Relationship between quality management practices and competitive performance: Japanese quality award perspective

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Understanding and Responding to Customer and Market Need (100) System Operation Direction and Driving Force Results I. Leadership 4. Strategy Planning and of Senior Leaders (120) Deployment (60) 8. Activity Results (400) 5. Improving Employee and • 2. Social Responsibility of Organizational Capabilities (100) Management (50) 6- Customer Value Creation Processes(120) 7. Information Management (50) Figure I -Japan Quality Award Model (Japan Quality Award Committee Administration, 2008) The different between JQA and DP is that DP does not provide a model framework for organizing and prioritizing criteria. Unlike JQA that focuses on continuous improvement and self-innovation, the DP is more concerned with company-wide quality control for product manufacturers. MBNQA and EQA include in their framework the importance of fostering a culture of entrepreneurial challenges and of harnessing new technology, as well as in employing diversity to create competitiveness and business, while JQA management philosophy focuses on building consensus (shared vision), aligning people with processes, and forming cooperation with suppliers to achieve excellent quality (Khoo and Tan, 2003). RESEARCH FRAMEWORK Empirical research refers to the research that makes use of data that is derived from naturally occurring field-based observations, taken from industry. Recently, empirical research on quality management is an area becoming increasingly important for both practitioners and academicians because it provides the type of understanding needed to achieve excellent quality in a global economy (Flynn et al., 1990). As more and more practitioners use JQA as a tool for self-assessment and continuous quality improvement, the JQA framework becomes so imperative that the JQA concept, model, and constructs be tested and validated (Calingo, 2002). In this study, we would like to adopt the JQA perspective to empirically study quality management because the JQA framework specifies the cause and effect of quality initiatives, implying which practices will lead to various desired outcomes. From our intensive literature review, we focus on a set of six quality management practices, which reflect six JQA categories: Top management leadership, Information analysis, Customer relationship, Strategic planning. Workforce 22 Phan and Matsui: Relationship between Quality Managemenl Practices and Competitive Performance- Japanese Quality Award Perspective ___________________________________ management, and Process management {Social responsibility of management is not included in this study because the data is not available). In the quality management literature, these practices have been also highlighted as critical components for achieving excellent quality. In this study, six constructs are developed to measure those six key quality management practices, based on the JQA concept. Table 3 summaries the relevance between these six underlying constructs and the JQA evaluation criteria. The JQA approach to these quality management practices, their interrelationship, and their impact on competitive performance as discussed in the quality management literature are summarized as follows. Top management leadership JQA emphasizes that top management is an important driver for developing quality management system. Top management leadership is a critical requirement for effective and efficient quality management because it encourages the practices and behaviors that lead to quality performance throughout the organization. Top management leadership is expected to have impact on all of the aspects of quality management. Particularly, top management leadership strongly relates to the development and deployment of manufacturing strategy throughout the plant, the analysis and utilization of quality information in the shop floor, and development of customer and supplier relationship for quality improvement. This argument has been supported by such empirical studies as Anderson et al. (1995), Flynn et al. (1995), Samson and Terziovski (1999), Das et al. (2000), Wilson and Collier (2000), and Kaynak (2003). Information analysis The quality improvement strongly depends on how the plants collect, analyze, and utilize the quality information. JQA emphasizes that information analysis provides the input for several managerial activities such as strategic planning, workforce management, process management, and customer relationship. Because it links to every aspect of quality management, information analysis needs the support and leadership by top management. This argument is supported by such empirical studies as Flynn et al. (2005), Choi and Eboch (1998), Kaynak (2003), and Yeung et al. (2005). Strategic planning JQA indicates that a strategic plan should be formed and developed based on facts and logical thinking. It presents the long-term goals, objectives, and commitment throughout the organization. Quality management studies such as Wilson and Collier (2000) and Parast et al. (2006) reported the positive impact of strategic planning on business performance. The successful quality management is dependent on how the plants develop and deploy their strategic plan, quality policy, and quality objectives. Particularly, some aspects of the workforce management as the employee's training and involvement depend on the establishment of the long-term strategy. In another side, the effective strategic planning process is influenced by information analysis. Workforce management JQA indicates that organizational capabilities would be enhanced by (1) respecting the opinion and actions of employees, (2) involving employees in quality improvement and innovation, (3) providing organizational support to employees though training, and (4) improving the working environment. In the same line, several empirical literatures (Flynn et 23 Phan and Matsui: Relationship between Quality Management Practices and Competitive Performance: Japanese Quality Award Perspective al., 1995; Choi and Eboch, 1998; Samson and Terziovski, 1999) report the importance of workforce management to quality improvement. It includes the promotion of employees' skills and attitudes by providing skill training programs and involving them into continuous improvement programs. These workforce management activities are moderated by the plants strategy. The plants with long-term and quality-based competitive strategy should focus on developing skills, attitude, and quality mindset of their employees. The effectiveness of workforce management is exposed in production processes. The properly trained workers improve product quality by conducting several activities such as statistical process control (SPC), 5S, and preventive maintenance. Customer relationship • JQA emphasizes the value interaction with customers and suggests companies to review the intended values from the customer's point of view. Managing the close relationship with customer allows manufacturing plants to identify and clarify their customers' requirements that will be translated into product and process specifications. Thus, the strong relationship with customers should directly relate with process management. The contribution of customer relationship to plants performance and its linkage with process management are reported in many empirical literatures such as Flynn et al. (1995), Samson and Terziovski (1999), Cua et al. (2001), Kaynak (2003), Yeung et al. (2005), and Parast et al. (2006). Process management Process management refers to the techniques and tools applied to a process to improve its effectiveness, hold the gains and ensure its integrity in fulfilling customer requirements. As JQA indicates, effective process management depends on several requirements concerning with human aspects, quality information analysis, and customer relationship management. In manufacturing plants, process management contributes to plant performance by reducing the variability of process, which leads to reduction in scraps and reworks. Better product quality allows the manufacturing plants to gain the higher competitive position in terms of cost, delivery and flexibility, because the better product quality with less scraps and reworks allows the plants to achieve lower manufacturing cost. The lesser defective products are associated with the shorter cycle time because the plants do not need to waste their times on repairing and reworking. This enables the plants to achieve on-time delivery and to change their production volume easily (volume flexibility). In brief, process management improves different dimensions of competitive performance. This argument is supported by such empirical studies as Flynn et al. (1995), Anderson et al. (1995), Forza and Flippini (1998), Choi and Eboch (1998), Dow et al. (1999), Kaynak (2003), Yeung et al. (2005), and Phan and Matsui (2009). We visualize and summarize the arguments on the relationship among quality management practices and competitive performance as discussed above into a framework presented in Figure 2. The boxes describe six quality management practices and competitive performance. The cause-and-effect relationships between two boxes are presented by arrows. It is expected that: • Top management leadership directly links to Strategy planning. Information analysis, and Customer relationship. • Information analysis directly links to Strategy planning. Workforce management. Process management, and Customer relationship. • Strategic planning directly links to Workforce management. 24 Phan and Matsui: Relationship between Quality Management Practices and Compefitive Performance: Japanese Ouality Award Perspective • Workforce management. Information analysis, and Customer relationship directly link to Process management. • Process management and Customer relationship directly link to competitive performance. Based on the JQA approach and the quality management literature as discussed above, we establish the hypotheses on the interrelationship among quality management practices and their impact on competitive performance as follows: Hypothesis Hj: Information analysis is directly related to Top management leadership. Hypothesis H2: Customer relationship is directly related to Top management leadership and Information analysis. Hypothesis H3: Strategic planning is directly related to Top management leadership and Information analysis. Hypothesis H4: Workforce management is directly related to Information analysis, Strategy planning, and Top management leadership. Hypothesis H5: Process management is directly related to Workforce management, Information analysis, and Customer relationship; and indirectly related to Top management leadership and Strategy planning. Hypothesis Ha: Competitive performance is directly related to Process management and Customer relationship; and indirectly related to Workforce management. Information analysis. Top management leadership, and Strategy planning. To measure the conceptual constructs underlying the JQA categories, a set of eleven measurement scales is selected from High Performance Manufacturing (HPM) Project database. We use four individual scales to measure Top management leadership, Information analysis. Strategic planning, and Customer relationship respectively. Workforce management is the first super-scale that is constructed from three individual measurement scales: Task- related training for employees, Small group problem solving, and Employee suggestions. The second super-scale, Process management is constructed from four individual measurement scales: Cleanliness and organization, Process control, Preventive maintenance, and Supplier quality involvement. Competitive performance refers to the ability of a business organization to survive in a competitive marketplace by providing products or services that satisfy its customers. For manufacturing companies, the competitive performance would be achieved by developing cumulative capabilities regarding quality, cost, delivery, flexibility, and time (Schroeder and Flynn, 2001). In this study, competitive performance is measured by: Unit cost of manufacturing, Conformance to product specifications, On-Time delivery performance, Fast delivery. Flexibility to change product mix, Flexibility to change volume. Inventory turnover, Cycle time (from raw materials to delivery), New product development lead time, Product capability and performance, On-time new product launch, Product innovativeness, and Customer support and service. Those performance indicators have been identified in quality management literature as the key performance of manufacturing companies (Shroeder and Flynn, 2001; Cua et al., 2001; Phan and Matsui, 2009 and 2010; Naor et al., 2010). 25 a ro u c o s- j= u ro —3 I a. 0 !2 £ ■~ f ^ £ c> r.OJI ^ I VI c E ^ Ll SO w 5 & _ o .0 ^ ro « o E ■5. a ■r: a. tB P ra 3 ;9 .s KJ 3 H E 0 >■—' i2 <3 u Q; s: ) C3 u 0 1 ro a. ^ < 3 3 O se •"■ -a CL <N t3 CL I §> § .2 a, a: 2 = o - .- (J rt Q ii c OJ: O cd 1J s (_> CJ -^I - 5Pn O C a ^ 2 a CJ 00 b.1 j: H c o o cd '5o .S OJ u O:; w c "b- CO 5 lyi a- I =3 n f o r A n 3 OJ / \ Is ■2 °^ C Q. It „ C v; rt "Q O CO u ^ OJ t- ^-o c ;3 c3 U rt Qj S-^ C crt 2 ^ s -« Phan and Matsui: Relationship between Quality Management Practices and (Competitive Performance: Japanese Ouality Award Perspective DATA COLLECTION AND MEASUREMENT TEST This study explored the data gathered through the international joint research initiative called High Performance Manufacturing (HPM) Project, which started in 1980s by researchers at the University of Minnesota and Iowa State University. The overall target of project is to study ■^best practices" in manufacturing plants and their impact on plant performance in the global competition. The first round of the survey has been conducted in 1989 gathering information from forty-six US manufacturing plants. In 1992, the project has been expanded to include researchers from Germany, Italy, Japan, and the UK. The second round of the survey gathered data from one hundred and forty-six manufacturing plants from the above countries. In 2003, the project has been expanded to include other researchers from Korea, Sweden, Finland, Austria, and Spain. The total number of manufacturing plants participated in the third round of the survey is two hundred and thirty-eight plants. Within each country, surveyed are plants with more than 100 employees belonging to one of three industrial fields - electrical & electronics, machinery, and transportation. The researchers, based on business and trade journals and financial information, identified manufacturers as having either a "World-Class Manufacturer (WCM)" or a "Non World-Class Manufacturer (NWCM)" reputation. Each manufacturer selected one typical plant for participating in the project. This selection criterion allowed for the construction of a sample with sufficient variance to examine variables of interest for the research agenda (Bozarth et al., 2009; Naor et al., 2010). Table 4 - Characteristic i of Survey's Respondents United States Japan Germany Italy Korea Total Electrical & Electronic 9 10 9 10 10 48 Machinery 11 12 13 10 10 56 Automobile 9 13 19 7 11 59 World Class 15 17 NA 13 18 63 Non- World Class 14 17 NA 14 13 58 Plant characteristics * » Average Market Share (%) 25.50 25.05 30.21 23.38 31.54 Average Sale ($000) 284.181 1.118.492 1.736.230 71.209 2.266.962 Average of Number of Employee* 583 1555 601 370 1045** * Including both salary personnel employee and hourly personnel ** Data from 19 plants only •Jn this research, we acquire the data from 163 manufacturing plants in five countries: Germany (41 plants), Italy (27 plants), Japan (35 plants), Korea (31 plants), and the United States (29 plants) during 2003-2004. The plants belong to three industries: electrical and electronics (48 plants), machinery (56 plants), and automobile (59 plants). The main characteristic of those plants is summarized in Table 4. Eleven measurement scales for quality management were constructed by four to six question items evaluated on a seven-point Likert scale (l=Strongly disagree, 4=Neither agree nor disagree, 7=Strongly agree). Those questions were answered by nine individuals as Direct Labor, Human Resources Manager, Production Control Manager, Inventory Manager, Process Engineer, Quality Manager, Supervisor, Plant Superintendent, and Plant Manager. 28 Phan and Matsui: Relationship between Quality Management Practices and Competitive Performance Japanese Quality Award Perspective Table 5 - Measurement Analysis ___________________________ Factor analytical results: eigenvalue value Measurement Scales Cronbach Alpha (% variance) JPN US PS GER ITA JPN US PS GER KOR Top management leadership 7.11 3.15 3.09 3 17 298 3.03 0 80 0 80 0 77 0.81 0.77 0.79 (52) (53) (52) (52) (50) (50) Strategic planning 2.46 3.02 2.20 2.55 290 2,55 0 79 0 89' 0 71 0.77 0.97 0.80 (62) (75) (55) (64) (73) (64) Infonnation analysis 2.80 3.45 2.71 2.48 263 2,79 080 088 078 0,74 0.76 . 0,80 (56) (69) (54) (50) (53) (56) Customer involvement 2.10 2,36 2.09 2 17 2,04 2 11 0,66 075 068 0,71 0 66 0.68 Task-jelated training for (53) (59) (52) (54) (51) (53) employees 2.55 2.37 2.29 2.53 2,60 2.28 080 076 075 0,80 0,82 0.79 (64) (59) (57) (63) (65) (57) Small group problem solving 3.53 3,40 2.63 2.66 3.50 3.14 0.86 0.84 0.74 0.75 0 85 0.82 (59) (57) (44) (44) (58) (52) Employee suggestion 3.33 3,22 2.85 2.37 3.17 3.03 087 0.86 0.81 0.69 0.85 0.83 (67) (65) (57) (47) (63) (61) Cleanliness and organization 3.19 2.80 3.08 223 2.92 2,82 0.85 0,79 0,83 0,66 0,81 0,80 (64) (56) (62) (45) (58) (56) Process control 0,80 0,89 0,85 0.80 0 84 0 82 2.78 3.67 3.17 2.81 3.12 2.95 (56) (69) (63) (56) (62) (59) Supplier quality involvement 0,70 0.79 0,70 0.62 0,75 0,70 2.29 2.75 2.27 1.89 2.53 2.29 (46) (55) (45) (47) (51) (46) Preventive maintenance 063 0,71 0,77 0,71 0,72 0,68 2.10 2.37 2.63 2.37 2.43 2.23 (42) (47) (53) (48) (49) (45) Workforce management 0,88 0.90 0,85 0,87 0,84 0,86 2.41 2.51 2.37 2.42 2.33 2.45 (80) (84) (78) (81) (78) (78) Process management 2.74 2.12 2.72 2.28 2,46 2.44 (68) (53) (68) (57) (61) (64) GER: Germany, ITA: Italy JPN: Japan. KOR: Korea, iStat zs, Pooled Sample 0,84 0,68 0.83 0.74 0.76 0.77 i PS: We evaluate the competitive perfonnance by investigating the competitive position of the plant for each performance indicator. Each plant manager was asked to indicate his/her opinion about how the plant compares to its competitors in the same industry on a global basis on a five-point Likert scale (l=Poor or low end of the industry, 2=Below average, 3=Average, 4= Equivalent to competitor, 5=Superior or top of the industry). Then, the overall index for competitive performance of each plant was obtained by calculating the mean value of thirteen individual performance indicators. __________________________ Table 6 - Correlations and Descriptive Analysis __________________________________ Constructs Min. Max. Mean Std. Top management leadership 3.500 7.000 5.549 0.624 Strategic planning 2.500 6.750 5.130 0.880 0.442 1 (0.000) Information analysis • Customer relationship 2.317 6.600 4.839 0.857 0.422 0.459 1 (0.000) (0.000) Workforce management 3.657 6.300 5.224 0,474 0.383 0,271 0.507 1 Process management (0.000) (0.001) (0.000) 0.766 3,444 6,733 5.099 0,559 0.558 0.482 0.652 0.495 (0.000) (0.000) (0.000) (0.000) 3.564 3.564 4.992 0.510 0.531 0.454 0.757 0.564 (0.000) (0,000) (0.000) (0.000) (0.000) Note: Significant level is given in parentheses under binary correlation coefficient 29 Phan and Matsui: Relationship between Qualin Management Practices and Competitive Performance: Japanese Quality Award Perspective The first step of analytical process is the analysis of reliability and validity of eleven individual measurement scales and two super-scales. In this study, Cronbach's alpha coefficient is calculated to evaluate the reliability of each measurement scale. Table 5 shows that the alpha value for all of eleven scales exceeded the minimum acceptable level of 0.60 for the pooled sample and country-wise. Most of the scales have the alpha valu& of above 0.75 indicating that the scales are internally consistent (Nunnally, 1967). Content validity: An extensive review on the JQA'model and quality management studies is undertaken to ensure the content validity. Construct validity. The construct validity is also tested to ensure that all question items in a scale all measure the same construct. Within-scale factor analysis is conducted with the three criteria: (1) uni-dimensionality, (2) a minimum eigenvalue of 1, and (3) item factor loadings in excess of 0.40. The results of the measurement test for the pooled sample and country-wise indicate that all scales are satisfactory in terms of the construct validity. The eigenvalue of the first factor for each scale is more than two. The factor loadings of each item are more than 0.40, mostly ranged between 0.70 and 0.90 for the pooled sample as shown in appendix. HYPOTHESIS TESTING Path analysis is a statistical method of finding cause/effect relationships. It has been used widely in empirical quality management studies (Flynn et al., 1995; Anderson et al., 1995; Kaynak, 2003; Yeung et al., 2005). In this study, path analysis is selected to test the framework and hypotheses, with regression analysis determining the significance of the relationships between the independent and dependent variables. Path coefficients between each independent variable and dependent variable are presented by standardized regression coefficients. As suggested in the cited literature, in order to simplify the model prior to decomposition, all paths whose coefficients are not statistically* significant at the 0.15 level or less should be eliminated (Flynn et al., 1995). The correlations between all pairs of variables are then decomposed into the sum of their direct and indirect effects. Prior to conducting path analysis, a correlation analysis is conducted. Table 6 presents the mean and the standard deviation of each variable along with the correlation matrix. It indicates that there is no problem with unusually high standard deviation and/or unusual mean. Regarding correlation, as discussed in the cited literature, the value of 0.8 or more indicates the possibility to suffer from the multi-collinearity between variables. Table 6 shows that correlation coefficients between Process management and Information analysis and between Workforce management and Process management are close to this criterion. Therefore, the Variance Inflation Factor (VIF) that measures the inflation in parameter estimate due to the coUinearities among independent variables is calculated for each model during path analysis. The value of VIF for each variable is presented in Table 7. By setting the acceptable value for VIF at 10 as suggested in the literature, it is found that all model variables are within the VIF limit indicating that their muhi-collinearities do not have an undue influence on least squares estimates. As the result, all the variables are retained in the model for further analysis. To test the hypotheses, six multiple regression models are developed and the results of analysis are shown in Table 7. We find that: • Top management leadership explains seventeen percent of variability of Information analysis. • Combination of Top management leadership and Information analysis explains twenty- eight percent of variability of Strategy planning and twenty-nine percent of variability of Customer relationship. 30 Phan and Matsui: Relationship between Quality Management Practices and ('ompetitive Performance: Japanese Ouality Award Perspective • Combination of Top management leadership. Information analysis, and Strategy planning, accounts for fifty-three percent of variability of Workforce management. • Combination of Information analysiSy Customer relationship, and Workforce management accounts for seventy-one percent of variability of Process management. • Process management and Customer relationship explain seventeen percent of variability of Competitive performance. * • Next, path coefficients are .decomposed into the direct and indirect effects. The total effect presents the sum of the direct effect and indirect effects of one variable on others as summarized in Table 8. Figure 2 summarizes the direct effects among variable. The arrows present direct relation between two variables along with values of direct effect. The indirect effects to a variable are indicated by a series of forward-pointing arrows. For example. Top management leadership produces indirect effects on Workforce management through mediating effects of Information analysis and Strategic planning. Literature suggests that the path having coefficient less than 0.15 needed to be removed. Therefore, the arrows between Strategic planning and Workforce management and between Customer relationship and Competitive performance (presented in the dashed-line in Figure 2) should be trimmed from the model. The final model has following paths: Top management leadership — Workforce management — -> Process management —► Competitive performance Top management leadership —■ Information analysis - ► Workforce management —► Process management - -> Competitive performance Top management leadership — Information analysis — > Process management —► Competitive performance Top management leadership —* " Information analysis ' Customer relationship —► Process management —-t " Competitive performance Top management leadership — ► Customer relationship -> Process management —► Competitive performance Table 7 - Summary of Path Analysis Dependent Variable F P R^ VIF Independent \'ariable B t P Information analysis 34.81 0.000 0.178 1.000 Top management leadership 0.422 5.900 0.000 Strategic planning 31.428 0.000 0.285 1.222 Top management leadership 0.299 4.047 0.000 1.222 Information analysis 0.331 4.442 0.000 Customer relationship 33.046 0.000 0.292 1.216 Top management leadership 0.206 2.810 0.006 1 216 Information analysis 0.420 5.732 0.000 Workforce management 60.250 0,000 0.535 1 398 Strategic planning 0.136 2.119 0,036 1.375 Infomiation analysis 0.461 7.153 0.000 1.349 Top management leadership 0.304 4.866 0.000 Process management 130.737 0.000 0.712 1 886 Information analysis 0.405 6.943 0,000 1.856 Workforce management 0.435 7.522 0,000 1.438 Customer relationship 0.147 2.898 0.004 Competitive perfonnance 14.995 0.000 0.170 1.467 0,000 Process management 0.347 3.803 1.467 0.270 Customer relationship 0.101 3.803 31 u u o > •u £L« a. F c2 o Per Ar. c CA c E E r fo 0) ..1^ 00 -^ O 00 IM cd o c a. s CL 5 s; CO J= CI ! E C _o o 3 ela cc: cu I "I I I a Ol s; ^ C Q. a. o an <u H an a ea d CJ 00 S-^ I"? CJ) c c c cd t/3 CU ** Ol Phan and Matsui: Relationship between Qualiri' Management Practices and Competitive Performance: Japanese Quality Award Perspective Table 8 • Decompositions of Path Coefficient Dependent Variable Independent V ariable Direct Effect Indirect EfTect Total Effect Information analysis Top management leadership 0.422 0,000 0422 Strategic planning Top management leadership 0.299 0.140 0 439 Information analysis 0331 0.331* 0 331 Customer relationship Top management leadership 0.206 0.177 0383 Information analysis 0.420 0.000 0 420 . Top management leadership 0,304 0.254 0 558 Workforce management Information analysis 0.461 0.045 0.506 Strategic planning 0.136 0.000 0.136 t Top management leadership 0.000 0.470 0.470 Information analysis 0.405 0.282 0687 Process management Strategic planning 0.000 0.059 0.059 Customer relationship 0,147 0.000 0.147 Workforce management 0,435 0.000 0.435 Top management leadership 0,000 0.191 0.191 Information analysis 0,000 0.279 0,279 Competitive performance Strategic planning 0 000 0.024 0,024 Customer relationship 0,101 0.000 0,101 Workforce management 0.000 0.177 0,177 Process management 0,347 0.000 0.347 Table 9 - Model Fit Summary Model Fit Value Recommend Value Chi-square 32954 Comparative Fit Index (CFI) 0.967 >0,90 Root Mean Square Error of Approximation (RMSEA, 90% confidence interval) 0.065 0,00; 0,08 Normed Fit Index (NFI) 0.954 >0,90 Incremental Fit Index (IFI) 0,968 >0,90 Parsimony Normed Fit Index (PNFI) 0.790 >0.70 DISCUSSIONS, IMPLICATIONS, AND LIMITATIONS In the previous sections, we have proposed an analytical framework and research hypotheses, and empirically tested them by the path analysis with muhiple regression models. The results support the hypotheses on the relationship among quality management practices and competitive performance. The main findings and implications of this study can be summarized as below. Firstly, our study reveals some empirical evidences on the interrelationship among quality management practices. The results indicate the significant impacts of Top management leadership and Information analysis on other practices. It is found that Workforce management and Customer relationship highly depend on Top management leadership and Information analysis. Top management leadership provides the direction and environment for quality improvement while Information analysis enables the shop-floor employees to capture the information on process variation, quality performance, and quality problems. The results also identify the critical determinants for Workforce management. Effective workforce management highly depends on the top management commitment and the long-term strategy implementation. These practices formulate the foundation to develop skilled, quality minded 33 Phan and Matsui: Relationship between Quality Management Practices and Competitive Performance: Japanese Quality Award Perspective and knowledgeable workforce. The great infiuence of Information analysis on Customer felationship indicates that the plants should build and maintain an effective information system in order to develop their relationship with customers. Production process is the place, which makes products that meet and excess customers" requirements and expectations. This study indicates that Process management highly depends on Information analysis -and Workforce management. The involvement of trained workers is a critical determinant for effective process management. In addition, the implementation'of statistical process control and preventive maintenance requires the support from information system. Quality data should be visualized and given back to shop-floor employees for identifying and eliminating the sources of quality problems. Secondly, our study suggests the linkage between quality management practices and competitive performance. It is found that competitive performance could be achieved by emphasizing several quality management practices. While process management is found directly related to competitive performance, our analysis strongly recommends that managers should build a strong infrastructure for quality management, based on leadership, workforce management, and information analysis. Our findings are in line with previous quality management studies such as Flynn et al. (2005), Cua et al. (2001), Kaynak (2003), Yeung et al. (2005), Nair (2006), and Arauz et al. (2009). This study contributes to the field by introducing and testing a JQA based-framework for quality management. The JQA management philosophy focuses on creating value through effective leadership, customer value process creation, and customer satisfaction. In addition, JQA emphasizes respecting opinions and actions of employees, involving employees into quality improvement and innovation activities, and training employees. These approaches when implemented in manufacturing plants, as demonstrated by our empirical analysis, lead to the achievement of high performance manufacturing. Though our measurement scales do not show an exact match for the JQA categories and subcategories, we have taken the first step to use JQA as a framework to study the relationship between quality management practices and competitive performance. As explained in the previous sections, our target is to test the relationship between the constructs underlying the JQA approach, not to test the relationship between the JQA categories. The results from the measurement test and the good fit between the data and the proposed framework indicate that our proposal and survey instrument could be used for further study on quality management. In future, other studies using the JQA framework should deal with the weights assigned to the JQA categories in order to identify whether the recent developments on weighting scheme accurately reflect the relative importance of the categories. This will help the researchers to validate the JQA model and contribute to extending the applicability of JQA as an approach for self-innovation in the global context. It is important to view this study in the context of its limitations. Methodologically, this study is based on the cross-sectional survey data gathered via self-reported questionnaires, and individual bias in reporting may exist. Although we addressed the issue of common method bias through the use of multiple respondents in the manufacturing plants, the study still heavily relies on the use of perceptual data. There is another issue concerned with the utilization of subjecfive measures for competitive performance. Because of the industry difference, we can only use subjective evaluation to measure operational aspects of competitive performance. The ftiture studies should try to explore other dimensions of competitive performance such as financial and market performance. In addition, the objective measures should be explored when we will focus on a specific industry. 34 Phan and Matsui: Relationship between Quality Management Practices and Competitive Performance: Japanese Oualiiv IwardPerspective CONCLUSIONS This study uses the database that includes different levels of qua!it> implementation and competitive performance evaluated by the respondents from a variety of plants from five countries. The results confirm that competitive performance is directly dependent on such core quality management practices as process management- which in turn depends on infrastructure on human resource, information analysis, and top management leadership. Our findings suggest that JQA should be adopted as a framework for improvement and innovation rather than a criterion for rewarding organizations with top quality performance. REFERENCES Anderson, J. C. Rungtusanatham, M, Schroeder, R. G. and Devaraj, S. (1995). "A path analytic model of a theory of quality management underlying the Deming Management Method: Preliminary empirical findings," Decision Sciences, Vol. 26, No. 5, pp. 637-658. Arauz, R.. Matsuo. H. and Suzuki, H. (2009), "Measuring changes in quality management: An empirical analysis of Japanese manufacturing companies," Total Quality Management & Business Excellence. Vol. 20. No. 12, pp.1337-1374. Bozarth, C, Warsing, D.. Flynn, B., and Flynn, J. (2009), "The impact of supply chain complexity on manufacturing plant performance,'' Journal of Operations Management, Vol. 27, No. 1, pp.79-93. Calingo, L. M. R. (2002), Quest for Global Competitiveness Through National Quality and Business Excellence Awards, Asian Productivity Organization, Tokyo. Choi, T. Y. and Eboch. K. (1998), "The TQM paradox: Relations among TQM practices, plant performance, and customer satisfaction." Vourrttj/o/Opera//o«5 Management, Vol. 17. No. 1, pp. 59-75. Cua, K. O., McKone, K. E. and Schroeder, R. G. (2001), "Relationship between implementation of TQM, JIT, and TPM and manufacturing performance,"" Journal of Operations Management. Vol. 19, No. 6, pp. 675-694. Das. A., Handfield, 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. 649-690. Deming Prize Committee (2002), The Deming Prize Application Guide. Union of Japanese Scientists and Engineers, Tokyo. Dow. D.. Samson. D. and Ford, S. (1999), "Exploding the myth: Do all quality management practices contribute to superior competitive performance," Production and Operations Management, Vol. 8, No. 1. pp. 1 -27. European Foundation for Quality Management (2003). EFQM Model for Business Excellence, Brussels. Flynn, B. B., Sakakibara, S.. Schroeder, R. G., Bates, K. A. and Flynn, E. J. (1990), "Empirical research methods in operations management," Journal of Operations Management, Vol. 9, No. 2, pp. 250-284. " Flynn, B. B., Schroeder, R. G. and Sakakibara, S. (1995), "The impact of quality management practices on performance and competitive advantage," Decision Sciences, Vol. 26. No. 5, pp.659-691. Flynn, B. B. and Saladin, B. (2006), "Relevance of Baldrige constructs in an international context: A study of national culture," Journal of Operations Management, Vol. 24, No. 2, pp. 583-603. Forza, C. and Flippini. R. (1998), "TQM impact on quality conformance and customer satisfaction: A causal model," International Journal of Production Economics, Vol. 55, No. 1, pp. 1-20. Japan Quality Award (2009). accessed on 20* Sept. 2009. Japan Quality Award Committee Administration (2008). 77?^ Japan Quality Award Assessment Criteria Guidebook 2008 Edition, Japan Productivity Center for Socio-Economic Development, Tokyo. Kaynak, H. (2003), 'The relationship between total quality management practices and their effects on firm performance." Journal of Operations Management. Vol. 21, No. 4, pp. 405-435. Khoo. H. H. and Tan, K. C. (2003), "Managing for quality in the USA and Japan: Differences between MBNQA, DP and JQA."" The TQM Magazine, Vol. 15. No. 1. pp. 14-24. Malcolm Baldrige National Quality Award (2007), Criteria for Performance Excellence. United States Department of Commerce. National Institute of Standards and Technology, Washington. DC. Matsui, Y. (2002), "An empirical analysis of quality management in Japanese manufacturing companies," Decision-Making at the Speed of Light: What is Amiss?. Proceedings of the Seventh Asia-Pacific Decision Sciences Institute Conference. National Institute of Development Administration, Bangkok, Thailand, pp. 1- 18. 35 Phan and Matsui: Relationship hemeen Quality Management Practices and Competitive Performance: Japanese Quality Award Perspective Nair, A. (2006). "Meia-anaKsis of the relationship between quality management practices and firm performance - Implications for qualit\ management theory development." Journal of Operations Management. Vol. 24. No. 6, pp. 948-975. Naor. M.. Linderman. K. and Schroeder. R. (2010). "The globalization of operations in eastern and western countries: Unpacking the relationship between national and organizational culture and its impact on . manufacturing performance."" Journal of Operations Management, Vol. 28. No. 3. pp. 194-205. Nunnally, J. (1967), Psychometric theory, McGraw Hill, New York. Parast, M. M., Adam, S. G.. Jones. E. C.,' Rao, S. S. and Raghu-Nathan, T. S. (2006;, "Comparing quality management practices between the United States and Mexico."" Quality Management Journal. Vol. 13. No. 4. pp. 36-49. Phan. C. A. and Matsui. Y. (2009). "Effect of quality management on competitive performance - International perspective,"" International Journal of Productivity and Quality Management. Vol. 4. No. 2. pp. 153-177. Phan, C. A. and Matsui, Y. (2010). "Contribution of quality management and just-in-time production practices to manufacturing performance," International Journal of Productivity and Quality Management. Vol. 6, No. 1, pp. 23-47. Powell, T. C. (1^995), "Total quality management as competitive advantage: A review_and empirical study," Strategic Management Journal. Vol. 16, No. 1, pp. 15-27. Rungtusanatham, M., Forza, C, Filippini. R. and Anderson, J. (1998), "A replication study of a theory of quality management underlying the Deming management method: Insights from an Italian context," Journal of Operations Management, Vol. 17. No. 1, pp. 77-95. 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. Schniederjans, M. J., Parast. M. M., Nabavi, M., Rao, S. S. and Raghu-Nathan, T. S. (2006). "Comparative analysis of Malcolm Baldrige National Quality Award criteria: An empirical study of India, Mexico, and the United States," Quality Management Journal, Vol. 13, No. 4, pp. 7-21. Schroeder, R. G. and Flynn. B. B. (2001), High Performance Manufacturing: Global Perspectives. John Wiley Sons, New York. Sousa, R. and Voss, C. (2002), "Quality management re-visited: A reflective review and agenda for future research," Journal of Operations Management, Vol. 20, No. 1, pp. 91-109. Vokurka, R. J., Stading, G. L. and Brazeal, J. (2000), *"A comparative analysis of national and regional quality awards,"" Quality Progress, Vol. 33, No. 8, pp. 41-49. Wilson. D. D. and Collier, D. A. (2000). "An empirical investigation of the Malcolm Baldrige National Quality Award causal model," Decision Sciences, Vol. 31. No. 2, pp. 361-390. Yeung, A. C. L., Cheng, T. C. E. and Lai, K. H. (2005), "An empirical model for managing quality in the electronic industry," Production and Operations Management. Vol. 14, No. 2, pp. 189-204. Zhao, X., Yeung, A. C. L. and Lee, T. S. (2004), '"Quality management and organizational context in selected service industries of China," Journal of Operations Management, Vol. 22, No. 6. pp. 575-587. APPENDIX: QUESTIONS ITEMS OF MEASUREMENT SCLES Factor loadings are given in parentheses following each item. Top Management Leadership 1. All major department heads within the plant accept their responsibility for quality (0.72) 2. Plant management provides personal leadership for quality products and quality improvement (0.82) 3. The top priority in evaluating plant management is competitive perfonnance (0.52) 4. Our top management strongly encourages employee involvement in the production process (0.63) 5. Our plant management creates and communicates a vision focused on quality improvement (0.79) 6. Our plant management is personally involved in quality improvement projects (0.77) Strategic Planning 1. Our plant has a formal strategic planning process, which results in a written mission, long-range goals and strategies for implementation (0.87) 2. This plant has a strategic plan, which is put in writing (0.87) 3. Plant management routinely reviews and updates a long-range strategic plan (0.78) Information Analysis \. Charts showing defect rates are posted on the shop floor (0.71) 2. Charts showing schedule compliance are posted on the shop floor (0.71) 3. Charts plotting the frequency of machine breakdowns are posted on the shop floor (0.68) 36 rhan and Matsui: Relationship between Oualit} Management Practices and Competitive Performance: Japanese Quality Award Perspective 4. Information on competitive performance is readily available to employees (0,81) 5. Information on productivity is readiK available to employees (0.76) Customer Irwolvement 1. We frequendy are in close contact with our customers (0.69) 2. Our customers seldom visit our plant (removed) 3. Our customers give us feedback on our quality and dehvery performance (0.70) 4. Our customers are actively involved in our product design process (0.58) * 5. We strive to be highly responsive to our customers" needs (0.72) 6. We regularly survey our customers' needs (0 71) Small Group Problem Solving 1. During problem solving sessions, we make an effort to get all team members' opinions and ideas before making a decision (.64) 2. Our plant forms teams to solve problems (0 80) 3. In the past three years,jnany problems have been solved through small group sessions (0.78) , 4. Problem solving teams have helped improve manufacturing processes at this plant (0.78) 5. Employee teams are encouraged to try to solve their own problems, as much as possible (0.65) 6. We don't use problem solving teams much, in this plant (0.72) Task-Related Training for Employees * % 1. Our plant employees receive training and development in workplace skills, on a regular basis (0.87) 2. Management at this plant believes that continual training and upgrading of employee skills is important (0.76) 3. Employees at this plant have skills that are above average, in this industry (0.58) 4. Our employees regularly receive training to improve their skills (0.89) 5. Our employees are highly skilled, in this plant (removed) Employee Suggestions 1. Management takes all product and process improvement suggestions seriously (0.82) 2. We are encouraged to make suggestions for improving performance at this plant (0.77) 3. Management tells us why our suggestions are implemented or not used (0.76) 4. Many useful suggestions are implemented at this plant (0.82) 5. My suggestions are never taken seriously around here (0.72) 6. The plant has an informal strategy, which is not very well defined (0.67) Cleanliness and Organization ^ I. Our plant emphasizes putting all tools and fixmres in their place (0.69) ^ 2. We take pride in keeping our plant neat and clean (0.85) 3. Our plant is kept clean at all times (0.86) 4. Employees often have trouble finding the tools they need (0.57) 5. Our plant is disorganized and dirty (0.79) Process Control 1. Processes in our plant are designed to be "foolproof (0,75) 2. A large percent of the processes on the shop floor are currenfly under statistical quality control (0.84) 3. We make extensive use ofstatistical techniques to reduce variance in processes (0.81) 4. We use charts to determine whether our manufacturing processes are in control (0.70) 5. We monitor our processes using statistical process control (0.87) Preventive Maintenance 1. We upgrade inferior equipment, in order to prevent equipment problems (0.71) 2. In order to improve equipment performance, we sometimes redesign equipment (0.55) 3. We estimate tiie lifespan of our equipment, so that repair or replacement can be planned (0,74) 4. We use equipment diagnostic techniques to predict equipment lifespan (0.75) 5. We do not conduct technical analysis of major breakdowns (0.55) Supplier Quality Involvement 1. We strive to establish long-term relationships with suppliers (0.64) 2. Our suppliers are actively involved in our new product development process (0.72) 3. Quality is our number one criterion in selecting suppliers (0.55) 4. We use mosUy suppliers that we have certified (0.61) 5. We maintain close communication with suppliers about quality considerations and design changes (0.80) 6. We actively engage suppliers in our quality improvement efforts (0.77) 7. We would select a quality supplier over one with a lower price (removed) 37