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  1. 1. THE IMPACT OF INTER-FIRM COLLABORATION ON THE PERFORMANCE OF SUPPLY CHAIN MANAGEMENT Seung-Chul Kim Hanyang University, School of Business, Seoul, South Korea. Tel: (822) 2220-1069; Fax: (822) 2220-1169; E-mail: sckim888@hanyang.ac.kr Se-Hyung Cho Konyang University, Department of Management Information Systems, South Korea Tel: (8241) 730-5180; Fax: (8241)733-2070; E-mail: shcho@kytis.konyang.ac.kr Yong-Kyun Chung Kangwon National University, Div. of Econ. and Int. Trade, South Korea Tel: (8233) 250-6187; Fax: (8233) 256-4088; E-mail: ykchung@kangwon.ac.kr July 11-13, 2007 International Conference on Business and Information (BAI2007) organized by International Business Academics Consortium Selected Track Area : Operations Management Address all correspondence to: Seung-Chul Kim Hanyang University, School of Business 17 Haengdang-dong, Seongdong-gu, Seoul 133-791, Korea TEL: (822) 2220-1069; FAX: (822) 2220-1169; E-MAIL: sckim888@hanyang.ac.kr
  2. 2. THE IMPACT OF INTER-FIRM COLLABORATION ON THE PERFORMANCE OF SUPPLY CHAIN Seung-Chul Kim Hanyang University, School of Business, Seoul, South Korea. Tel: (822) 2220-1069; Fax: (822) 2220-1169; E-mail: sckim888@hanyang.ac.kr Se-Hyung Cho Konyang University, Department of Management Information Systems, South Korea Tel: (8241) 730-5180; Fax: (8241)733-2070; E-mail: shcho@kytis.konyang.ac.kr Yong-Kyun Chung Kangwon National University, Div. of Econ. and Int. Trade, South Korea Tel: (8233) 250-6187; Fax: (8233) 256-4088; E-mail: ykchung@kangwon.ac.kr ABSTRACT Inter-firm collaboration has a strong influence on the efficiency and the performance of a supply chain as firms can achieve competitive advantage by working together with their suppliers. There are many factors that can affect the inter-firm collaboration, and the effect of each factor needs to be studied and understood for building an efficient supply chain. Automotive parts manufacturing industry consists of several layers of suppliers, and they interact with one another in a network of complex relationships. It is a very ideal industry to study the effect of inter-firm collaboration on supply chain management. This paper examines the impact of inter-firm collaboration on the supply chain performance in the context of automotive industry. We use the data collected from Korean automotive parts manufacturers through survey questionnaires. This study will be able to provide useful insights about how the buyer-supplier relationship should be managed to make a more efficient supply chain and to improve supply chain performance. Key words: inter-firm collaboration; supply chain performance; auto parts manufacturing industry; buyer-supplier relationship. 2
  3. 3. INTRODUCTION Inter-firm collaboration has become an important issue in supply chain management since implementing supply chain management(SCM) successfully requires companies to develop close collaborative relationships with their suppliers. Firms produce better quality products at lower costs by working together with their suppliers and achieve a strategic advantage in the competition as can be portrayed, for example, in the concept of collaborative planning, forecasting and replenishment (CPFR). It is not, however, easy to work with other companies which have different organizational cultures, missions, IT systems, values, etc. It is imperative to have a close collaboration between firms to build an efficient supply chain as coordination through strategic partnerships replaces the vertical integration, i.e. the conventional way, to attain the desired efficiency in the supply chain. Inter-firm collaboration may be measured by the level of trust and satisfaction about the relationship between two firms. Also, there are many factors that can affect the inter- firm collaboration such as information system maturity, asset specific investment, opportunistic behavior, communication, etc. Some factors have more influence on the inter-firm collaboration while others might not be as important for maintaining it. The effect of each factor needs to be studied and understood for effective supply chain management. Auto parts manufacturing industry consists of automobile manufacturers and several layers of parts suppliers, and they interact with one another in a network of complex relationships. The results of good collaboration between buyer and supplier firms are often reflected in the performance of the supply chain which the buyer and supplier firms belong to. One common form of the inter-firm collaboration that was developed and extensively implemented in auto industry is Just-In-Time delivery (JIT) in which we could observe close cooperation between buyer and supplier firms. In this regard, it is a very ideal industry to study the effect of inter-firm collaboration on supply chain management. This paper examines the effect of inter-firm collaboration on the efficiency of supply chain in the context of auto parts manufacturing industry. First, we identify the important factors that affect inter-firm collaboration. Second, the impact of inter-firm collaboration is measured on the performance of a supply chain. We use the data collected from Korean automotive parts manufacturers through survey questionnaires. Korea has a very sizable automobile industry with several world class auto 3
  4. 4. manufacturers such as Hyundai Auto Co., Kia Auto Co., GM Daewoo, and Renault Samsung. Also there are a large number of parts manufacturers and their suppliers in the upstream of the Korean auto industry supply chain. This study will be able to provide useful insights about how the buyer-supplier relationship should be managed to achieve a better supply chain. LITERATURE REVIEW In recent times, no one question the importance of SCM for improving the firms’ performance in a networked economy. Usually, many firms cooperate to produce a final product. In particular, an automobile manufacturer does not make every component of a car in automobile manufacturing industry. Naturally car makers purchase auto parts from many suppliers. In this structure, SCM is an important tool to realize the efficiency in auto industry. Many previous studies investigated the relationships between buyer firms and supplier firms, and found that both groups will benefit more from the cooperative relationships than the competitive relationships. Table 1 contains some of the previous research that investigated various types of the buyer-supplier relationship and its impact on the firms. TABLE 1. Summary of previous studies. Researcher Content Dyer(1996); Nishiguchi and Compared U.S. and Japanese auto industries. Brookfield (1997) Japanese auto industry has more cooperative buyer- supplier relationships. Dywer, Schurr and Oh(1987) Emphasized the importance of the buyer-supplier cooperation. Anderson and Narus(1990) Maloni and Benton (1997) Boddy et al. (1998) Investigates key success factors in implementing supply chain Akintoye et al.(2000) Wathne and Heide(2004) Cooper (1993) Reasons that firms form a supply chain (inventory reduction, improving service, etc.). Lambert and Cooper(2000) Lamming (1993) Suggest a lean supply chain model in auto industry to understand the buyer and supplier relationship. Han (1993); Fisher et al. The importance of buyer-supplier relationship for a (1994); Imne and Morris firm’s competitive advantage. (1993);Jap(1999) Shapiro (1985); Spekman Investigate the various types of buyer-supplier (1988); Landeros and relationship: Monczka (1989);Burt 4
  5. 5. (1989); Bensaou and Venkatraman (1995); Helper (1991); Helper and Sako competitive vs. cooperative (1995) open market negotiation vs. vertical integration integrated vs. structural Morgan and Hunt(1994) Trust and cooperation among firms. Smith et al. (1995) Akintoye et al.(2000) Landeros (1993) Define the cooperative relationships. RESEARCH METHODOLOGY Research model and hypotheses A research model is constructed to depict the relationship among the factors as shown in Figure 1. It shows the relationship between the success factors, inter-firm collaboration, and SCM performance. The variables representing each factor of the model will be identified from the analysis, and the relationship will be tested by using statistical analysis. The level of inter-firm collaboration is measured by surveying the supplier firms. That is, first-tier supplier firms evaluate the first-level relationship (auto manufacturers and first-tier parts suppliers), and second-tier supplier firms evaluate the second-level relationship (first-tier parts suppliers and second-tier parts suppliers). Figure 1 contains the research model showing the relationships between the tiers of firms in the supply chain, and hypotheses are constructed as below for each of the relationships indicated as H1 through H3. H1: Success factors affect the collaboration level between firms. H2: Important factors affecting collaboration are different between the first-level and the second-level relationships. H3: SCM performance is positively related to the collaboration level between firms. High level of inter-firm collaboration will result in high SCM performance. 5
  6. 6. FIGURE 1. Research model. Data collection Data for this study were collected from automotive parts manufacturers through survey in 2006. The survey questionnaire consists of eighty two questions. The survey was conducted by mails. The respondents are the staff of the sales department in the supplier firms, and thus familiar with the relationships between their own company and their buyer company. In this study, we analyze the impact of inter-firm collaboration on the SCM performance. The inter-firm collaboration was measured from the viewpoint of supplier firms because supplier firms are normally in a weaker position in the relationships, and thus, we expect that it is possible to obtain more conservative assessment of the inter-firm collaboration between supplier and buyer firms. About five hundred fifty questionnaires were mailed out to the companies listed on the Korea Auto Industries Cooperation Association member directory, and seventy responses were returned. A total of sixty five responses were used for this study after five were discarded due to incomplete responses. They were divided into two groups depending on their relationship with the automobile manufacturers. Fifty companies were the first-tier suppliers, and fifteen companies were the second-tier suppliers. The profile of the firms is shown in Table 2. The survey questions are answered by 7-point Likert-type scale except those requiring single-item answers. We used descriptive statistics, factor analysis, correlation test, 6
  7. 7. and regression to analyze the data. TABLE 2. Profile of the firms Frequency Proportion Sales in dollars 0 ~ $50 million 34 53.1% $50 ~ $100 mil. 9 14.1 $100 ~ $200 mil. 8 12.5 $200 mil. ~ 13 20.3 No. of 0 ~ 200 33 52.4% 200 ~ 500 16 25.4 employees 500 ~ 1000 9 14.3 1000 ~ 5 7.9 Relationship 1st-tier supplier 50 78.1% with Auto 2nd-tier supplier 10 15.6 Others 4 6.3 manufacturer Length of 0 ~ 10 years 12 19.0% 10 ~ 20 years 20 31.7 relationship 20 ~ 30 years 18 28.6 with the buyer 30 years ~ 13 20.7 *The numbers do not add up to 65 since there are a few firms that did not answer certain questions. ANALYSES AND RESULTS Statistical tests are conducted on the data to identify factors and to group them for further analyses. Identification of the factors and variables The internal consistency reliabilities are tested for the measured variables, and the results (Cronbach α> 0.6) show that all of them are above the acceptance level. Factor analyses were conducted to identify the groupings of the variables for success factors, inter-firm collaboration, and SCM performance. We used eigenvalue of 1 and the factor loading of 0.5 as the selection criteria. The results yielded six factors that provide four variables for success factors and two variables for the level of inter-firm collaboration. They are named as follows: IT maturity, asset specific investment, opportunistic behavior, communication , trust, and satisfaction. Another factor analysis produced three factors for SCM performance, and they are the efficiency, speed, and effectiveness of SCM. The results are shown in Tables 3 and 4. Based on the results of the factor analyses, factor groupings of the variables are made as shown in Table 5. 7
  8. 8. TABLE 3. Results of factor analysis to extract the success factors. Factor Factor Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Quest. IT util 54 .874 .125 .155 .198 -4.756E-02 9.802E-02 IT util 55 .850 -7.483E-02 1.035E-03 9.037E-02 -8.007E-02 .213 IT util 53 .844 .147 .173 .186 .167 4.987E-04 IT util 50 .827 1.864E-02 -.186 9.565E-02 1.847E-02 -.144 IT util 49 .810 1.952E-02 -.124 6.376E-02 5.018E-02 -7.863E-02 IT util 52 .746 .203 9.858E-02 5.446E-02 .282 -2.843E-02 Success-Relationship 16 1.538E-02 .859 6.644E-02 3.082E-02 .190 .198 Success-Relationship 17 .150 .855 .268 .121 8.990E-02 7.656E-02 Success-Relationship 19 .114 .844 .159 7.076E-02 2.236E-02 -1.011E-03 Success-Relationship 7 -3.287E-02 2.572E-02 .818 -.126 -.135 -3.749E-02 Success-Relationship 12 6.573E-02 .152 .730 -.264 .169 .227 Success-Relationship 15 -.161 .232 .707 5.329E-02 .166 -.355 Success-Relationship 14 .148 .249 .692 .247 -1.611E-02 -.126 Success-Coordination 28 .242 .104 -.174 .849 1.570E-03 -4.000E-02 Success-Coordination 25 .178 .190 -4.722E-02 .809 -9.984E-02 .261 Success-Coordination 29 9.314E-02 -4.997E-02 .107 .766 .205 .121 Success-Relationship 5 4.483E-02 .157 -7.205E-02 9.596E-02 .918 1.291E-02 Success-Relationship 4 .214 .108 .162 4.902E-03 .810 .342 Success-Relationship 23 -.137 .127 -8.016E-02 7.994E-02 .214 .827 Success-Relationship 21 .139 .159 -.102 .413 6.701E-02 .658 Reliability(Cronbach α) 0.9174 0.8666 0.7690 0.8096 0.7575 0.6318 Note: Principal component and Varimax rotation with Kaiser normalization. TABLE 4. Factor analysis for SCM performance variables. Factor Factor Factor 1 Factor 2 Factor 3 Quest. SCM Perf 10 .849 .255 -3.629E-02 8
  9. 9. SCM Perf 9 .795 .115 .161 SCM Perf 8 .754 .202 .316 SCM Perf 6 .737 .192 .335 SCM Perf 3 .161 .820 -7.923E-02 SCM Perf 4 .162 .693 .192 SCM Perf 1 .256 .691 .260 SCM Perf 7 .150 .424 .765 SCM Perf 5 .374 -.278 .658 SCM Perf 2 .148 .512 .649 Reliability(Cronbach α) 0.8509 0.7300 0.6416 Note: Principal component. Varimax rotation with Kaiser normalization. TABLE 5. Composition of the factors and variables Factor Variables Related studies Success IT maturity Asset specific investment Heide and John(1988), factors Morgan and Hunt(1995), Dyer et al.(1996), Heide(2003) Opportunistic behavior Klein et al.(1990), Stump and Heide(1996) Communication Anderson and Narus(1990) Morgan and Hunt(1995) Ballou et al.(2000) Collaboration Trust Anderson and Narus(1990) Morgan and Hunt(1995) Akintoye et al.(2000) Smith et al.(1995), Spekman et al.(2005) Satisfaction Anderson and Narus(1984) Anderson and Narus(1990) Morgan and Hunt(1995) SCM Overall SCM performance performance Efficiency Speed Effectiveness Test of Hypotheses Regression analyses were performed to determine the acceptance of the three hypotheses, and the results are presented in Tables 6 through 9. 9
  10. 10. For Hypothesis 1, a multiple regression test was conducted to find out which of the four variables representing success factors had significant impact on the inter-firm collaboration level. Among the four variables, asset specific investment and communication turned out to have significant impacts at α = 0.05 or α = 0.10 on the inter- firm collaboration level respectively. Thus, two of the success factors are found to be important for inter-firm collaboration, but IT maturity and opportunistic behavior do not seem to affect inter-firm collaboration. For Hypothesis 2, data were divided into two groups, that is, 1st-tier suppliers and 2nd- tier suppliers, according to the firm’s relationship with the auto makers. Again, multiple regression tests were conducted on the data to find out the relationship between the success factors and the inter-firm collaboration level. None of the success factors turned out to have significant impact on inter-firm collaboration for both 1st-tier and 2nd-tier groups. Thus, it was not possible to determine whether important factors affecting inter- firm collaboration are different between the first-level and the second-level supplier-buyer relationships. For Hypothesis 3, the result of the regression test showed that inter-firm collaboration seemed to have a positive and significant impact on the SCM performance although the significance level is very marginal with p-value = 0.1089. Thus, we can say that SCM performance will improve as the level of inter-firm collaboration increases. 10
  11. 11. TABLE 6. Collaboration vs. the success factors. coefficient t p-value IT maturity 0.0167 0.2803 0.7802 Asset specific investment 0.1857 2.0465 0.0453** Opportunistic behavior -0.0912 -0.9730 0.3346 Communication 0.1961 1.8325 0.0720* 2 F = 2.865, p-value = 0.0311, r = 0.167, df = 61 * : p < 0.10, ** : p < 0.05 TABLE 7. 1st-level relationship: Collaboration vs. the success factors. coefficient t p-value IT maturity 0.0514 0.7516 0.4561 Asset specific investment 0.1570 1.4377 0.1574 Opportunistic behavior -0.0955 -0.8188 0.4172 Communication 0.1543 1.2266 0.2263 F = 1.3206, p-value = 0.2768, r2 = 0.0255, df = 49 TABLE 8. 2nd-level relationship: Collaboration vs. the success factors. coefficient t p-value IT maturity -0.1169 -0.8288 0.4345 Asset specific investment 0.2291 1.0832 0.3146 Opportunistic behavior -0.0327 -0.1472 0.8870 Communication 0.4412 1.4755 0.1835 F = 1.9421, p-value = 0.2082, r2 = 0.2551, df = 11 TABLE 9. SCM performance vs. Collaboration. 계수 t p-value Collaboration 0.5325 1.6271 0.1089 F = 2.647, p-value = 0.1089, r2 = 0.026, df = 61 Path analysis and test of Hypotheses We used correlation test and regression analyses to find out the relationships among the success factors, inter-firm collaboration and SCM performance, and the results are 11
  12. 12. presented in Figure 2. In this analysis, the inter-firm collaboration is defined as the sum of the three variables of trust, satisfaction and communication. From the analyses, it seems that IT maturity and asset specific investment affect the inter-firm collaboration level and also has direct effect on the SCM performance as well while opportunistic behavior seems to affect only asset specific investment. Inter-firm collaboration is also found to have a direct influence on the SCM performance. FIGURE 2. Path analysis. CONCLUSION In this paper, we attempted to identify the important factors affecting inter-firm collaboration, and assessed the impact of inter-firm collaboration on the performance of supply chain management. First, we found that asset specific investment and communication were the two important factors that would have significant impact on inter-firm collaboration. The result shows that the inter-firm collaboration level will increase when firms make investments that are designed to be used specifically for their supplier and buyer firms and when firms are actively engaged in communication with their suppliers and buyers. Second, inter-firm collaboration was found to have a marginally significant impact on the SCM performance. Thus, the SCM performance 12
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