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Management Research Review Relationship between quality management information and operational performance: International perspective Phan Chi Anh Yoshiki Matsui Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) Article information: To cite this document: Phan Chi Anh Yoshiki Matsui, (2011),"Relationship between quality management information and operational performance", Management Research Review, Vol 34 Iss pp 519 - 540 Permanent link to this document: http://dx.doi.org/10.1108/01409171111128706 Downloaded on: 12 May 2015, At: 07:14 (PT) References: this document contains references to 37 other documents To copy this document: permissions@emeraldinsight.com The fulltext of this document has been downloaded 1955 times since 2011* Users who downloaded this article also downloaded: Kevin Baird, Kristal Jia Hu, Robert Reeve, (2011),"The relationships between organizational culture, total quality management practices and operational performance", International Journal of Operations & Production Management, Vol 31 Iss pp 789-814 http://dx.doi.org/10.1108/01443571111144850 Roy Staughton, Robert Johnston, (2005),"Operational performance gaps in business relationships", International Journal of Operations & Production Management, Vol 25 Iss pp 320-332 http:// dx.doi.org/10.1108/01443570510585525 R Saravanan, K.S.P Rao, (2007),"The impact of total quality service age on quality and operational performance: an empirical study", The TQM Magazine, Vol 19 Iss pp 197-205 http:// dx.doi.org/10.1108/09544780710745621 Access to this document was granted through an Emerald subscription provided by 514603 [] For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all Please visit www.emeraldinsight.com/authors for more information About Emerald www.emeraldinsight.com Emerald is a global 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Vietnam and Faculty of Business Administration, Yokohama National University, Yokohama, Japan, and Yoshiki Matsui International Graduate School of Social Sciences, Faculty of Business Administration, Yokohama National University, Yokohama, Japan Abstract Purpose – The purpose of this paper is to examine whether quality management information (QMI) can be a source of competitive advantage and should be managed strategically Design/methodology/approach – Analysis of variance and regression techniques were applied to the database of the high-performance manufacturing (HPM) project to analyze the differences and similarities existing across the countries on the degree of implementation of QMI practices and their contribution to operational performance of manufacturing plants Findings – The results of statistical analysis indicate significant differences in the implementation of QMI practices across the countries This study highlights the important role of QMI in Japanese plants where shop-floor and cross-functional communication and information sharing practices significantly impact on different dimensions of operational performance Practical implications – This study suggests that HPM could be achieved by the implementation of a set of communication and information sharing practices in shop-floor and cross-functional levels of manufacturing plants Originality/value – Although scholars considered information as one dimension of quality management, existing quality management literature provides little empirical evidence on the relationship of QMI and operational performance of manufacturing plants This paper fills the gap by introducing a comprehensive research framework to analyze the communication and information sharing practices in the shop-floor and cross-functional levels Keywords Quality management, Management information systems, Operations and production management Paper type Research paper Introduction Quality management information (QMI) refers to the systematic collection and analysis of data in a problem-solving cycle to identify critical problems, find their root causes, and generate solutions to the problems Effective implementation of QMI allows the Management Research Review Vol 34 No 5, 2011 pp 519-540 q Emerald Group Publishing Limited 2040-8269 DOI 10.1108/01409171111128706 MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 520 manufacturers to improve product and service quality and facilitate their supplier relationship management (Flynn et al., 1994; Forza and Flipini, 1998; Kaynak, 2003; Morita et al., 2001; Schniederjans et al., 2006) Recently, greater attention has been paid to QMI by such international standards and awards as ISO 9000, Malcom Baldrige National Quality Award, and Japan Quality Award Although scholars considered information as one dimension of quality management, existing quality management literature provides little empirical evidence on the relationship of QMI practices and operational performance of manufacturing plants This study aims to fill this gap by responding to the following questions: What are similarities and differences in the perception of QMI practices across countries? Do QMI practices positively relate to various dimensions of operational performance of manufacturing plants such as quality, cost, delivery, flexibility, etc? To be competitive in global market, many manufacturing companies have implemented a set of practices such as total quality management (TQM), just in time (JIT), and total productive maintenance (TPM) that hereafter broadly labeled as high-performance manufacturing (HPM) initiatives HPM literature indicates that effective implementation of such HPM practices highly depend on how the companies manage the communication and information flow This study examines QMI by introducing a set of communication and information sharing practices at shop-floor and cross-functional levels of manufacturing plants These practices reflect various types of communication and interaction within shop floor and between functions/departments of manufacturing plants such as information feedback, suggestions, training, small group activities, cross-functional product design, coordination of decision between departments, etc This study utilizes survey data which have been gathered from 167 manufacturing plants in six countries during 2003-2004 in the framework of HPM project The statistical results indicate the significant difference in the perception of the QMI practices across the countries Plants in the USA and Sweden show their stronger emphasis on QMI practices than other plants, particularly those in Japan and Italy All the countries except Japan and Korea place their higher attention on cross-functional practices than shop-floor practices The significant difference among countries in the effect of QMI practices on performance is detected The connection between the QMI practices and high performance in Japanese plants appears tight, comparing with other countries These findings are consistent with the institutional theory when the institutions are taken to be the countries National culture, geographical specifics, and competitive environment may account for the differences we observe in communication and information sharing practices across the countries The linkage between QMI and operational performance found in this study suggests that HPM could be obtained by implementing a set of communication and information sharing practices The remaining of this paper presents the literature and research framework, which are followed by the descriptions of data collection, measurement test, and hypothesis testing The last three sections discuss on the important findings, the limitations of this research, and the final conclusions Literature review The impact of QMI on performance has been widely investigated by scholars (Flynn et al., 1994; Forza and Flipini, 1998; Morita et al., 2001; Kaynak, 2003; Schniederjans et al., 2006) Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) Flynn et al (1994) indicate that process management strongly depends on how process’s owner collect and analyze data at the source to take immediate problem-solving action Quality performance data such as defect rate, scrap, and rework must be collected, analyzed, shared, and used for quality improvement Design quality also depends on QMI because QMI provides a wide range of data from purchasing, marketing, manufacturing, design, customers, and suppliers in order to design quality into products To support suppliers for improving product quality, manufacturing plants need to create a database about the suppliers’ performance regarding quality, delivery, purchasing cost, etc so that managers and employees can identify and solve problems from materials and parts supplied and provide the suppliers timely and important feedbacks to improve their performance (Kaynak, 2003) In summary, empirical studies on quality management emphasize importance of QMI as follows: timely quality measurement; feedback of quality data to employees and managers for problem solving; evaluation of managers and employees based on quality performance; and availability of quality data Recently, researchers find that systematic management of information and data resource is also important to the use of advanced quality management methods such as Six Sigma, which is itself a data-driven approach to eliminate defects and wastes in business processes Researchers agree that the execution of Six Sigma relies on the availability and accuracy of QMI because quality metrics can only be used for quality improvement when they are calculated from reliable and valid data (Zu et al., 2008) To successfully implement QMI practices, many requirements need to be satisfied as indicated from empirical literature Effective QMI directly depends on customer focus, workforce management, and top management support Workforce management is considered as infrastructure for quality management and it facilities the collection and use of QMI by increasing employee’s continuous awareness of quality-related issues and empowering employees in quality decision making Close contact with customers, frequent visit to customers, and customer surveillance allow the firm to obtain product and service quality information and use it for further quality improvements For manufacturing organization, QMI is a critical issue influencing its long-term viability However, little empirical research has been conducted with the international perspective of QMI even in manufacturing sectors (Parast et al., 2006) Early studies on international comparison of quality management mainly focused on comparing the quality practices between the USA and Japan (Garvin, 1986; Flynn, 1992) Recently, the scope of international comparison of quality management has been extended to study the quality practices in other countries and regions around the world (Madu et al., 1995; Rao et al., 1997; Flynn and Saladin, 2006; Phan and Matsui, 2009) Most of these studies use different frameworks, instruments, and constructs for measuring and comparing quality management practices across the countries As discussed in the literature, the question regarding the “universal applicability” of quality management has not been fully answered, and more empirical studies on international comparison of quality management are needed (Sila and Ebrahimpour, 2003; Rungtusanatham et al., 2005) QMI and operational performance 521 MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 522 Research framework QMI improves quality performance through collecting, storing, analyzing, and reporting information on quality to assist decision makers at all level This concept requires input from a variety of functional areas and recognized that information consists of not only data but also other knowledge needed for decision making ( Juran and Gryna, 1980; Forza, 1995) Schroeder and Flynn (2001) argue that successful implementation of variety of manufacturing management practices such as TQM, JIT, and TPM depend on how the manufacturing plants develop their horizontal linkage structure throughout the communication network The “communication and action” process is one of the underlying forces that have made such practices as TQM and JIT so successful While most of quality management literature have emphasized on the importance of availability, accuracy, and timeliness of QMI, this study focuses on how the manufacturing plants develop QMI through facilitating communication and information sharing practices to achieve HPM The flow of communication and information sharing is distinguished into two categories: shop-floor and cross-functional levels Shop-floor QMI concentrates on the collection, analysis, and feedback of quality information on the shop floor where products are created It relates with two-way communications between managers/engineers and workers and between workers themselves Conducting small group activities is the means for employees to share their ideas and expertise for quality improvement In addition, along with the feedback of quality performance, employee’s suggestions should be formally acknowledged to encourage the employee’s participation in quality improvement Cross-functional QMI, on the other hand, relates with communication and information sharing between functions/departments concerning with coordination, new product development efforts, and the interaction with customers and suppliers Communication and information sharing between different functions are important for making quality decisions especially to solve critical quality problems External communication with customers and suppliers is also crucial for quality management Close contact with customers, frequent visit to customers, and regular customers’ survey are the best ways to capture customers’ needs and expectations while sharing information with suppliers improves their mutual trust within the supply chain The framework of this study is simply shown in Figure Prior to examining the linkage between QMI and operational performance, this study empirically compares the degree of implementation of QMI practices across the countries This is important as we can determine whether QMI depends on the contextual factors such as national culture or geographical specifics Some scholars Shop-floor quality management information practices Figure Research framework Cross-functional quality management information practices Operational performance Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) argue that, with the evolvement and spreading of modern technologies, benchmarking, organizations may design their operational structure in similar ways in order to be efficient and effective (Form, 1979) Other scholars, however, indicate the linkage between information and national specifics (Wacker and Sprague, 1998; Snell and Hui, 2000) More recently, Flynn and Saladin (2006) point out that such component of quality management as QMI would be influenced by Hofstede national culture values The power distance, individualism, masculinity, and uncertainty avoidance may affect the use of information to support decision making For example, high power distance cultures may restrict learning opportunities to high-status members and discourage open access to information and information sharing between different organizational levels Members of collectivist national cultures are more likely to rely on information provided though teamwork and cross-functional collaboration Because of a lack of development of valid instruments on QMI, the results of previous QMI studies cannot be generalized The question regarding the universality of QMI and its linkage with performance has not been answered More empirical and cross-country research is needed in QMI study Then, we establish comprehensive instruments on QMI and test whether country location influences the implementation of QMI practices The first hypothesis is presented as follows: H1 There is difference in the implementation of QMI practices across the countries The contribution of communication and information sharing to quality performance or supply chain performance has been identified in the existing literature (Forza, 1995; Carr and Kaynak, 2007) The use of bilateral relations, including lateral forms of communication and joint decision-making processes increases information systems capacity This permits problems to be solved at the level where they occur, rather than being referred upward in the hierarchy, increasing the capacity of the organization to process information and make decisions by increasing the discretion at lower levels of the organization (Phan and Matsui, 2009) Flynn and Flynn (1999) suggest that the use of lateral relations would moderate the adverse impact of environmental complexity, thereby improving manufacturing performance We assume that, shop-floor QMI is a critical element for process control and improvement The application and results of statistical process control need to be intensively discussed and shared on the shop floor to solve the problems Process variation and quality problems should be detected, analyzed, controlled, and eliminated through several activities such as shop-floor information feedback, interaction between managers/engineers and workers, small group activities, etc As cited in the existing literature, the reduction of defective products leads to a reduction of time delay for rework, inspection, and time for machine stop These allow the production run faster with shorter consuming time from material receiving to customer delivery Thus, shop-floor QMI practices would relate with the various dimensions of operational performance: product cost, on-time delivery, and flexibility to change the production volume Cross-functional QMI, in other way, would contribute to design quality and new product development lead time Fast identification of customer’s expectations and translating those expectations into product specifications requires intensive interaction with customers in various channels such as web/fax/phone contacts, survey, or direct visits The reduction of time-to-market and improvement of the design quality would be achieved though the cross-functional products design effort This is an overlap design/engineering practice that includes QMI and operational performance 523 MRR 34,5 524 all functions from the beginning of new product development project Suppliers can be regarded as an external process of the plants Collaboration with suppliers through opening and sharing information concerning quality problems and design changes would also allow the plants to improve product quality and save production cost The hypothesis on the relationship between QMI practices and operational performance, therefore, is presented as follows: H2 QMI practices positively relate to operational performance Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) To test the hypotheses, analysis of variance (ANOVA) and regression analysis are used to compare those practices across the countries and identify whether QMI significantly impact 13 operational performance indicators Research variables From literature reviewing, ten measurement scales are developed to examine QMI under two perspectives: shop floor and cross-functional as mentioned early Shop-floor QMI includes six measurement scales as follows: (1) Feedback – measures whether the plant provides shop-floor personnel with information regarding their performance (including quality and productivity) in a timely and useful manner (2) Shop-floor contact – measures the level of interaction between managers, engineers, and workers, on the shop floor A high degree of interaction between management and workers is thought to promote problem solving and general improvement (3) Employee suggestions – measures employees’ perception regarding management’s implementation and feedback on employee suggestions (4) Small group problem solving – evaluates how the plant uses teamwork activities to solve quality problems (5) Supervisory interaction facilitation – measures whether supervisors successfully encourage workers works as team, including expressing their opinions and cooperating with each other to improve production (6) Multi-functional employees – determines if employees are trained in multiple task/areas; that is, received cross-training so that they can perform multiple tasks or jobs Cross-functional QMI includes four measurement scales as follows: (1) Coordination of decision making – determines cross-functional cooperation and communication in the plants (2) Cross-functional product design – measures the level about amount of input that the manufacturing function has in the new product introduction process This includes cooperation and input into process across functional boundaries (3) Communication with customers – assesses the level of customer contact, customer orientation, and customer responsiveness (4) Communication with suppliers – assesses whether plants develop trust-based relationship with suppliers by exchanging communication and sharing information Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) A total of 13 measurement items are used to evaluate different dimensions of operational performance of the plants: 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 items are summed up to form overall operational performance Because the objective of this study is to identify impacts of QMI practices on operational performance that can be generalized across countries and industries, the effects of country and industry need to be removed prior to evaluating the relationship between QMI practices and operational performance We, therefore, include the following control variables in the regression analyses Five country control variables: USA (the USA compared to Japan), ITA (Italy compared to Japan), SWE (Sweden compared to Japan), KOR (Korea compared to Japan), and AUT (Austria compared to Japan) are used to represent the five countries Similarly, two industry control variables, MAC (machinery industry compared to automobile industry) and EE (electric and electronics industry compared to automobile industry), are used to represent the three industries from which the data were collected Data collection This study explores data gathered through the international joint research initiative called High-Performance Manufacturing (HPM) Project started in 1980s by researchers at the University of Minnesota and Iowa State University The overall target of this 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 was conducted in 1989 gathering information from 46 US manufacturing plants In 1992, the project was expanded to include researchers from Germany, Italy, Japan, and the UK The second round of the survey gathered data from 146 manufacturing plants from the above countries In 2003, the project was 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 210 except Spanish plants Within each country, surveyed are plants with more than 100 employees belonging to one of three industrial fields – electrical and 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-WCM” 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 In this research, the authors can acquire data from 167 manufacturing plants in six countries: the USA, Japan, Italia, Sweden, Austria, and Korea during 2003-2004 The key characteristics of these plants are summarized in Table I In each plant, the degree of implementation of QMI practices and continuous improvement and learning is evaluated by nine positions such as direct workers, supervisors, process engineer, quality manager, production control manager, inventory manager, human resource manager, plant superintendent, and a member of new product development team as summarized in Table II Ten QMI measurement scales are constructed by four to six question items evaluated on a seven-point Likert scale (1 – strongly disagree, QMI and operational performance 525 MRR 34,5 – neither agree nor disagree, and – strongly agree) The individual question items are shown in the Appendix Finally, 13 operational measures of manufacturing plants are judged by the plant manager Each plant manager is 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 (1 – poor or low end of the industry, – average, and – superior or top of the industry) Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 526 Measurement analysis The first step of analytical process is the analysis of reliability and validity of ten measurement scales and two super-scales In this study, Cronbach’s alpha coefficient is calculated to evaluate the reliability of each measurement scale Table III shows the alpha values for all of ten scales exceeded the minimum acceptable alpha value of 0.60 for pooled sample and country-wise Most of the scales have the alpha value above 0.75 indicating that the scales were internally consistent: Content validity An extensive review of literature and empirical studies is undertaken about quality management and organization performance to ensure content validity USA Table I Demographic of survey respondent Electrical and electronic Machinery Automobile Total Plant characteristics Average market share (%) Average sale ($000) Average of number of employee (salaried person) 11 29 25.50 284,181 10 12 13 35 Feedback Shop-floor contact Supervisory interaction facilitation Employee suggestions Multi-functional employees Small group problem solving Coordination of decision making Cross-functional product design Communication with suppliers Communication with customers Operational performance 474 PD Italy Sweden 10 10 27 33.05 1,118,492 153 Measurement scales Table II Survey respondents Japan HR 10 24 Austria Korea Total 10 10 11 31 56 60 51 167 10 21 23.38 71,209 34.80 584,371 20.00 64,470 31.54 2,266,962 296 348 122 2,556 Positions to answer questionnaire DL IM PE QM SP 6 1 1 6 1 PM 1 6 PS 1 4 4 4 4 1 1 1 Notes: DL, Direct labor; PM, plant manager; PD, member of new product development team; HR, human resource manager; QM, quality manager; PS, plant superintendent; IM, inventory manager; SP, supervisor; PE, process engineer Scales Feedback (FDB) Shop-floor contact (SFC) Supervisory interaction facilitation (SIF) Employee’s suggestions (ESG) Small group problem solving (SPS) Cross-functional training (CFT) Coordination of decision making (CDM) Cross-functional product design (CPD) Communication with supplier (CSP) Communication with customer (CCS) Super scales Shop-floor quality information (SQMI) Cross-functional quality information (CQMI) Measurement items 0.812 0.632 0.624 0.604 0.601 0.623 0.648 0.724 0.478 0.535 0.521 0.451 4.972 5.245 5.131 5.149 5.097 5.297 5.226 4.817 5.463 5.260 5.151 5.192 Descriptive Mean SD 0.88 0.64 0.76 0.60 0.76 0.85 0.85 0.84 0.73 0.79 0.65 0.66 USA 0.88 0.84 0.78 0.64 0.75 0.81 0.81 0.82 0.78 0.70 0.68 0.68 Japan 0.84 0.62 0.80 0.69 0.80 0.80 0.80 0.78 0.75 0.70 0.69 0.78 Sweden 0.91 0.89 0.74 0.70 0.71 0.69 0.69 0.77 0.69 0.71 0.79 0.77 0.83 0.69 0.88 0.60 0.82 0.86 0.86 0.76 0.77 0.75 0.82 0.75 Cronbach’s alpha Korea Italy Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 0.90 0.81 0.80 0.72 0.83 0.86 0.86 0.76 0.80 0.81 0.86 0.67 Austria 0.88 0.74 0.79 0.64 0.79 0.82 0.83 0.79 0.74 0.74 0.71 0.69 Pooled sample QMI and operational performance 527 Table III Measurement test MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 528 Construct validity Construct validity is conducted to ensure that all question items in a scale all measure the same construct Within-scale factor analysis is tested with the three criteria: uni-dimensionality, a minimum eigenvalue of 1, and item factor loadings in excess of 0.40 The results of measurement testing for the pooled sample and country-wise show that all scales had well construct validity The eigenvalue of the first factor for each scale is more than two Factor loading for each items are more than 0.40, mostly range between 0.70 and 0.90 for the pooled sample as shown in the Appendix Hypothesis testing This section starts with the analysis of country effect existed in QMI practices One-way ANOVA is used to identify the similarities and differences in QMI practices across the countries The last two columns of Table IV show the values of the F-statistic and their significant levels If we set the set significant level at percent, the ANOVA test results suggest that all of QMI practices are significantly different across the countries except employee suggestions Next, Tukey pairwise comparison tests of mean differences are conducted to identify how QMI practice differed between each pair of countries We observe that the largest differences exist in such practices as supervisory interaction facilitation, cross-functional product design, coordination of decision making, communication with suppliers, and communication with customers The Japanese and US plants are quite similar in almost of the practices except multi-functional employees and communication with customers In addition, QMI practices are evaluated in similar way in two Asian countries In general, shop-floor QMI practices are lowest in Italy and highest in Austria and Korea, while cross-functional QMI practices are lowest in Japan and highest in Austria and the USA In the USA, an Italian plants, the focus of cross-functional QMI practices are appeared higher than shop-floor QMI while both of them are similar in Japanese and Korean plants It is found that the most focused practices (top practices) of QMI practices are different between the countries: communication with customer (in the USA), multi-functional employees (in Sweden), coordination of decision making (in Austria), shop-floor contact (in Korea), and employee suggestions (in Japan) In summary, the results of ANOVA test suggest that QMI practices vary widely by country Each country evaluated the degree of implementation of QMI practices in different ways National culture, geographical specifics, and competition environment and other factors may account for the differences we observed among QMI practices adopted in different countries As the result, we would like to accept H1 and state that there is significant difference in QMI practices across the countries Primary relationship between ten QMI practices and 13 operational performance measures is identified by the binary correlation analysis that conducted in pooled and countries-wise samples as show in Table V It has 130 cells, each corresponding to a pair of one QMI practices and one operational indicator The cells include initials of the countries for which significant correlations are found between the practices and the performance indicators We observe that linkage between QMI practices and performance in Japanese plants exhibits closer than the one in other countries if we set the significant level at 0.5 percent as suggested in literature Out of 130, the number of pair of significant correlation in Japanese case is 43 This number is 14, 13, 10, 8, 7, and 82 in Korea, Austria, Italy, Korea, US, Sweden, and pooled samples, respectively It is observed that QMI practices are highly associated with on-time delivery, 5.323 5.191 5.157 4.604 4.982 4.476 4.808 4.867 5.168 4.912 4.533 5.125 5.624 5.262 5.507 5.275 5.261 5.352 5.578 5.710 5.016 5.170 F 2.568 4.976 7.946 1.874 (U vs I) 3.008 ( J vs I), ( J vs S), ( J vs A), and (S vs K) 5.531 (U vs A), ( J vs A), (S vs K), (S vs I), and (I vs A) 5.721 (U vs J), (U vs S), (U vs K), (U vs A), and (S vs I) 5.630 (U and J), (U and K), ( J and S), ( J and I), and ( J and A) 7.870 (U and J), (U and K), (U and I), ( J and S), (I and A), and (K and A) 8.304 (I vs A) (K vs I) and (I vs A) (U vs I), ( J vs I), (S vs I), (K vs I), and (A vs I) 5.561 4.891 5.139 5.471 5.163 5.286 5.016 5.188 5.089 4.871 4.827 Communication with customers 4.980 5.372 5.503 5.242 5.215 5.639 5.547 5.146 5.394 5.047 5.144 5.292 5.091 5.327 5.457 5.103 4.366 5.190 Feedback Shop-floor contact Supervisory interaction facilitation Employee suggestions Small group problem solving Multi-functional employees Coordination of decision making Cross-functional product design Communication with suppliers 4.843 5.087 5.140 5.204 4.920 4.957 5.177 5.015 4.698 USA Japan Sweden Korea Italy Austria Pair-wise differences QMI practices Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 0.000 0.020 0.000 0.000 0.087 0.008 0.000 0.000 0.000 0.000 Sig QMI and operational performance 529 Table IV Quality information practices across countries P P P P P P P, U P, I, S S CPS P, P, P, P, P, P, P, P, P, P, A, I U A A, I A, I A, K A, J, U I U J, U OTD P P P P, J P, J P P P FDL P K P FPM P, P, P, P, P P, P, P, P, P, J, K J, K J, K, S A, I, J J J J J J FCV Notes: U, USA; J, Japan; S, Sweden; K, Korea; I, Italy; A, Austria; P, pooled sample P, S P, J P J P, J P, J P Feedback Shop-floor contact Supervisory interaction facilitation Employee suggestions Small group problem solving Multi-functional employees Coordination of decision making Cross-functional product design Communication with suppliers Communication with customers Table V Correlation between quality information practices and operational performance indicators UCM I, J J J P P P ITO J, S J P P P CTM P, J P, I, J, K A, J, K P, A, J P, J K J P, J P, K NDT P I J P P PCP P, J, K A, K, U P P P, K P, K P, A, K, U P, I, J, K A, J P, J OPL 530 QMI practices Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) P, U P, J P, S P P P P, J A, J A, J PIN P, P, P, P P, P, J P, P, J J J J J J J, S CSS MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) flexibility to change volume, new product develop lead time, and on-time new product launch In case of Japanese and pooled samples, QMI practices significantly correlate with every performance indicators The number of performance indicators significantly correlate with QMI practices is 11, 6, 5, 5, 4, and 4, in Japanese, Sweden, Austrian, Korean, US, and Italian samples, respectively To formally test the impact of shop-floor and cross-functional QMI practices on operational performance, further regression analysis was conducted Regression model is formulated using SQMI and CQMI as two dependent variables along with seven dummy variables Table VI presents the regression on overall operational performance (delivered by summarizing 13 individual operational performances) using pooled sample of 167 cases If we consider the value of adjusted R as the indicator for explanation power of the model, regression result indicates that both SQMI and CQMI can explain 13.5 percent variability of operation performance Between two independent variables, CQMI is found as significant predictor for operational performance Although correlation analysis suggests the country-difference on impact of individual QMI practices on individual operational performances, regression analysis rather indicates the non-significant difference in the determinants of operational performance between Japan and other countries To confirm this finding with more formal statistical evidence, additional regression analysis is required to check whether the coefficients in a particular regression model are the same for the samples of different countries, after dividing the pooled sample into six sub-samples representing each country What is required is to compare an estimated regression model including two measurement scales as independent variables for the QMI and operational performance 531 Coefficients and significant level (Constant) USA SWE KOR ITA AUT MAC EE SQMI CQMI US*CQMI US*SQMI SWE*SQMI SWE*CQMI KOR*CQMI KOR*SQMI ITA*SQMI ITA*CQMI AUT*CQMI AUT*SQMI R2 Adjusted R F and p Note: Model using dummy country and industry variables 1.306 (0.128) 2.431 (0.026) 2.195 (0.039) 1.493 (0.112) 2.029 (0.044) 1.847 (0.050) 0.104 (0.147) 0.006 (0.477) 0.165 (0.356) 0.694 (0.036) 2.453 (0.130) 0.009 (0.498) 1.221 (0.264) 1.357 (0.253) 2.300 (0.174) 0.709 (0.394) 0.366 (0.420) 2.497 (0.106) 2.332 (0.137) 0.291 (0.441) 0.244 0.135 2.242 (0.002) Table VI Regression analysis on relationship between quality information practices and operational performance MRR 34,5 532 pooled sample with the corresponding model applied for six sub-samples In estimating the regression models for the sub-samples, no restrictions are imposed on the values of regression coefficients so that every coefficient can take different values for different countries We can evaluate the improvement in explanatory power by dividing the pooled sample into six sub-samples and enabling regression coefficients to take different values by an F-test (Chow, 1960): RSSR SSSRi ị=k ; F statistic ẳ SSSRi =ðn i*kÞ where: Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) RSSR is the sum of squared residuals from a linear regression of the pooled sample SSRi is the sum of squared residuals from a linear regression of sub-sample i i is the number of subgroup k is number of independent variable n is number of total observations Table VII shows regression analysis on relationship between QMI practices and operational performance taking on pooled and country-wise sample using SQMI and CQMI as two independent variables We obtain value of F-statistic is 3.654 with p-value is 0.008 If we setting significant level at percent, the results of Chow test indicate the difference on determinant of operational performance across the countries In summary, we can accept H2 and state that QMI practices significantly impact operational performance In addition, statistical results reveal that this impact widely varies across the countries and the linkage between QMI and performance in Japanese plants appears closer than others Discussions Results of the present study show that the differences on QMI practices existed in manufacturing plants operating in different countries The degree of implementation of each QMI practices and their linkage with specific operational performance indicators appear differently across the countries Although this has been previously implied in the quality management literature, comparison of a comprehensive list of QMI practices among countries was lacking We obtained the mixed results when the QMI practices were compared across six countries First, we find that all the QMI practices are significantly different across the countries (except employee suggestion) The USA and three other European countries place their higher focus on cross-functional QMI practices rather than on shop-floor QMI practices Plants in the USA, Austria, and Sweden show their stronger emphasis on every QMI practice than other plants in Italy and Japan Second, we find the linkage between QMI practices with different dimensions of operational performance rather than with quality performance only For example, statistical analysis reveals that QMI practices closely linked with time-based performance indicators of manufacturing plants, for example: on-time delivery (in Austria, the USA, and Italy), on-time new product launch (in Korea), and new product development lead time (in Japan) Cross-functional product design is found as the most (Constant) SQMI CQMI R2 Adjusted R F and p Chow test F and p: 3.654 (0.008) 1.386 0.139 0.251 0.136 0.124 11.688 (0.000) (0.002) (0.120) (0.018) Pooled sample 2.376 (0.050) 0.137 (0.325) 0.109 (0.362) 0.053 20.030 0.641 (0.267) USA 0.945 (0.240) 0.318 (0.235) 0.107 (0.403) 0.173 0.094 2.192 (0.137) South Korea 1.067 0.053 0.551 0.358 0.315 8.348 (0.001) (0.188) (0.430) (0.039) Japan 1.985 20.194 0.409 0.105 0.020 1.231 (0.156) (0.074) (0.237) (0.070) Sweden Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) Austria 1.528 (0.135) 0.322 (0.217) 0.131 (0.374) 0.189 0.081 1.750 (0.104) QMI and operational performance Italy 1.485 (0.88) 0.227 (0.195) 0.179 (0.249) 0.139 0.067 1.934 (0.084) 533 Table VII Regression analysis on relationship between QMI practices and operational performance taking on pooled and country-wise sample MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 534 critical factor for these performances In general, employee suggestions, coordination of decision making, and cross-functional product are found highly associated with operational performance of plants in the six countries Third, the significant difference between countries in the linkage of individual QMI practices on specific performance indicators is detected We observed that the connection between the QMI practices and high performance in Japanese plants appears tight, comparing with other countries Japanese plants with high performance highly focus on shop-floor contact, small group problem solving, and feedback The findings on significant differences across the countries are consistent with the institutional theory when the institutions are taken to be the countries National culture, geographical specifics, and competitive environment may account for the differences that we observed in communication and information sharing practices across the countries In addition, the finding of our study highlights the Japanese quality management The prosperity and survival of Japanese manufacturers are archived by their Japanese way of management such as TQM, JIT production, TPM, concurrent engineering, and their ability to create horizontal linkage structure throughout the communication network Those are the real strengths of Japanese manufacturers, besides of their technological advantages The “communication and action” process is one of underlying forces that have made such practices as TQM and JIT so successful (Morita et al., 2001) For the researchers and practitioners, this study provides the evidence on how performance is associated with communication and information sharing in the plants Managers who want to improve selected operational performance indicators can find some valuable suggestions from the statistical analysis results For example, in Japanese plants, high performance in term of manufacturing cost and volume flexibility relates with the implementation of such shop-floor QMI practices as feedback, shop-floor contact, supervisory interaction facilitation, and employee suggestions Improvement of inventory turnover and reduction of new product development lead time would be achieved by implementation of such cross-functional QMI practices as cross-functional product design, and communication to suppliers and communication to customers Because the quality, cost, delivery, and flexibility performances are closely correlated, benefits of QMI practices sometimes have multiple effects on operational performance Regression analysis on the pooled sample shows that cross-functional QMI is significant predictor for operational performance This suggests that the emphasis on communication and information crossing the borders of functions would explain the difference on competitive position of manufacturing plants This study contributes to quality management literature as it refines our understanding of the nature of relationship between QMI practices and plant performance Continuing to use HPM perspective to study QMI, we provide further insight on the achievement of high performance through communication and information sharing This study introduces a comprehensive research framework to study QMI and uses the latest database to test the hypotheses Our findings are in line with previous studies on QMI using HPM perspectives such as Forza and Salvador (2001), Schroeder and Flynn (2001) and Flynn and Saladin (2006) In addition, we find that operational performance would be influenced by such QMI components as shop-floor contact or supervisory interaction facilitation, which have not been fully studied in previous works Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) Limitation and future research It is important to view this research in the context of its limitations Methodologically, this study is based on cross-sectional survey research data It utilizes database gathered from self-reported questionnaires, and individual bias in reporting may exist Although we address the issue of common method bias through the use of multiple respondents, the study heavily relies on the use of perceptual data The other issue is sample size Because time and resources constrains, the sample consist of only 167 plants belonging to three industries These restrict the scope of the studies and utilization of some data analysis techniques For example, the relative small sample size not allows the authors to use path analysis technique to examine interrelations among specific QMI practices and operational performance with industry and country effects Next is the issue relates with evaluation of operational performance The HPM collected both objective and subjective data on operational performance of manufacturing plants in all of member countries The objective measures of operational performance on quality, cost, and delivery have been collected such as “percentage of scrap and rework”, “manufacturing cost”, “percentage of on-time delivery”, etc However, because of industrial difference; these objective data on performance cannot be fully used in this study Therefore, the subjective measures are used to evaluate operational performance in this study Other studies in framework of HPM projects also encountered this issue (Flynn et al., 1995; Ahmad et al., 2003; Matsui and Sato, 2002; Phan and Matsui, 2009) To overcome above-mentioned limitations, a future research should be conducted with larger and comprehensive sample size This will allow the researchers to use comprehensive techniques for investigating relationship between management practices and performance for specific countries or specific industries, such as path analysis or structural equation modeling Researchers should explore both objective measures and subjective measures in their studies, particularly when focusing on a specific industry Conclusions In the previous sections, we presented the results of the empirical study on the relationship between QMI and operational performance in manufacturing plants A simple analytical framework and two hypotheses were proposed Then, based on the literature, we introduced ten measurement scales and two super scales and all of those measurement scales are satisfactory in terms of reliability and validity for the dataset of 167 manufacturing plants in six countries Using these scales, we examined the country effect on QMI to explore the critical success factors of operational performance The results indicate the similarities and differences of the implementation of QMI and its impact on operational performance across countries This study suggests that manufacturing plants should develop the process and network of shop-floor and cross-functional communication and information sharing which is an underlying force that have made manufacturing management practices became successful and contribute to their competitive performance It also highlights the unique and distinguished position of Japanese manufacturers in the impact of QMI on operational performance References Ahmad, S., Schroeder, R.G and Sinha, K.K (2003), “The role of infrastructure practices in the effectiveness of JIT practices: implications for plant competitiveness”, Journal of Engineering and Technology Management, Vol 20 No 3, pp 161-91 QMI and operational performance 535 MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 536 Carr, A and Kaynak, H (2007), “Communication methods, information sharing, supplier development and performance: an empirical study of their relationships”, International Journal of Operations & Production Management, Vol 27 No 4, pp 346-70 Chow, G.C (1960), “Tests of equality between sets of coefficients in two linear regressions”, Econometric, Vol 28 No 3, pp 591-605 Flynn, B.B (1992), “Managing for quality in the US and in Japan”, Interface, Vol 22 No 5, pp 69-80 Flynn, B.B and Flynn, E.J (1999), “Information-processing alternatives for coping with manufacturing environment complexity”, Decision Sciences, Vol 30 No 2, pp 1021-52 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 Flynn, B.B., Schroeder, R.G and Sakakibara, S (1994), “A framework for quality management research and an associated instrument”, Journal of Operation Management, Vol 11 No 4, pp 336-9 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 659-91 Form, W (1979), “Comparative industrial sociology and the convergence hypotheses”, Annual Review of Sociology, Vol 5, pp 1-25 Forza, C (1995), “The impact of information systems on quality performance: an empirical study”, International Journal of Operations & Production Management, Vol 15 No 6, pp 69-83 Forza, C and 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 Forza, C and Salvador, F (2001), “Information flows for high-performance manufacturing”, International Journal of Production Economics, Vol 70 No 1, pp 21-36 Garvin, D.A (1986), “Quality problem, policies, and attitudes in the United States and Japan: an explore study”, The Academy of Management Journal, Vol 29 No 4, pp 653-73 Juran, J.M and Gryna, F.M (1980), Quality Planning and Analysis, McGraw-Hill, New York, NY 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 405-35 Madu, C.N., Kuel, C and Lin, C (1995), “A comparative analysis of quality practices in manufacturing firms in the US and Taiwan”, Decision Sciences, Vol 26 No 5, pp 621-36 Matsui, Y and Sato, O (2002), “An international comparison study on benefits of production information systems”, International Journal of Operation and Quantitative Management, Vol No 3, pp 191-214 Morita, M., Sakikabara, S., Matsui, Y and Sato, O (2001), “Japanese manufacturing organization: are they still competitive”, in Schroeder, R.G and Flynn, B.B (Eds), High Performance Manufacturing: Global Perspectives, Wiley, New York, NY, pp 199-232 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 in manufacturing companies: international perspective”, Journal of Productivity and Quality Management, Vol No 2, pp 153-77 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) Rao, S.S., Ragu-Nathan, T.S and Solis, L.E (1997), “Does ISO have an effect on 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Flynn, B.B., Schroeder, R.G., Flynn, E.J., Sakakibara, S and Bates, K.A (1997), “World-class manufacturing project: overview and selected results”, International Journal of Operations & Production Management, Vol 17 No 7, pp 671-85 Matsui, Y (2002), “An empirical analysis of quality management in Japanese manufacturing companies’”, Proceedings of the Seventh Asia-Pacific Decision Sciences Institute Conference, National Institute of Development Administration, Bangkok, Thailand Molina, L.M., Lore´ns-Montes, J and Ruiz-Moreno, J (2007), “Relationship between quality management practices and knowledge transfer”, Journal of Operations Management, Vol 25 No 3, pp 682-701 Nair, A (2005), “Meta-analysis of the relationship between quality management practices and firm performance – implications for quality management theory development”, Journal of Operations Management, Vol 24 No 6, pp 948-75 Saraph, J.V., Benson, G.P and Schroeder, R.G (1989), “An instrument for measuring the critical factors of quality management”, Decision Sciences, Vol 20 No 4, pp 810-29 Schonberger, R.J (1986), World Class Manufacturing: The Lessons of Simplicity Applied, The Free Press, New York, NY QMI and operational performance 537 MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 538 Appendix The values that follow the names of scales and super-scales report the results of factor analysis (the eigenvalue and percentage of variance of the first factor) taking on the pooled sample to evaluate the validity of these scales The values follow each question item show factor loading for this question item: I Shop-floor quality information practices (3.83 and 64 percent) I.1 Feedback (2.792 and 56 percent): Charts showing defect rates are posted on the shop floor (0.76) Charts showing schedule compliance are posted on the shop floor (0.78) Charts plotting the frequency of machine breakdowns are posted on the shop floor (0.68) Information on quality performance is readily available to employees (0.78) Information on productivity is readily available to employees (0.74) I.2 Shop-floor contact (2.20 and 44 percent): Managers in this plant believe in using a lot of face-to-face contact with shop-floor employees (0.70) Engineers are located near the shop floor, to provide quick assistance when production stops (0.73) Our plant manager is seen on the shop floor almost every day (0.75) Managers are readily available on the shop floor when they are needed (0.79) Manufacturing engineers are often on the shop floor to assist with production problems (0.70) I.3 Supervisory interaction facilitation (2.57 and 64 percent): Our supervisors encourage the people who work for them to work as a team (0.77) Our supervisors encourage the people who work for them to exchange opinions and ideas (0.78) Our supervisors frequently hold group meetings where the people who work for them can really discuss things together (0.79) Our supervisors rarely encourage us to get together to solve problems (0.76) I.4 Employee suggestions (3.03 and 61 percent): Management takes all product and process improvement suggestions seriously (0.80) We are encouraged to make suggestions for improving performance at this plant (0.78) Management tells us why our suggestions are implemented or not used (0.81) Many useful suggestions are implemented at this plant (0.70) My suggestions are never taken seriously around here (removed) I.5 Small group problem solving (2.64 and 53 percent): During problem solving sessions, we make an effort to get all team members’ opinions and ideas before making a decision (0.80) Our plant forms teams to solve problems (0.78) In the past three years, many problems have been solved through small group sessions (0.87) Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) Problem solving teams have helped improve manufacturing processes at this plant (0.76) Employee teams are encouraged to try to solve their own problems, as much as possible (0.78) We not use problem-solving teams much, in this plant (removed) I.6 Multi-functional employees (3.026 and 61 percent): Our employees receive training to perform multiple tasks (0.77) Employees at this plant learn how to perform a variety of tasks (0.76) The longer an employee has been at this plant, the more tasks they learn to perform (0.77) Employees are cross-trained at this plant, so that they can fill in for others, if necessary (0.78) At this plant, each employee only learns how to one job (0.75) II Cross-functional quality information practices (2.320 and 58 percent) II.1 Coordination of decision making (2.28 and 57 percent) Generally speaking, everyone in the plant works well together (0.79) Departments in the plant communicate frequently with each other (0.79) Departments within the plant seem to be in constant conflict (0.70) Management works together well on all important decisions (0.75) II.2 Cross-functional product design (2.28 and 56 percent) Direct labor employees are involved to a great extent before introducing new products or making product changes (0.76) Manufacturing engineers are involved to a great extent before the introduction of new products (0.77) There is little involvement of manufacturing and quality people in the early design or products, before they reach the plant (0.78) We work in teams, with members from a variety of areas (marketing, manufacturing, etc.) to introduce new products (0.77) II.3 Communication with customer (2.11 and 53 percent) We frequently are in close contact with our customers (0.70) Our customers give us feedback on our quality and delivery performance (0.65) We strive to be highly responsive to our customers’ needs (0.76) We regularly survey our customers’ needs (0.80) II.4 Communication with supplier (2.18 and 54 percent) We are comfortable sharing problems with our suppliers (0.76) In dealing with our suppliers, we are willing to change assumptions, in order to find more effective solutions (0.77) We believe that cooperating with our suppliers is beneficial (0.76) We emphasize openness of communications in collaborating with our suppliers (0.72) We maintain close communications with suppliers about quality considerations and design changes (removed) QMI and operational performance 539 MRR 34,5 Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) 540 About the authors Phan Chi Anh is a Lecturer in the Faculty of Business Administration, University of Economics and Business – Vietnam National University, Hanoi His research topics relate to quality management, lean production, and high-performance manufacturing His articles can be found in International Journal of Productivity and Quality, Operation Research Review, and International Journal of Production Economics Phan Chi Anh is the corresponding author and can be contacted at: anhpc@yahoo.com Yoshiki Matsui is a Professor of Operations Management at the International Graduate School of Social Sciences and Faculty of Business Administration, Yokohama National University in Japan His research and teaching topics cover issues of manufacturing management, supply chain management, quality management, JIT production, and new product development He has published papers in International Journal of Production Economics, International Journal of Operations and Quantitative Management, International Journal of Global Logistics and Supply Chain Management, and so on To purchase reprints of this article please e-mail: reprints@emeraldinsight.com Or visit our web site for further details: www.emeraldinsight.com/reprints Downloaded by FREIE UNIVERSITAT BERLIN At 07:14 12 May 2015 (PT) This article has been cited by: Sean Valentine, David Hollingworth 2014 Communication of Organizational Strategy and Coordinated Decision Making as Catalysts for Enhanced Perceptions of Corporate Ethical Values in a Financial Services Company Employee Responsibilities and Rights Journal [CrossRef] Jain Divij, Banwet D.K 2013 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levels Keywords Quality management, Management information systems, Operations and production management. .. practices and operational performance with industry and country effects Next is the issue relates with evaluation of operational performance The HPM collected both objective and subjective data on operational