 8 No. 1, pp. 1-27. Dunk, A. S. (2002), “Product quality, environmental accounting and quality performance”, Accounting, Auditing & Accountability Journal, Vol. 5 No. 5, p. 719. Flynn, B. , Sakakibara, S. and Schroeder, R. (1994), “A framework for quality management research and an associated measurement instrument”, Journal of Operations Management, Vol. 11 No. 4, pp. 339-66. Fok, L. Y. , Fok, W. M. and Hartman, S. J. (2001), “Exploring the relationship between total quality management and information system development”, Information & Management, Vol. 38 No. 6, pp. 355-71. Forslund, H. (2010), “ERP systems’ capabilities for supply chain performance management”, Industrial Management & Data Systems, Vol. 110 No. 3, pp. 351-67.
Forza, C. (1995), “The impact of information systems on quality performance: an empirical study”, Journal of Operations & Production Management, Vol. 15 No. 6, pp. 69-83. Foster, S. T. and Ogden, J. (2008), “On differences in how operations and supply chain managers approach quality management”, International Journal of Production Research, Vol. 46 No. 24, pp. 6945-61. Frohlich, M. T. and Dixon, J. R. (2001), “A taxonomy of manufacturing strategies revisited”, Journal of Operations Management, Vol. 5 No. 19, pp. 541-58. Effect of IT and quality management 845 IMDS 111,6 46 Fynes, B. and de Burca, S. (2005), “The effects of design quality on quality performance”, International Journal of Production Economics, Vol. 96 No. 1, pp. 1-14. Garver, M. S. and Mentzer, J. T. (1999), “Logistics research methods: employing structural equation modeling to test for construct validity”, Journal of Business Logistics, Vol. 20 No. 1, pp. 33-57. Gunasekaran, A. and Ngai, E. W. T. (2004), “Information systems in supply chain integration and management”, European Journal of Operational Research, Vol. 159 No. 2, pp. 269-95. Gupta, M. and Kohli, A. 2006), “Enterprise resource planning systems and its implications for operations function”, Technovation, Vol. 26 No. 5, pp. 687-96. ? ? Gutierrez, L. J. G. and Perez, V. F. (2010), “Managerial networks and strategic ? exibility: a QM perspective”, Industrial Management & Data Systems, Vol. 110 No. 8, p. 1192. Hair, J. , Anderson, R. , Tatham, R. and Black, W. (1995), Multivariate Data Analysis with Readings, Prentice-Hall, Englewood Cliffs, NJ. Jiang, B. C. and Chiu, S. (2002), “Form tolerance-based measurement points determination with CMM”, Journal of Intelligent Manufacturing, Vol. 13 No. 2, pp. 101-8.
Johnson, P. F. , Klassen, R. D. , Leenders, M. R. and Awaysheh, A. (2007), “Utilizing e-business technologies in supply chains: the impact of ? rm characteristics and teams”, Journal of Operations Management, Vol. 25 No. 6, pp. 1255-74. ? ? Joreskog, K. , Sorbom, D. , du Toit, S. and du Toit, M. (2000), LISREL 8: New Statistical Features, Scienti? c Software International, Lincolnwood, IL. Jung, J. Y. , Wang, Y. J. and Wu, S. (2009), “Competitive strategy, TQM practice, and continuous improvement of international project management”, The International Journal of Quality & Reliability Management, Vol. 6 No. 2, pp. 164-83. Kaynak, H. and Hartley, J. L. (2008), “A replication and extension of quality management into the supply chain”, Journal of Operations Management, Vol. 26 No. 4, pp. 468-89. Kock, N. F. J. and McQueen, R. J. (1997), “Using groupware in quality management programs”, Information Systems Management, Vol. 14 No. 2, pp. 56-62. Kumar, M. and Antony, J. (2008), “Comparing the quality management practices in UK SMEs”, Industrial Management & Data Systems, Vol. 108 No. 9, pp. 1153-66. Laframboise, K. and Reyes, F. (2005), “Gaining ompetitive advantage from integrating enterprise resource planning and total quality management”, Journal of Supply Chain Management, Vol. 41 No. 3, pp. 49-64. Lee, C. , Huang, S. , Barnes, F. and Kao, L. (2010), “Business performance and customer relationship management: the effect of IT, organisational contingency and business process on taiwanese manufacturers”, Total Quality Management & Business Excellence, Vol. 21 No. 1, pp. 43-65. Li, L. , Markowski, C. , Xu, L. and Markowski, E. (2008), “TQM – a predecessor of ERP implementation”, International Journal of Production Economics, Vol. 15 No. 2, pp. 569-80. Li, L. , Su, Q. and Chen, X. (2011), “Ensuring supply chain quality performance through applying the SCOR model”, International Journal of Production Research, Vol. 49 No. 1, pp. 33-57. Linderman, K. , Schroeder, R. , Zaheer, S. , Liedtke, C. and Choo, A. S. (2004), “Integrating quality management practices with knowledge creation processes”, Journal of Operations Management, Vol. 22 No. 6, pp. 589-607. Lo, V. H. Y. , Yeung, A. H. W. and Yeung, A. C. L. (2007), “How supply quality management improves an organization’s quality performance: a study of Chinese manufacturing ? ms”, International Journal of Production Research, Vol. 45 No. 10, pp. 2219-43. McAdam, R. and Galloway, A. (2005), “Enterprise resource planning and organisational innovation: a management perspective”, Industrial Management & Data Systems, Vol. 105 No. 3, pp. 280-91. McAdam, R. and Henderson, J. (2004), “In? uencing the future of TQM: internal and external driving factors”, The International Journal of Quality & Reliability Management, Vol. 21 No. 1, pp. 51-72. McQuitty, S. (2004), “Statistical power and structural equation models in business research”, Journal of Business Research, Vol. 7 No. 2, pp. 175-83. Madu, C. N. (2005), “Strategic value of reliability and maintainability management”, The International Journal of Quality & Reliability Management, Vol. 22 No. 3, pp. 317-29. Martinez-Lorente, A. R. , Sanchez-Rodriguez, C. and Dewhurst, F. W. (2004), “The effect of information technolgies on TQM: an initial analysis”, International Journal of Production Economics, Vol. 89 No. 1, pp. 77-93. Melville, N. , Kraemer, K. and Gurbaxani, V. (2004), “Review: information technology and organizational performance: an integrative model of it business value”, MIS Quarterly, Vol. 8 No. 2, pp. 283-322. Mjema, E. A. M. , Victor, M. A. M. and Mwinuka, M. S. M. (2005), “Analysis of roles of IT on quality management”, The TQM Magazine, Vol. 17 No. 4, pp. 364-75. Montaldo, E. , Sacile, R. and Boccalatte, A. (2003), “Enhancing work? ow management in the manufacturing information system of a small-medium enterprise: an agent-based approach”, Information Systems Frontiers, Vol. 5 No. 2, pp. 195-205. Moreno, A. R. , Morales, V. G. and Montes, F. J. L. (2005), “Learning during the quality management process: antecedents and effects in service ? ms”, Industrial Management & Data Systems, Vol. 105 No. 8, pp. 1001-22. Overby, J. W. and Min, S. (2001), “International supply chain management in an internet environment”, International Marketing Review, Vol. 18 No. 4, pp. 392-420. Peteraf, M. A. (1993), “The cornerstones of competitive advantage: a resource-based view”, Strategic Management Journal, Vol. 14 No. 3, pp. 179-92. Powell, T. C. and Dent-Micalef, A. (1997), “Information technology as competitive advantage: the role of human, business, and technology resources”, Strategic Management Journal, Vol. 18 No. 5, pp. 375-405. ? ? Sanchez-Rodr? guez, C. , Dewhurst, F. W. and Mart? nez-Lorente, A. R. (2006), “IT use in supporting TQM initiatives: an empirical investigation”, International Journal of Operations & Production Management, Vol. 26 No. 5, pp. 486-504. Sandelands, E. (1994), “Du pont’s integrated vision”, International Journal of Physical Distribution & Logistics Management, Vol. 24 No. 3, pp. 31-3. Saraph, J. , Benson, P. and Schroeder, R. (1989), “An instrument for measuring the critical factors of quality management”, Decision Sciences, Vol. 20 No. 4, pp. 810-29. Schniederjans, M. J. and Kim, G. C. 2003), “Implementing enterprise resource planning systems with total quality control and business process reengineering: survey results”, International Journal of Operations & Production Management, Vol. 23 Nos 3/4, pp. 418-30. Sila, I. (2007), “Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: an empirical study”, Journal of Operations Management, Vol. 25 No. 1, pp. 83-109. Sila, I. and Ebrahimpour, M. (2005), “Critical linkages among TQM factors and business results”, International Journal of Operations & Production Management, Vol. 5 No. 11, pp. 1123-56. Effect of IT and quality management 847 IMDS 111,6 848 Sit, W. , Ooi, K. , Lin, B. and Chong, A. Y. (2009), “TQM and customer satisfaction in malaysia’s service sector”, Industrial Management & Data Systems, Vol. 109 No. 7, pp. 957-75. Soliman, F. , Clegg, S. and Tantoush, T. (2001), “Critical success factors for integration of CAD/CAM systems with ERP systems”, International Journal of Operations & Production Management, Vol. 21 Nos 5/6, pp. 609-29. Soltani, E. , Azadegan, A. , Liao, Y. and Phillips, P. (2011), “Quality performance in a global supply chain: ? ding out the weak link”, International Journal of Production Research, Vol. 49 No. 1, pp. 269-93. Tan, B. , Lin, C. and Hung, H. (2003), “An ISO 9001 2000 quality information system in e-commerce environment”, Industrial Management & Data Systems, Vol. 103 Nos 8/9, pp. 666-76. Themistocleous, M. , Irani, Z. and O’Keefe, R. M. (2001), “ERP and application integration”, Business Process Management Journal, Vol. 7 No. 3, pp. 195-204. Appendix Table AI. Survey indicators Information technologies To what extent does your company use the following IT? 1 – no use at all to 5 – intensive use) edi1 EDI with customers/clients edi2 EDI with suppliers erp1 Manufacturing requirements planning (MRP) erp2 ERP for example SAP cadm1 Computer-aided design (CAD) cadm2 Computer-aided manufacturing (CAM) Quality management To what extent does your company use quality management in the following : (1 – for no use at all and 5 – for intensive use) qdwm1 Information analysis qdwm2 Workforce management csr1 Customer relationships csr2 Supplier relationships ppm1 Product design ppm2 Process ? w management Quality performance Please indicate how do the following measures at your plant compare to industry competition? : (1 – no competitive and 5 – highly competitive) qp1 Rate of defective units To what extent do you agree with the following statements? : (1 totally disagree and 5 totally agree) qp2 The quality of our products is superior to that of our competitors qp3 The quality performance in our plants in the last three ears has been low compared to that of ? rms in our industry (reverse coded) Corresponding author ? ? ? Cristobal Sanchez-Rodr? guez can be contacted at: sanchezc@yorku. ca To purchase reprints of this article please e-mail: reprints@emeraldinsight. com Or visit our web site for further details: www. emeraldinsight. com/reprints