Critical success factors for TQM implementation

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1741-0401.htm Critical success factors for TQM implementation and their impact on performance of SMEs Salaheldin Ismail Salaheldin Department of Management and Marketing, College of Business and Economics, Qatar University, Doha, Qatar Critical success factors for TQM implementation 215 Received January 2008 Revised March 2008 Accepted March 2008 Abstract Purpose The purpose of this paper is to identify the critical success factors of TQM implementation, to evaluate their impact on the primary measures as expressed by the operational performance and the secondary measures as expressed by the organizational performance, and to find out the effect of the operational performance on the organizational performance of small and medium-sized enterprises (SMEs) in the Qatari industrial sector using the structured equation modeling (SEM) approach. Design/methodology/approach A questionnaire was designed and distributed to 297 SMEs in the Qatari industrial sector. Of the 297 questionnaires posted, a total of 139 were returned and were used to test the theoretical model. In particular, hypotheses were developed to evaluate the impact of TQM implementation on the operational and organizational performance of the SMEs. Findings The empirical analysis demonstrates several key findings: data analysis reveals that there is a substantial positive effect of the TQM implementation on both the operational and the organizational performance. The findings confirm the significant relationship between operational and organizational performances of the SMEs. Overall, the results showed the central role of the strategic factors in the successful implementation of the TQM programs within the SMEs. Research limitations/implications The research is subject to the normal limitations of survey research. The study is using perceptual data provided by production managers or quality managers which may not provide clear measures of performance. However, this can be overcome using multiple methods to collect data in future studies. Interestingly, the findings here may be generalisable outside Qatar, i.e. a similar country to Qatar such as the GCC countries. Practical implications Qatari SMEs should consider TQM as an innovative tool for improving operational and organizational performance in today s dynamic manufacturing environment. The findings suggest the notion that the TQM critical success factors (CSFs) should be implemented holistically rather than on a piecemeal basis to get the full potential of the TQM. Moreover, the study emphasizes the need to link operational performance to organizational performance to achieve the success of TQM implementation. Originality/value The study integrates the CSFs of TQM practices, i.e. strategic, tactical and operational factors, with operational and organizational performances as related drivers of the effectiveness and success of TQM practices in the SMEs. Very few studies have been performed to investigate and understand this issue. Therefore, the research can make a useful contribution. Keywords Total quality management, Critical success factors, Small to medium-sized enterprises, Business performance, Organizational performance, Qatar Paper type Research paper Background of the study With the rapid globalization of the Qatari economy, manufacturing firms are faced with a changing competitive environment. They are competing in creating the conditions that will enable them to be competitive in the domestic and international International Journal of Productivity and Performance Management Vol. 58 No. 3, 2009 pp. 215-237 q Emerald Group Publishing Limited 1741-0401 DOI 10.1108/17410400910938832

IJPPM 58,3 216 markets. Accordingly, all manufacturing firms seek to adopt and implement a set of operations management practices that have been successful elsewhere and that will help them to identify changes in their environment and to respond proactively through continuous improvement (Fassoula, 2006). One form of operations management practices is TQM which has received great attention in the last two decades (Jung and Wang, 2006). Thus far, much have been written on TQM and its value in improving the performance of manufacturing industries in general. Literature on TQM implementation suggests that the TQM practices are positively associated with operational performance (Choi and Eboch, 1998), but they marginally affect organizational performance (Broetzmann et al., 1995). On the other hand, research findings concluded that the success of SMEs has a direct impact on the economic development in both the developed and developing countries (Demirbag et al., 2006). They have the ability to generate employment with minimum cost, are pioneer in innovation realm and have high flexibility which allow them to meet the needs of the customers (Brock and Evans, 1986; Acs and Audretsch, 1990). However, a review of the current literature on TQM practices indicated that much have been written about TQM implementation in large manufacturing companies, but little attention has been paid to their implementation in the SMEs (Rahman, 2001; Petroni, 2002; Seth and Tripathi, 2005). In a similar vein, there is a dearth of literature regarding the impact of TQM implementation on performance of SMEs, despite the potential synergies between the two areas (Demirbag et al., 2006; Sila, 2007). Furthermore, it has been pointed out about the lack of consistency in research in Quality Measurement (QM) area due to the absence of standard and universally acceptable measurement instrument. More importantly, most of previous studies have been done on the impact of TQM practices on performance of the SMEs in Europe, USA and the Far East (Rahman, 2001; Petroni, 2002; Seth and Tripathi, 2005; Demirbag et al., 2006; Sila, 2007). In contrast, few, if any, previous writers have analyzed TQM- performance relationships of SMEs in emerging market economies (Koh et al., 2007). In the same line, Pun and Gill (2002) reported in their study that there is a consensus regarding the versatility of TQM implementation. Thus, there is a stringent necessity to provide a model that amalgamates TQM enablers with TQM effectiveness and TQM success. Importance of the study There are many empirical studies which examine TQM practices-performance relationships in large firms (Powell, 1995; Ahire and Golhar, 1996; Motwani, 2001; Montes et al., 2003; Brah and Lim, 2006; Kapuge and Smith, 2007). In contrast to most previous studies found in the literature, this research examined these relationships in a different way. Specifically, this study adopted a more comprehensive approach than previous studies to investigate the effects of TQM practices on performance in the SMEs. In other words, this study has a wider coverage of the key TQM success factors, i.e. strategic, tactical and operational factors. It also adopted the primary measures as expressed by the operational performance as the key mediating variables that comprise TQM effectiveness, all of which might have an impact on TQM CSFs organizational performance relationships.

More importantly, the study offers an added factor to be taken into consideration, particularly when examining the effect of the operational performance on the organizational performance as expressed by the financial and non-financial measures. This study offers a theoretical model that can be considered as a step forward in developing an integrated model toward investigating the relationship between TQM CSFs, TQM effectiveness as expressed by the operational performance and TQM success as expressed by the organizational performance and might serve as a basis for future research. Most of previous research on TQM practices have been done in developed countries. The present study contributes by comparing TQM practices and their impact on the operational and organizational performances in the SMEs of developed and developing countries. Finally, this research adds to the body of knowledge by providing new data and empirical insights into the relationship between the CSFs of TQM practices and operational and organizational performances of SMEs in Qatar. Thus, based on the analysis of past reseach, the purpose of this paper is threefold: (1) To identify the CSFs of TQM practices of the SMEs in the Qatari industrial sector; (2) To evaluate the impact of the TQM CSFs on the operational and the organizational performances of the SMEs; and (3) To examine the effects of the operational performance (primary measures) on the organizational performance (secondary measures). Critical success factors for TQM implementation 217 Model and hypotheses The conceptual model of the current study is drawn from two streams of research, i.e. operations management literature and organizational performance literature. Figure 1 illustrates the conceptual model with the hypothesized relationships between the constructs. These relationships deal with three sets of hypotheses: Figure 1. Proposed model for the effects of TQM practices on performance

IJPPM 58,3 218 (1) The effects of the TQM CSFs on the operational performance (primary measures). (2) The relationship between the TQM CSFs and the organizational performance (secondary measures). (3) The impact of the primary measures (as expressed by the operational performance) on the secondary measures (as expressed by the organizational performance). The next section provides a brief definition for each construct, i.e. TQM CSFs and performance measures followed by the development of the hypotheses. Critical success factors of TQM practices in the SMEs Generally speaking, the CSFs can be defined as the critical areas which organization must accomplish to achieve its mission by examination and categorization of their impacts (Oakland, 1995). Thus, in the current study they can be viewed as those things that must go right in order to ensure the successful implementation of TQM. On the other hand, the review of the literature suggested that there are numerous CSFs that can be identified as being crucial to the successful implementation of TQM (also referred to as contributing variables or critical factors or enablers in the literature). One of the earlier empirical studies in the quality management area that analyzed the TQM CSFs in the SMEs was conducted by Yusof and Aspinwall (2000). This study found that the CSFs for TQM implementation in the SMEs are management leadership, continuous improvement system, measurement and feedback, improvement tools and techniques, supplier quality assurance, human resource development, systems and processes, resources, education and training, and work environment and culture. More importantly, Hodgetts et al. (1999) found that the CSFs of TQM implementation in the SMEs are top management involvement, customer focus, employees training, employees empowerment and generating new ideas. In this line of work, a study by Dayton (2003) used data from American industrial companies to determine whether the ten TQM critical factors (i.e. people and customer management, supplier partnerships, communications, customer satisfaction, external interface management, strategic quality management, teamwork structures for improvement, operational quality planning and quality improvement systems) identified by the Black and Porter (1996) study could be considered as important TQM CSFs by USA small and large companies. From his conclusion he identified the strategic quality management as the most important TQM critical factor. The empirical findings from Rahman s (2001) study of 53 Australian SMEs found that the critical factors of the successful implementation of TQM are leadership, strategy and planning, employee empowerment and employee involvement, employee training and development, information and analysis and customer management. Demirbag et al. (2006) carried out an empirical study to identify factors critical to the success of TQM in the Turkish SMEs. They concluded that there are seven CSFs of TQM practices, i.e. quality data and reporting, role of top management, employee relations, supplier quality management, training, quality policy and process management.

However, in contrast to the previous studies, organization culture was used as a separate variable in the current study since an organization s culture affects behaviors and attitudes at all levels and it determines, to a large extent, how employees act (Robbins and DeCenzo, 2005). In addition, the literature review undertaken revealed a lack of research with regard to some critical factors of TQM implementation (e.g. employees satisfaction, product design and building teams and solving problems), and this could be due to the fact that these factors are related to any new managerial approach such as JIT, MRPII and ERP, not necessarily to TQM only. Consequently, the current research proposes a holistic framework for TQM implementation based on an extensive review of the factors that contribute to the success TQM implementation. Critical success factors for TQM implementation 219 Performance measures Generally speaking, performance is defined as the degree to which an operation fulfills the performance objectives primary measures in order to meet the needs of the customers secondary measures (Slack et al., 2001). Performance measurement is a critical factor for the effective management. This may stem back from the fact that without measuring something, it is difficult to improve it. Therefore, improving the organizational performance requires identifying and measuring the impact of TQM practices on it (Demirbag et al., 2006; Koh et al., 2007). Several empirical studies have been conducted to establish the link between TQM practices and organizational performance (e.g. Sterman et al., 1997; Choi and Eboch, 1998; Easton and Jarrell, 1998; Samson and Terziovski, 1999; Brah et al., 2002; Brah and Lim, 2006; Demirbag et al., 2006; Feng et al., 2006). The results of these studies indicated that there are various measures, i.e. organizational performance, corporate performance, business performance, operational performance, financial and non-financial performance, innovation performance, and quality performance. In a similar vein, Ramamurthy (1995); Beaumont et al. (2002); Brah et al. (2002); and Koh et al. (2007) measured performance in two dimensions: operational performance and organizational performance. Operational performance reflects the performance of internal operation of the company in terms of cost and waste reduction, improving the quality of products, improving flexibility, improving delivery performance; and productivity improvement. They are considered as primary measures because they follow directly from the actions taken during the implementation of TQM, while organizational performance measured by financial measures such as revenue growth, net profits, profit to revenue ratio and return on assets, and non-financial measures such as investments in R&D, capacity to develop a competitive profile, new products development, market development and market orientation, are secondary measures because they are a consequence of TQM implementations. Accordingly, performance measures that have been suggested by (Ramamurthy, 1995; Beaumont et al., 2002; Brah et al., 2002; Demirbag et al., 2006; Sila, 2007) are used to measure performance in this research. In addition, the current study makes an attempt to bridge the gap left by earlier studies regarding a lack of attention to safety and waste reduction as performance measures.

IJPPM 58,3 220 Hypotheses formulation Although there are several CSFs related to performance measures, from review of the literature 26 major variables are hypothesized as being significantly related to the organizational and operational performance measures in the Qatari SMEs. The relationship between the TQM CSFs and operational performance There is a common assumption in the literature that the TQM CSFs have a positive impact on the operational performance (Powell, 1995; Ahire and Golhar, 1996; Brah and Lim, 2006; Sila, 2007). They indicated that TQM firms out perform non-tqm firms in operational performance such as improving delivery performance, reduction in production costs, increasing productivity, improving flexibility, reducing scrap and improving the quality of products. To investigate the previous mentioned relationship, the current study makes an attempt to operationalize the CSFs, not only in terms of the importance of each factor, but also in terms of relative importance that is given to each factor. In this way, those factors can be classified as strategic factors. They are broad in nature and impact the long-term effectiveness of the company (Davis et al., 2003), and also they require a significant change in the manner in which the business is conducted (Turban et al., 1999). Moreover, they are dominant factors which play a significant role in the successful implementation of TQM practices. Those factors include; top management commitment, organizational culture, leadership, continuous improvement, quality goals and policy, resources value addition process and benchmarking. So, the following hypothesis is therefore proposed: H1. Strategic factors have a direct and positive effect on operational performance. The second group of factors can be classified as tactical factors. They are of less criticality than strategic factors of TQM implementation. However, these factors are significant to support the latter. More importantly, they impact the methods and actions that help accomplish the expected benefits of TQM implementation. In other words, they affect the decision that are made by middle management (Turban et al., 1999). Those factors include employee empowerment, employee involvement, employee training, team building and problem solving, use of information technology to collect and analyze quality data, supplier quality, supplier relationships, integration with other systems and assessment of performance of suppliers. Therefore, it is hypothesized that: H2. Tactical factors have a direct and positive effect on operational performance. At the other end of the list, i.e. the least important or less critical factors are classified as operational factors. They reflect those factors which produce consequences that will be visible in a short term period. Those factors include product and service design, process control, management of customer relationships, customer orientation, customer and market knowledge, realistic TQM implementation schedule, resources conservation and utilization, inspection and checking work and enterprise performance metrics for TQM. Thus, the following hypothesis is offered: H3. Operational factors have a direct and positive effect on operational performance.

The effects of TQM CSFs on organizational performance The relationships between TQM practices and organizational performance have been addressed in several studies (Motwani, 2001; Montes et al., 2003; Brah and Lim, 2006; Demirbag et al., 2006; Kapuge and Smith, 2007; Sila, 2007). They indicated a positive association between TQM practices and improved performance. In other words, the results of those studies demonstrated the crucial role of TQM practices in enhancing the organizational performance, i.e. financial performance as measured by return on investment and market share growth and non-financial performance as measured by investments in R&D and market orientation. Therefore, we expect: H4. Strategic factors have a positive influence on financial performance. H5. Strategic factors have a positive influence on non- financial performance. H6. Tactical factors have a positive influence on financial performance. H7. Tactical factors have a positive influence on non- financial performance. H8. Operational factors have a positive influence on financial performance. H9. Operational factors have a positive influence on non- financial performance. Critical success factors for TQM implementation 221 The effects of operational performance on organizational performance This study attempts to investigate the effects of the primary measures (as expressed by the operational performance measures) on the secondary measures (as expressed by the organizational performance) (see Figure 1). As emphasized by Brah and Lim (2006), the operational performance has a positive correlation with overall organizational performance. One possible explanation could be due to the success of TQM implementation as measured by operational measures such as producing high quality products, speed of delivery, high flexibility, switching costs, safety, waste reduction, resource conservation and high productivity would lead to success in the secondary measures, i.e. financial and non-financial measures (Brah et al., 2002; Brah and Lim, 2006). This gives rise to the following hypotheses: H10. Operational performance has a strong impact on financial performance. H11. Operational performance has a strong impact on non-financial performance. Hypothetical model The hypotheses presented in the previous section led us to a theoretical model described in Figure 1. The CSFs of TQM practices are factored into the three constructs of strategic, tactical and operational factors. The relationships between the CSFs constructs to the operational and organizational performance constructs were hypothesized. Study design Sample The research design employed in the current study was a postal survey. The term SMEs covers a variety of definitions and measures. In Qatar, SMEs are defined as an industrial undertaking in which the investment in fixed assets in plant and machinery

IJPPM 58,3 222 is less than Q.R 1.5 million ($410,959) for small firms and between Q.R 1.5 million ($410,959) to Q.R 10 million ($2,739,726) for medium firms (Qatar Bank for Industrial Development, 2001). The firms included in the survey were all the SMEs in the Qatari industrial sector. This choice was motivated by the following reasons:. the SMEs represent the backbone of the Qatari economy; they have shown their presence in nearly all sectors of the economy;. they account for 93.6 percent of the manufacturing industrial firms in the country and provides about 70.2 percent of employment in the Qatari industrial sector (Gulf Organization for Industrial Consulting, 2005a, b); and. some of the SMEs are producing directly for customer market while others are serving as suppliers to large firms. Our target population (297 SMEs) was obtained from listings provided by the Gulf Organization for Industrial Consulting and from Industrial Bank databases. These were carefully verified and cross-checked to ensure complete and up-to-date information. A follow-up letter and a telephone call were also utilized to maximize the response rate. All of the firms were contacted personally while 45 refused to be involved in the research quoting confidentiality of data in the questionnaire as a reason. A total of 139 firms thus comprised the final sample which represents a (139/297) 46.8 percent response rate. Hair et al. (2006) pointed out that opinions regarding sample sizes have varied. They further said that most SEM estimation procedure (including the one used in this research) is maximum likelihood estimation (MLE) and they recommended that minimum sample sizes to ensure stable MLE solutions are 100 to 150. Thus, the sample size of 139 is considered as appropriate for this research. The construction of the questionnaire and its appropriateness to the study A personally-administered questionnaire was primarily adopted from earlier studies specifically, the works of Saraph et al. (1989); Brah et al. (2002); Brah and Lim (2006); Demirbag et al. (2006); and Feng et al. (2006) and it was modified where necessary. All the items in the questionnaire were measured with a five-point Likert scale ranging from very low (1) to very high (5) to ensure consistency and the ease of data computation (Brah and Lim, 2006). This scale was also pre-tested several times by academics, consultants and 7 SMEs, who were well known to the researcher and it was found to be valid on the basis of our study. The questionnaire distributed contained seven questions in three different categories as follows (see the Appendix): (1) Questions 1-5. Data on SMEs profile(role in the enterprise, type of industry, number of employees, ownership and years of implementing TQM). (2) Question 6. Data on TQM critical success factors (24 practices). (3) Question 7. Data on performance measures (15 measures). It was requested that the questionnaire needed to be completed by production manager or any manager in charge of quality management as in Saraph et al. (1989) and Demirbag et al. (2006).

Reliability of the questionnaire Cronbach s alpha scores were computed for each construct (strategic factors, tactical factors, operational factors, operational measures, financial measures and non-financial measures) to measure the internal consistency and to indicate how different items can reliably measure the construct. Kline (1998) pointed out that a reliability coefficient of around 0.90 can be considered excellent, values of around 0.80 as very good, and values of around 0.70 as adequate, depending on the questions. In this research, all scales have reliability coefficients ranging from very good to excellent where their values were ranging from 0.84 to 0.97 (see Table I). More importantly, research conducted by Brah et al. (2002) and Brah and Lim (2006) found the internal consistency level TQM CSFs and performance measures to be greater than 0.70. Thus, the scales used in this research could be considered as reliable. Critical success factors for TQM implementation 223 Results of the study Profile of the respondents Table II presents the demographic profile of the respondents. The response rate was 46.8 per cent, i.e. 113 out of the 297 companies claiming to have implemented or have been implementing some of TQM practices. This is a healthy sign as it suggests that a substantial number of Qatari SMEs realize the importance of TQM as a critical factor in the success and survival of manufacturing firms in the marketplace (Brah et al., 2002). The responses indicated that a majority of the respondents completing the questionnaire were production managers, i.e. of the 139 respondents, 113 (81 per cent) were production mangers. This result may stem from the fact that the introduction of TQM can result in, a dramatic increase in operational effectiveness (Slack et al., 2001). The findings in Table II indicate that the majority of SMEs implementing TQM programs are family owned. This result can be interpreted as a major feature in the Qatari economic structure with a large family business sector dominating control over the industry. The metal, machinery and equipment firms constituted the largest portion of the respondents with 32.4 percent of respondents. This result supports the result of Rahman (2001) study which concluded that manufacturing firms in the engineering, manufacturing (durable), and manufacturing (non-durable) fields are the major industries in which TQM programs have been implemented. Constructs Number of items a Strategic factors a 6 0.93 Tactical factors a 8 0.89 Operational factors a 10 0.97 Operational measures 6 0.87 Financial measures b 4 0.91 Non-financial measures b 5 0.84 Notes: a TQM critical success factors; b Organizational performance; a ¼ Cronbach alpha Table I. Measures of constructs reliability and convergent validity

IJPPM 58,3 224 Table II. Demographics of respondents of the survey Number of respondents Percentage of respondents Years of implementing TQM Less than 3 years 87 62.6 More than 3 years 52 37.4 Role in the enterprise Production manager 113 81.0 General manager 17 12.0 Marketing manager 7 5.0 Other 3 2.0 Ownership Created as a new business 23 16.6 Franchised 4 2.9 Purchased 18 12.9 Family business 94 67.6 Other 0 0.0 Capital Less than QR 1.5 millions 78 56.1 QR 1.5 millions-qr 10 millions 61 43.9 More than QR 10 millions 0 0.0 Type of industry Food and beverage 23 16.6 Textile, garments and leather 18 12.9 Wood and furniture 14 10.1 Chemical and petrochemical 32 23.0 Mining 6 4.3 Metal, machinery and equipment 45 32.4 Other 1 0.07 Constructs of TQM CSFs In order to examine if the items for a construct share a single underlying factor and to establish discriminant validity of the constructs under investigation, an exploratory factor analysis (EFA) using varimax rotation was performed. Hair et al. (1995) indicated that factor loading with coefficients greater than 0.50 are very significant. Accordingly, this research used 0.50 as the cutoff score for factor loadings. In order to determine the number of factors needed to represent the data, the 26 items (variables) measuring the TQM CSFs in the research model were subjected to principal component factor analysis. Table III indicates that three factors out of 26 critical success variables were extracted with an eigenvalue greater than 1 for each, and explaining 75.71 percent of the total variance. Based on the items loading on each factor, these factors were labelled as strategic factors (factor 1), tactical factors (factor 2) and operational factors (factor 3). None of the items (variables) were dropped in the analysis because all factor loadings exceeded 0.50 on its own factors, i.e. all items loaded onto the expected factors as they were originally designed. This analysis shows that the average variances extracted (AVE) of the individual constructs are higher than the shared variances between the constructs, thus confirming discriminant validity.

Component Factor 1 Factor 2 Factor 3 TQM critical success factors Strategic factors Tactical factors Operational factors Leadership 0.882 Organisational culture 0.923 Top management support 0.801 Continuous improvement 0.603 Benchmarking 0.731 Quality goals and policy 0.861 Team building and problem solving 0.835 Employee empowerment 0.741 Employee involvement 0.598 Employee training 0.901 Use of information technology 0.743 Supplier quality 0.710 Supplier relationships 0.911 Assessment of performance of suppliers 0.730 Product and service design 0.914 Enterprise performance metrics for TQM 0.667 Process control 0.823 Customer orientation 0.634 Management of customer relationships 0.612 Resources value addition process 0.881 Realistic TQM implementation schedule 0.752 Customer and market knowledge 0.651 Resources conservation and utilization 0.683 Inspection and checking work 0.923 Eigenvalues 3.652 2.973 2.483 Percent of variance explained 28.32 25.16 22.23 Cumulative percent 28.32 53.48 75.71 Critical success factors for TQM implementation 225 Table III. Results of factor analysis for CSFs Testing the measurement models A confirmatory factor analysis (CFA) using AMOS version 6.0 package was used to test the measurement model. To evaluate the fit of CFAs, several goodness-of-fit indicators were used to assess the model s goodness of fit including the ratio of x 2 to degrees-of-freedom (df), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), non-normalized fit index (NNFI), comparative fit index (CFI) and standardized root mean square residual (SRMSR) (see Table IV). The results indicated that all individual measurement model indices exceed their respective common acceptance levels recommended by previous researchers (Jöreskog and Sörbom, 1982; Bentler, 1990; Browne and Cudeck, 1993; MacCallum et al., 1996; Hu and Bentler, 1995), thus suggesting that all the constructs were unidimensional and demonstrating that the measurement model posited a good fit with the data collected. Path model The structural equation modeling was considered as a more comprehensive and flexible approach to research design and data analysis than any other statistical model (Hafeez et al., 2006). Therefore, this study develops a structural equation modeling (SEM) model, where the combined effects of CSFs of TQM on the primary measures as expressed by

IJPPM 58,3 226 Table IV. Goodness of fit indices for individual measurement and structural models Measurement model Fit indices Suggested value SF * TF * OF * OP * FM * Non-FM * Structural model x 2 /df,3.00 1.46 1.09 0.68 0.41 01.0 0.07 1.15 [P_ value ] [0.19] [0.18] [0.34] [0.59] [0.88] [0.67] [0.34] Goodness-of-fit index (GFI).0.90 1.00 1.00 1.00 1.00 1.00 1.00 0.93 Adjusted goodness-of-fit index (AGFI).0.80 0.94 0.96 0.95 0.98 0.99 0.98 0.89 Non-normalized fit index (NNFI).0.90 1.00 1.00 1.00 1.00 1.00 1.00 0.93 Comparative fit index (CFI).0.90 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Standardized root mean square residual (SRMSR),0.10 0.0083 0.0041 0.0066 0.0053 0.0037 0.0017 0.0521 Notes: * SF ¼ Strategic factors; TF ¼ Tactical factors; OF ¼ Operational factors; OP ¼ Operational performance; FM ¼ Financial performance; Non-FM ¼ Non-financial performance

the operational performance, as well as the effects of these measures on the secondary measures as expressed by the organizational measures, are tested using survey data as shown in Figure 2. A similar set of indices was used to examine the structural model (see Table IV). The model s key statistics are good since the x 2 =df ¼ 1:15, GFI ¼ 0:93, AGFI ¼ 0:89, NNFI ¼ 0:93, CFI ¼ 1:00, and SRMR ¼ 0:0521. We can thus safely conclude that the model is accepted to fit the data and we can continue to analyze the outcome of the hypothesized effects. Hypotheses test results One of the purposes of this study is to test the hypothesized causal relationships among the constructs of the model, using the structural equation-modeling package of AMOS. The model parameters were estimated using the maximum likelihood estimates (MLE) method. The average of item scores for each factor in TQM construct was used as measures in the path model as in Demirbag et al. (2006); and Sila (2007). Carroll and Ruppert (1998); Chatterjee and Price (1991); and Hutcheson (1997) indicated that using the standard regression techniques such as the MLE technique require us to test the normality for all the constructs. Therefore, for all the constructs, tests of normality specifically Skewness, and Cook s distance were computed. They indicated no departure from normality. Thus, we proceeded in using the MLE technique to estimate the model. Figure 2 presents the estimated standardized parameters for the causal paths and their level of significance. It also illustrates the strength of the relationships among the constructs. Following Cohen s (1988) recommendations, standardized path coefficient with absolute values of less than 0.10 may indicate small effect; values of around 0.30 a medium effect; and large effects may be suggested by coefficients with absolute value of 0.50 or more. As a consequence, the results of the squared multiple correlations posit that the fit of the model to the data is strong (0.55, 0.58, and 0.51 respectively). Critical success factors for TQM implementation 227 Figure 2. Results of path analysis

IJPPM 58,3 228 Moreover, Table V summarizes the measurement models for TQM practices and shows the hypothesized relationships, the standardized regression weight for each variable, the results of hypotheses testing and the square multiple correlations for each construct. Once again, we computed the chi-square statistic of the model (for double accuracy). It was very small (x 2 ¼ 11:731) and insignificant (p ¼ 0:096), thus demonstrating that the measurement model posited a good fit with the data collected. The findings in Table V support our conceptual model. The results place support to all the hypotheses. Hypotheses 1, 4 and 5. The relationship between strategic factors and operational performance, financial performance and non-financial performance. Inspection of these coefficients indicates that, as expected, strategic factors have a strong significant positive effect on operational performance, financial performance and non-financial performance, thus, confirming H1, H4 and H5. (H1: b ¼ 0:68, p, 0.01; H4: b ¼ 0:56, p, 0.01; H5: b ¼ 0:61, p, 0.01;). This result is not surprising considering that strategic factors such as leadership, organizational culture, top management support, continuous improvement and quality goals and policy have a major impact on what the organization does and how it does it (Stevenson, 2007). Therefore, without those factors it is hard for TQM to be implemented effectively and successfully. These positive effects support previous studies that investigated the relationship (see Demirbag et al., 2006; Sila, 2007). Hypotheses 2, 6 and 7. The relationship between tactical factors and operational performance, financial performance and non-financial performance. The findings indicated that there is a strong significant positive effect of the tactical factors on the operational performance (H2: b ¼ 0:73, p, 0.01), but there is a weak effect of the tactical factors on the financial performance and non-financial performance of the SMEs (H6: b ¼ 0:18, p, 0.05; and H7: b ¼ 0:17, p, 0.05). This is again expected, as employee empowerment, employee training, employee involvement, team building, supplier quality and supplier relationships are the required pillars to be strongly built in the organizations operations structure to maximize the effects of TQM on the operational performance. This is a good sign reflecting that SMEs in Qatar are aware of how to specify the methods and actions necessary to achieve SME objectives i.e. operational objectives and organizational objectives of TQM implementation. These findings are consistent with previous studies that investigated the relationship (see Tata et al., 2000; Ahmad and Schroeder, 2002; Huang and Lin, 2002; Sila, 2007). Hypotheses 3, 8 and 9. The relationship between operational factors and operational performance, financial performance and non-financial performance. The standardized regression weight for the direct relationships between operational factors and operational and financial performances were found to be positive and significant (H3: b ¼ 0:81, p, 0.01; H8: b ¼ 0:89, p, 0.01) indicating a strong support for H3 and H8 that operational factors had a positive and strong direct effects on operational performance and also on financial performance measures as shown in Table V. In line with the above mentioned result, the standardized regression weights for the relationship between operational factors and non-financial performance were found to be positive and significant (H9: b ¼ 0:15, p, 0.05), thus confirming H9.

Hypothesis result R 2 Suggested Obtained Standardized coefficient Hypothesized relationship Predictor variables Criterion variables Strategic factors Operational performance H1 0.68 ** Supported 0.55 Tactical factors Operational performance H2 0.73 ** Supported Operational factors Operational performance H3 0.81 ** Supported Strategic factors Financial performance H4 0.56 ** Supported 0.58 Tactical factors Financial performance H6 0.18 * Supported Operational factors Financial performance H8 0.89 ** Supported Operational performance Financial performance H10 0.67 ** Supported Strategic factors Non-financial performance H5 0.61 ** Supported 0.51 Tactical factors Non-financial performance H7 0.17 * Supported Operational factors Non-financial performance H9 0.15 * Supported Operational performance Non-financial performance H11 0.19 * Supported Statistic Chi-square significance $0.05 0.072 Chi-square/degree of freedom #5.00 3.645 Notes: * p, 0.05; ** p, 0.01 Critical success factors for TQM implementation 229 Table V. Standardized regression weights

IJPPM 58,3 230 To a large extent this result is similar to Handfield et al. (1998); Anderson and Sohal (1999); Demirbag et al. (2006); and Sila (2007) where they found that there is a significant association between TQM practices and operational and organizational performances. Hypotheses 10 and 11. The relationship between operational performance and financial performance and non-financial performance. Operational performance has a strong effect on financial performance (H10: b ¼ 0:67, p, 0.01) while there is a weak effect of the operational performance on non-financial performance (H11: b ¼ 0:19, p, 0.05). However, H10 and H11 are confirmed. Thus, this finding confirmed a previous study that investigated the relationship (see Brah et al., 2002; Brah and Lim, 2006). This finding shows the nature of the relationship between TQM effectiveness-operational performance- and the success of TQM organizational performance. In other words, operational performance measures should be brought into the proactive measurement loop. They should be the starting point of the measurement cycle, particularly if TQM managers are really interested in reaping the full benefits of TQM implementation. Conclusion, theoretical and managerial implications The purpose of the current paper is to identify the critical success factors of TQM implementation and to evaluate their impact on the primary measures as expressed by the operational performance and the secondary measures as expressed by the organizational performance in SMEs in the Qatari industrial sector. But, the novelty of it lies in investigating the effects of the operational performance on the organizational performance i.e. financial and non-financial performances of SMEs. Unlike the previous studies, the current study presents a roadmap for the successful implementation of TQM in SMEs. A roadmap proposed by the current study has been taken from a model proposed in the study. The model contained 24 CSFs which are expected to enhance the practices of TQM implementation in SMEs. The model divides those factors into three levels, namely strategic, tactical, and operational factors. However, our findings are consistent with the findings of previous studies where the CSFs of TQM implementation in SMEs in Qatar are similar to their peers in developed countries including USA, Japan and the Far East. Our model implicitly acknowledges the potency of strategic factors as crucial factors in the successful implementation of TQM in SMEs in Qatar. Overall, it can be concluded that there is a causality between strategic factors of TQM practices and operational and organizational performances (H1, H4 and H5 are significant). Hence, it can be said that CSFs and operational and organizational performances are largely related and feed-off from each other. Interestingly, we found that tactical factors (H2) have a strong impact on operational performance. Moreover, the higher the degree of employees empowerment, employees training, quality suppliers, employees involvement displayed by the SMEs, the greater their influences on operational performance and consequently, the higher the likelihood of the success of TQM implementation. Thus, this explains why tactical factors (H6 and H7) and operational performance (H10 and H11) have an effect on financial and non-financial performances, respectively. The findings of this study also support prior research that operational factors have a strong impact on performance. All operational factors have effects on operational

performance (H3), financial performance (H8), and non-financial performance (H9). This indicates that operational factors are not only concerned about the effectiveness of TQM as expressed by operational performance but also are drivers of the success of TQM as expressed by organizational performance, i.e. financial and non-financial performances. More importantly, this research contributes to the body of knowledge by proposing and testing a conceptual model that considers operational performance as an antecedent to organizational performance. Thus, we can now confirm that operational performance is an important factor for both financial and non-financial performance i.e. organizational performance. From the managerial perspective, this study offers a number of managerial implications for SMEs managers and policy makers. First, the instrument developed and used in this research will be very useful to policy makers in SMEs as a tool for evaluating the effectiveness of their current TQM practices. Second, the SMEs managers should be aware of the intermediating impact of operational performance that TQM-related financial and non-financial performance could only be enhanced by improving operational performance in the first place. Third, in order to get the full potential of TQM it is necessary train all people at all levels in order to create TQM awareness, interest, desire and action. Thus, top management attention might be fruitfully focused on the development of appropriate training programs on TQM implementation. Fourth, SMEs managers should consider suppliers as business partners. They have to be involved in product development, process improvement and making the quality policy. This may lead to better quality and then better customer satisfaction. Fifth, SMEs leaders should be aware that the imminent competitive pressures affecting the domestic market can be appeased through improving both operational performance and organizational performance and this depends on the successful implementation of TQM. Further, the findings of this study could offer a useful potential orientation of the importance of the CSFs for TQM implementation and their impact on performance of SMEs to both researchers and decision makers who are concerned with the issue under investigation. Finally, the findings presented in this paper support the argument that SMEs managers need to realize that the TQM CSFs should be implemented holistically rather than on a piecemeal basis to get the full potential of the TQM practices. Critical success factors for TQM implementation 231 Limitations of the study and future research Since this study is considered as the first attempt to investigate the state of the art of TQM implementation in SMEs in Qatar, directions for further research are suggested. A detailed study (i.e. an independent investigation) of the CSFs influencing the operational performance is warranted. This should be exploited in-depth to understand and highlight what are the hindrances and stumbling blocks that are disturbing the effectiveness of TQM implementation. Another useful avenue for future research is to carry out a comparative study with SMEs in the service sector to provide good insights on the effectiveness of TQM implementation. One important limitation of this study is using perceptual data provided by production managers or quality mangers which may not provide clear measures of performance. However, this can be overcome using multiple methods to collect data in future studies. Finally, the model used in this study can be

IJPPM 58,3 232 tested by conducting cross-country studies. This will help produce a useful benchmark for comparing how the model behaves in the same SMEs but in different countries. References Acs, Z. and Audretsch, D. (1990), The Economics of Small Firms: A European Challenge, Kluwer Academic Publishers, Norwall, MA. Ahire, L. and Golhar, Y. (1996), Quality management in large vs small firms, Journal of Small Business Management, Vol. 34 No. 2, pp. 1-11. Ahmad, S. and Schroeder, R. (2002), The importance of recruitment and selection process for sustainability of total quality management, International Journal of Quality & Reliability Management, Vol. 19 No. 5, pp. 540-51. Anderson, M. and Sohal, A. (1999), A study of the relationship between quality management practices and performance in small businesses, International Journal of Quality & Reliability Management, Vol. 16 No. 9, pp. 859-77. Beaumont, N., Schroder, R. and Sohal, A. (2002), Do foreign-owned firms manage advanced manufacturing technology better?, International Journal of Operations & Production Management, Vol. 22 No. 7, pp. 759-71. Bentler, B. (1990), Comparative fit indices in structural models, Psychological Bulletin, Vol. 107, pp. 238-46. Black, S. and Porter, L. (1996), Identification of critical factors of TQM, Decision Sciences, Vol. 27, pp. 1-21. Brah, S. and Lim, H. (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. Brah, S., Tee, S. and Rao, B. (2002), Relationship between TQM and performance of Singapore companies, International Journal of Quality & Reliability Management, Vol. 19 No. 4, pp. 356-79. Brock, W. and Evans, D. (1986), The Economics of Small Business: Their Roles and Regulations in US Economy, Holmes & Meier Publishers, Teaneck, NJ. Broetzmann, S., Kemp, J., Rossano, M. and Marwaha, J. (1995), Customer satisfaction-lip service or management tool?, Managing Service Quality, Vol. 5, pp. 13-18. Browne, M. and Cudeck, R. (1993), Alternative ways of assessing model fit, in Bollen, K.A. and Long, J.S. (Eds), Testing Structural Equation Models, Sage, Newbury Park, CA, pp. 136-62. Carroll, R. and Ruppert, D. (1998), Transformation and Weighting in Regression, Chapman & Hall, New York, NY. Chatterjee, S. and Price, B. (1991), Regression Analysis by Examples, 2nd ed., John Wiley & Sons, New York, NY. Choi, T. and Eboch, K. (1998), The TQM paradox: relations among TQM practices, plant performance, and customer satisfaction, Journal of Operations Management, Vol. 17 No. 1, pp. 59-75. Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences, Academic Press, New York, NY. Davis, M., Aquilano, N. and Chase, R. (2003), Fundamentals of Operations Management, 4th ed., McGraw-Hill, Boston, MA. Dayton, N. (2003), The demise of total quality management (TQM), The TQM Magazine, Vol. 15 No. 6, pp. 391-6.

Demirbag, M., Tatoglu, E., Tekinkus, M. and Zaim, S. (2006), An analysis of the relationship between TQM implementation and organizational performance: evidence from Turkish SMEs, Journal of Manufacturing Technology Management, Vol. 17 No. 6, pp. 829-47. Easton, G. and Jarrell, S. (1998), The effects of total quality management on corporate performance: an empirical investigation, Journal of Business, Vol. 71 No. 2, pp. 253-307. Fassoula, D. (2006), Transforming the supply chain, Journal of Manufacturing Technology Management, Vol. 17 No. 6, pp. 848-60. Feng, J., Prajogo, D., Tan, K. and Sohal, A. (2006), The impact of TQM practices on performance: a comparative study between Australian and Singaporean organizations, European Journal of Innovation Management, Vol. 9 No. 3, pp. 269-78. Gulf Organization for Industrial Consulting (2005a), A Report on the Status of SMEs in GCC Countries, Doha, Qatar. Gulf Organization for Industrial Consulting (2005b), The 2nd Arabian Symposium on SMEs, Kuwait, May. Hafeez, K., Keoy, K. and Hanneman, R. (2006), E-business capabilities model: validation and comparison between adopter and non-adopter of e-business companies in UK, Journal of Manufacturing Technology Management, Vol. 17 No. 6, pp. 806-28. Hair, J.F. Jr, Anderson, R., Tatham, R. and Black, W. (1995), Multivariate Data Analysis, Prentice-Hall, Upper Saddle River, NJ. Hair, J.F. Jr, Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), Multivariate Data Analysis, Pearson-Prentice Hall, Upper Saddle River, NJ. Handfield, R., Ghosh, S. and Fawcett, S. (1998), Quality-driven change and its effects on financial performance, Quality Management Journal, Vol. 5 No. 3, pp. 13-30. Hodgetts, R., Kuratko, D. and Hornsby, J. (1999), Quality implementation in small business: perspectives from the Baldrige award winners, SAM Advanced Management Journal, Vol. 64 No. 1, pp. 37-47. Hu, T. and Bentler, M. (1995), in Hoyle, R. (Ed.), Evaluating Mode Fit, in Structural Modeling Concepts, Issues and Applications, Sage, Thousand Oaks, CA, pp. 76-99. Huang, Y. and Lin, B. (2002), An empirical investigation of total quality management: a Taiwanese case, The TQM Magazine, Vol. 14 No. 3, pp. 172-81. Hutcheson, G. (1997), Factor analysis, Faculty of Social Science, Glasgow University, Glasgow, unpublished paper. Jöreskog, K. and Sörbom, D. (1982), Recent developments in structural equation modeling, Journal of Marketing Research, Vol. 19, pp. 404-16. Jung, J. and Wang, Y. (2006), Relationship between total quality management (TQM) and continuous improvement of international project management (CIIPM), Technovation, Vol. 26 Nos 5-6, pp. 716-22. Kapuge, A. and Smith, M. (2007), Management practices and performance reporting in the Sri Lankan apparel sector, Managerial Auditing Journal, Vol. 22 No. 3, pp. 303-18. Kline, R. (1998), Principles and Practice of Structural Equation Modeling, Guilford Press, New York, NY. Koh, S., Demirbag, M., Bayraktar, E., Tatoglu, E. and Zaim, S. (2007), The impact of supply chain management practices on performance of SMEs, Industrial Management & Data Systems, Vol. 107 No. 1, pp. 103-24. MacCallum, R., Browne, M. and Sugawara, H. (1996), Power analysis and determination of sample size for covariance structure modeling, Psychological Methods, Vol. 1 No. 2, pp. 130-49. Critical success factors for TQM implementation 233

IJPPM 58,3 234 Montes, M., Jover, V. and Fernandez, M. (2003), Factors affecting the relationship between total quality management and organizational performance, International Journal of Quality & Reliability Management, Vol. 20 No. 2, pp. 189-209. Motwani, J. (2001), Critical factors and performance measures of TQM, The TQM Magazine, Vol. 13 No. 4, pp. 292-300. Oakland, S. (1995), Total Quality Management Text with Cases, BH Ltd, Oxford. Petroni, A. (2002), Critical factors of MRP implementation in small and medium-sized firms, International Journal of Operations & Production Management, Vol. 22 No. 3, pp. 329-48. Powell, C. (1995), Total quality management as competitive advantage: a review and empirical study, Strategic Management Journal, Vol. 16, pp. 15-37. Pun, K. and Gill, R. (2002), Integrating EI/TQM efforts for performance improvement: a model, Integrated Manufacturing Systems, Vol. 13 No. 7, pp. 447-58. Qatar Bank for Industrial Development (2001), A Report on SMEs Definition in State of Qatar, Doha, Qatar. Rahman, S. (2001), A comparative study of TQM practice and organisational performance of SMEs with and without ISO 9000 certification, International Journal of Quality & Reliability Journal, Vol. 18 No. 1, pp. 35-49. Ramamurthy, K. (1995), The influence of planning on implementation success of advanced manufacturing technology, IEEE Transactions on Engineering Management, Vol. 42 No. 1, pp. 62-73. Robbins, S. and DeCenzo, D. (2005), Fundamentals of Management: Essential Concepts and Applications, 5th ed., Prentice-Hall, Englewood Cliffs, NJ. Samson, D. and Terziovski, M. (1999), The relationship between total quality management practices and operational performance, Journal of Operations Management, Vol. 17 No. 4, pp. 393-409. Saraph, V., Benson, G. and Schroeder, G. (1989), An instrument for measuring the critical factors of quality management, Decision Sciences, Vol. 20 No. 4, pp. 810-29. Seth, D. and Tripathi, D. (2005), Relationship between TQM and TPM implementation factors and business performance of manufacturing industry in an Indian context, International Journal of Quality & Reliability Management, Vol. 22 No. 3, pp. 256-77. Sila, I. (2007), Examining the effects of contextual factors on TQM and performance through the lens of organizational theory: an empirical study, Journal of Operations Management, Vol. 25 No. 1, pp. 83-109. Slack, N., Chambers, S. and Johnston, R. (2001), Operations Management, 3rd ed., Prentice-Hall, Harlow. Sterman, J., Repenning, N. and Kofman, F. (1997), Unanticipated side effects of successful quality programs: exploring a paradox of organizational improvement, Management Science, Vol. 43, pp. 503-21. Stevenson, W. (2007), Operations Management, 9th ed., McGraw-Hill, Boston, MA. Tata, J., Prasad, S. and Motwani, J. (2000), Benchmarking quality management practices: US versus Costa Rica, Multinational Business Review, Vol. 8 No. 2, pp. 37-42. Turban, E., McLean, E. and Wetherbe, J. (1999), Information Technology for Management: Making Connections for Strategic Advantage, John Wiley & Sons, Hoboken, NJ. Yusof, S. and Aspinwall, E. (2000), Critical success factors in small and medium enterprises: survey results, Total Quality Management, Vol. 11 No. 4, pp. 248-462.