汽車產業中衛結構之網絡研究

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(1)國立高雄大學亞太工商管理學系碩士班碩士論文. 汽車產業中衛結構之網絡研究 Investigation of CSDS Network Structure of Taiwan Automobile Industry. 研究生：余昌展撰指導教授：李亭林博士. 中華民國九十八年六月.

(2) 致謝回想兩年前，帶著既興奮又害怕的心情來到高大，如今將從這裡離開，前往人生下一個未知的旅程，害怕與興奮的感覺油然而生，不過這次多了一種情緒---是一種捨不得的情懷。感謝系上提供完善的設備與服務，協助我學習上不於匱乏；堅強又具競爭力的師資群，讓我在追求學問過程中，得以涉獵廣泛。當然，亦師亦友的李亭林老師在我困惑與迷惘適時地予以解惑與指引；當擁有收穫與愉悅的同時，願意與我分享；在彼此失落消沈時，不吝嗇給對方打氣鼓舞，這樣的師生關係令我難以忘懷。再者，這群“靜如處子，動如脫兔”的同班摯友們，耀龍、大頭提前帶我領略社會的殘酷與現實；宗倫、坤政使我體會對籃球的熱情；雋心、鯰魚、宜璋告訴我十賭不一定九輸；感謝恩人宏佳陪同發問卷與發燒掛急診；豪偉在球場是與我默契十足的好搭擋；鍾威的一代拳法讓我認識中國武術的精隨；安琪帶我領會大草原蒙古風情；兼具超級玩咖成績又好的亦凡；最後是麗妮，一同成為李氏門派的我們，碩一開始就相互扶持、打氣，課業上共同追求知識的真理。最後感謝同門碩一的學弟妹們，撰寫論文期間的關心，以及張羅口試的準備。除此之外，在口試的期間，感謝口試委員丁一賢教授與邱彥婷教授對於本論文的細心審閱，並於論文口試時，提供寶貴的建議與指正，讓本論文更加完備。更要感謝的是台灣汽車相關業界先進們，無論是在問卷的填答與訪談的協助下，提供你們寶貴意見與建議，沒有你們熱心的回應與支持，就無法完成本論文。回首這兩年多來的求學生涯，感謝我最愛的父母親毫不保留給予我最大的支持；另外，也感謝 Winnie 這幾年在我身旁相互鼓舞激勵，一同取得碩士學位，使我能夠成為有肩膀與責任感的男人。最後，將這一切彰顯上天，感恩於心。. 昌展. 謹致於. 楠梓. 高大. 2009.7 I.

(3) 汽車產業中衛結構之網絡研究指導教授：李亭林博士國立高雄大學亞太工商管理學系碩士班學生：余昌展國立高雄大學亞太工商管理學系碩士班摘要本研究以台灣汽車產業為背景，探討汽車組裝廠與零組廠商於國內產業的網絡活動之情形；利用某三家汽車組裝廠所提供之廠商名冊進行問卷發放，輔以網絡分析理論與分析方法，從宏觀至微觀的網絡角度，分別從整體關係網絡與個別廠商中衛體系探討網絡結構、特性，並切了解跨組織關係(inter-organizational relationships; IORs)與受到網絡結構與特性所影響的中衛體系成員網絡認知。研究結果顯示整體汽車產業之核心高度集中在汽車組裝廠，並且同時佔據於五種整體產業關係網絡之資訊結構洞位置上，總的來說，汽車組裝廠的影響力橫跨整個產業網絡。在個別中衛體系網絡，呈現中高度的核心邊緣現象，儘管如此，存在個別中衛體系網絡間成員的跨組織關係並不受到成員網絡位置的差異而不一致，這顯示個別中衛體系成員對於合作與資訊分享有正面的態度。另外，越處於高向內中心性之汽車組裝廠與部分零組件而言，兩者對於中衛體系所致的內部利益與成功性的認知越持有正向的看法。擁有高度中心性的汽車組裝廠掌握整體與個別網絡之關鍵地位，扮演「促進者 (stimulator)」之角色，對於未來汽車產業的轉型、創新與跨產業合作之達成性擁有舉足輕重之影響。關鍵字：社會網絡分析、跨組織關係、中衛體系、供應鏈關鍵字. II.

(4) Investigation CSDS of Network Structure of in Taiwan Automobile Industry Advisor: Dr. Ting-Lin LEE Institute of Asia-Pacific Industrial and Business Management National University of Kaohsiung Student: Yu, Chang-Jan Institute of Asia-Pacific Industrial and Business Management National University of Kaohsiung ABSTRACT The study explores the network activities between auto-assembler and component manufacturer in Taiwan automobile industry. The study uses the firm list provided by three auto-assemblers and utilizes the social network theories and analyzing tools to investigate the network structure and properties from macro to micro perspective. Also, the study figure out how the IORs (inter-organizational relationships) and perception of CSDS are impacted by network structure and properties. The results indicated that auto-assembler is highly located on core of overall industrial network and information structural hole of five overall industrial networks simultaneously. All in all, the influence of auto-assembler is across the whole industry. In the individual CSDS, the core-periphery phenomenon modestly exists. However, the inter-organizational relationships are not affected by the difference of position within the CSDS. This implies that members in the CSDS have the positive attitude toward the corporation and information sharing. What’s more, the more in-degree centrality auto-assemblers and some component manufacturers have, they are higher positive attitude toward perception of internal benefit and performance. In the future, the auto-assembler with high centrality will dominate the vital status of the whole industrial network and individual CSDS and play a “stimulator” for stimulating industry future transformation, innovation and cross-field/industrial cooperation. Keywords: social network analysis, inter-organizational relationships (IORs), corporate synergy development system (CSDS), supply chain. III.

(5) TABLE OF CONTENTS 1. Introduction....................................................................................................................... 1 1.1 Research Background .............................................................................................. 1 1.2 Research Motivation ................................................................................................ 3 1.3 Research Objectives and Research Questions........................................................... 4 1.4 Research procedure.................................................................................................. 7 2. Literature review ............................................................................................................... 8 2.1 Inter-organizational Relationships (IORs) ................................................................ 8 2.1.1 Trust ........................................................................................................... 10 2.1.2 Commitment ............................................................................................... 11 2.1.3 Shared vision .............................................................................................. 12 2.2 Social Network Analysis........................................................................................ 13 2.2.1 Network ...................................................................................................... 13 2.2.2 Network Position ........................................................................................ 14 2.2.3 Network and Graph Theory ......................................................................... 15 2.2.4 Indicators of Social Network Analysis ........................................................ 16 2.3 Diffusion of innovation .......................................................................................... 21 3. Research Methodology .................................................................................................... 23 3.1 Concept structure ................................................................................................... 23 3.2 Questionnaire design ............................................................................................. 24 3.3 Sample .................................................................................................................. 27 3.4 Network design...................................................................................................... 28 3.5 Analytical approach ............................................................................................... 29 4. Findings and Results ....................................................................................................... 31 4.1 Sample characteristics ........................................................................................... 31 4.2 Factor analysis and reliability/validity analysis ...................................................... 33 4.3 Network analysis ................................................................................................... 36 4.3.1 Overall collaborative network (OCN).......................................................... 36 4.3.2 Co-R&D network (CoRDN)........................................................................ 42 4.3.3 Knowledge sharing network (KSN) ............................................................. 46 4.3.4 Proprietary network (ProN) ......................................................................... 51 4.3.5 Competition network (CompN) ................................................................... 55 4.3.6 Other findings of network analysis .............................................................. 60 4.4 Information sharing/connectivity with five overall industrial network .................... 60 4.4.1 Quadratic assignment procedure analysis (QAP) ......................................... 61 4.4.2 Extraction of key players ............................................................................. 62 4.5 Network position of CSDS and inter-organizational relationships .......................... 66 IV.

(6) 4.5.1 Extraction from OCN .................................................................................. 66 4.5.2 Core-periphery analysis............................................................................... 69 4.5.3 T-test of IOR for CSDS network ................................................................. 70 4.6 Perception test of CSDS ........................................................................................ 73 5. Discussion and Conclusions ............................................................................................ 77 5.1 Findings and Discussion ........................................................................................ 77 5.1.1 Key player in the five overall industrial networks ........................................ 77 5.1.2 Information sharing/connectivity ................................................................. 78 5.1.3 Network position and inter-organizational relationship ................................ 79 5.1.4 Perception of CSDS .................................................................................... 80 5.2 Conclusions and implication .................................................................................. 80 5.3 Limitation and future research ............................................................................... 82 Reference ............................................................................................................................ 84 Appendix ............................................................................................................................ 90. V.

(7) LIST OF TABLE Table 2-1 Summaries of sub-dimensions of inter-organizational relationships ....................... 9 Table 2-2 Composition factor of trust .................................................................................. 11 Table 2-3 Composition factor of commitment...................................................................... 12 Table 3-1 The list of preliminary interview .......................................................................... 24 Table 3-2 The interview questions outline ........................................................................... 24 Table 4-1 Demographic statistics ......................................................................................... 32 Table 4-2 Distribution of employee ..................................................................................... 32 Table 4-3 Distribution of capital .......................................................................................... 32 Table 4-4 Response rate three list providing firms ............................................................... 33 Table 4-5 Analysis of IRO constructs .................................................................................. 34 Table 4-6 Factor analysis of function, benefit and performance construct ............................ 35 Table 4-7 Summary statistics for new factor ........................................................................ 35 Table 4-8 Network indicators of OCN firms ........................................................................ 39 Table 4-9 Network indicators of CoRDN firms.................................................................... 45 Table 4-10 Network indicators of KSN firms ...................................................................... 49 Table 4-11 Network indicators of ProN firms ...................................................................... 54 Table 4-12 Network indicators of CompN firms .................................................................. 57 Table 4-13 Quadratic assignment procedure correlations among five networks .................... 61 Table 4-14 Top twenty structural occupiers for OCN, CoRDN and KSN ............................. 62 Table 4-15 Occupied percentage of repetition ...................................................................... 63 Table 4-16 Numbers of actors who locate on structural hole ................................................ 64 Table 4-17 Network Indicators of CSDS A-07 ..................................................................... 67 Table 4-18 T-test for trust .................................................................................................... 70 Table 4-19 Significance test for trust ................................................................................... 71 Table 4-20 T-test for commitment........................................................................................ 72 Table 4-21 Significance test for commitment....................................................................... 72 Table 4-22 T-test for shared vision....................................................................................... 72 Table 4-23 Significance test for shared vision...................................................................... 72 Table 4-24 Regression coefficients table.............................................................................. 74. VI.

(8) LIST OF FIGURE Figure 1-1 Corporate Synergy Development System ............................................................. 2 Figure 1-2 Research procedure .............................................................................................. 7 Figure 2-1 Three actors network with no structural holes..................................................... 18 Figure 2-2 Three actors network with a structural hole ........................................................ 19 Figure 2-3 High effective size for A .................................................................................... 20 Figure 2-4 Low effective size for A ..................................................................................... 20 Figure 2-5 The situation that partners constraint A............................................................... 21 Figure 3-1 The concept structure of this research................................................................. 23 Figure 4-1 Visualization of OCN’s in-degree centrality ....................................................... 38 Figure 4-2 Visualization of OCN’s out-degree centrality ..................................................... 39 Figure 4-3 Visualization of CoRDN’s in-degree centrality ................................................... 44 Figure 4-4 Visualization of CoRDN’s out-degree centrality ................................................. 44 Figure 4-5 Visualization of KSN’s in-degree centrality ........................................................ 48 Figure 4-6 Visualization of KSN’s out-degree centrality ...................................................... 48 Figure 4-7 Visualization of ProN’s in-degree centrality ....................................................... 53 Figure 4-8 Visualization of ProN’s out-degree centrality ..................................................... 53 Figure 4-9 Visualization of CompN’s in-degree centrality ................................................... 56 Figure 4-10 Visualization of CompN’s out-degree centrality ............................................... 57. VII.

(9) 1. Introduction. 1.1 Research Background In 1953, the first Taiwanese car manufacturer corporation, Yulon Motors, was founded by Mr. Tjing-Ling Yen. Automobile industry needs cross-industry supporting from other industry such as chemicals, steel, rubber, glass, electrical engineering etc. At present, there are eight major auto-assemblers which include Kuozui Motor Ltd., Yulon Motors, China Motors Corporation, Ford Lio Ho Motor Company Ltd., Honda Taiwan Co., Ltd, SANYANG Industry Co., LTD., Prince Motors Co., LTD., and Taiwan ISUZU. In 2007, according to the Industrial Economics & Knowledge Center (IEK),the sales of automobile approaches to NT＄299 billion whose growth rate decreases almost 3.39% than previous year (Chen, 2008). There are several reasons for accounting automobile shrinking sales in Taiwan, for instance, increasing material cost, soaring oil price, declining global economy and domestic political chaos and so on so forth. These affect consumers purchasing behavior. Moreover, both auto-assemblers and auto-component manufacturer also encounter the pressure of international competition individually, especially for the pressure of low-price competition from China and other nations of Southeast Asia. The automobile industry with the effects of the economy of scale needs long-term cooperation and development with upstream suppliers. Therefore, the competition is not only between auto-assemblers, but also between the supply chains which are conducted by auto-assemblers. This phenomenon has resulted in close-relationship supply chains for a long time. Auto-assemblers and upstream auto-components manufacturer are planet-satellite alike. Close connections between auto-assemblers and auto-components is like a relationship network which provides reciprocal supporting. In Taiwan, this kind of 1.

(10) inter-organizational connection network, named corporate synergy development system (CSDS), which is conducted by auto-assemblers (Su, 2004) (see Figure 1-1). The CSDS was composed of an auto-assembler, several first-tier and second-tier component manufacturers. That means each auto-assembler would have its own CSDS (Su, 2004). The study will attempt to analyze the different CSDS from the sample lists obtained with difficulty from three auto-assemblers. They support each other in finance, logistics and personnel. The rising and declining of car industry in a country has an inseparable connection with its local upstream auto-component manufacturers (Su, 2004). The auto-components manufacturers in Taiwan have been cooperated with domestic auto-assemblers for many years. They have developed manufacturing technologies and cumulated their own R&D capabilities.. Figure 1-1 Corporate Synergy Development System Source: produced by the study The CSDS is a complicated multi-echelon network (Su, 2004). It has influence on operation activities in different tiers member of supply chain and produces many uncertainties, integration challenges as well. Therefore, information sharing plays a crucial role in the CSDS which can improve production efficiency and establish better collaboration and integration. 2.

(11) 1.2 Research Motivation As competition in the 1990s magnified and market became world-wide, so did challenges related to getting a product and service to the right place at the right time at lowest cost (Li and Lin, 2006). Organizations started to realize that it is not enough to improve efficiencies within an organization, but their whole supply chain must be made competitive. In this vein, many auto-assemblers and component-manufacturers join CSDS. In the last few years, several articles have been devoted to the study of supply chain networks (SCNs) (Lee and Billington, 1992, 1993) and focused on making each node of the SCNs efficient. However, only efficiency in each node is not enough to achieve operating optimization in whole supply chain. Therefore, the challenges associated with improved product, customer service and operating efficiency cannot be effectively met by isolated change to specific organization, but instead rely seriously on connections and interdependencies between organizations (Swaminathan, 1998). The vision of Corporate Synergy Development Center (CSD), a juridical person, is to promote industrial cooperation and progress, and the role it plays in Taiwan’s automobile industry is to promote cooperation between satellite factory and major auto-assembler, and to establish long-term, mutual dependency, enterprises horizontal specialization network. Therefore, CSDS of automobile is the most consolidated and the most effective network (Su, 2004). In other words, the connection within CSDS is neither stake-relation nor pure trading, instead is partnership and highly dependency. Information sharing can improve production efficiency and establish better collaboration and integration. However, it alone is insufficient to guarantee superior supply chain performance (Bailey and Francis, 2008). Moreover, divergent and opportunistic behavior of partners, informational asymmetries, willingness of sharing and fear of power loss in supply chain etc. would affect information quality. Therefore, bull-effect and 3.

(12) collaboration-related problem in supply chain has possibility to be resolved imperfectly. Besides, without a foundation of great inter-organizational connection, any effort to manage the flow of information or material across the supply chain is likely to be fail (Handfield & Nichols Jr., 1999). The study extrapolates that the activities from decision to implementation within supply chain are almost based on “inter-organizational relationships (IORs)” in order to further progress. Social network analysis (SNA), a set of theories and techniques designed to study connections among individuals and organizations. However, few scholars used SNA to map communication among partners and figure out the communication pattern of supply chain network, especially automobile industry. All in all, this study adopts the aspect of inter-organizational relationships (IORs) and tries to explore the connection situation within the industry with social network analysis approaches and techniques. Under the massive competition came from other nations and the fluctuating/ unstable economy situation, the study would try to examine the effect of that perceived performance, perception of how well the CSDS functions, and perceptions of benefits that behind the progress among CSDS members.. 1.3 Research Objectives and Research Questions The task of SCM is to solve two major problems (Strader, Lin, Shaw, 1999): one is uncertainty-solving and the other one is management of lead time. The former includes demand (volume and preference), process (yield rate, machine downtimes and transportation), supply (parts quality, delivery reliabilities) (Lee and Billington, 1993; Lee et al, 1993). In this vein, information sharing via IT would play a crucial role since it affect customer service, inter-firm collaboration, asset management by decision making, operation and manufacturing cost. These two major tasks are concerned about inter-organization 4.

(13) conduction and information sharing. The bulk of researches in SCM have focused on resolving coordination-relative and process-relative problems, such as inter-organization connection investigation, constructing a mathematical model to simulate outcome performance by inputting some variables that likely affect SCM performance. However, quite few researches discussed the relationship between inter-organizational relationships (IORs) and network structure/features simultaneously. Sparsely, what seems to lack in automobile industry is to implement a visual framework to explore the dynamic connection. Therefore, the study will employ social network analysis (SNA), coming from graph theory concept, to tease out the pattern of network, to find out which company is the critical actor, to trace the direction of flow of information among those surveyed firms, and discover what affects these relation and network on firms in supply chain. Through SNA, knowing well the network positions will interpret the business relation between nodes (firms in the CSDS). This study also tried to figure out whether organization position in CSDS is associated with perception of CSDS performance. While the CSDS members presumably share a primary goal (increasing competence, stable and long-tem transaction etc.), alliances among member or organizations may be partitioned by organization characteristics such as, organization type, profit goal, sourcing of technology/skill, or different shareholder etc. To realize the factors that might guide communication and collaboration patterns of CSDS members, this study maps the inter-organizational collaborative relationships in CSDS. In addition to collaborative relationship, the author also measure coordination, competition, knowledge-sharing and proprietorship. In order to interpret the research objectives more precisely, objectives can be divided into four points: 1.. Exploring the network pattern and features of automobile industry with SNA. 2.. Understanding the members’ cognition toward IORs (trust, commitment and 5.

(14) shared vision) within the CSDS 3.. Examining the perception of the function, benefit and performance of the CSDS. 4.. In addition to collaborative relationship, this study also measure coordination, competition, knowledge-sharing and proprietorship. In view of the proceeding research objectives, three primary research questions to be addressed in this paper are as follows: 1.. Whether. network. structure/features. in. the. CSDS. affect. IORs. and. information-sharing? 2.. Is there any sub-group (-relationship) within CSDS? If yes, are they positively/negatively correlated with information-sharing?. 3.. Whether network position in the CSDS is associated with perceptions (evaluations) of CSDS function and performance?. 6.

(15) 1.4 Research procedure According to the research questions, the relevant literature was initially searched and preliminary interview was conducted in order to understand the operation of CSDS. In succeeding to that, the survey questionnaire design was developed based on literature review and preliminary interview. After that, the social network analysis tool was used for analyzing the network pattern and features of automobile industry and common statistics was adopted for exploring the relation between network features and relational variables. Afterward, the in-depth interview was proceeded to assure that the analysis outcomes are reasonable. Finally, the conclusion is presented in the research.. Figure 1-2 Research procedure 7.

(16) 2. Literature review In this section, the author reviews the literature on inter-organizational relationship, information sharing, information quality and related supply chain performance. The literature review provides the theoretical foundation for this research.. 2.1 Inter-organizational Relationships (IORs) Since the study takes the aspect of “relationship” as a starting point, the author must realize inter-organizational relationship in order to capture the inter-firm relationship in CSDS. Because of been exposing to extreme competition and dynamic environment recently, many firms are faced to great uncertainty about resources flow such as capital, material, equipment, and information. Under this condition, uncertainty prompts firms to establish and manage relationships in order to achieve stability, predictability, and dependability in their relations with others (Oliver, 1990). Inter-organizational relationships are the relatively enduring transactions, flows, and linkages that occur among or between organizations and one or more organizations in its environment (Oliver, 1990). Relationship starts with exchange behaviors in capturing critical or scarce resources between two or more organizations, which is the best way to share or learn special knowledge or skills (Baranson, 1990). By leveraging relationship among organizations, it can create competitive advantages for organization (Dyer and Singh, 1998; Kale, Singh and Perlmutter, 2000). That is to say, relationship in business environment have significantly impact on many firm’s operating strategies (Wilson, 1995). IT can be used to easily link physical supply chain process with partners, but not 8.

(17) inter-organizational relationships. Handfield and Nichols addressed that “without a foundation of effective inter-organizational relationship, any effort to manage the flow of information or materials across the supply chain is likely to be unsuccessful”. (Handfield and Nichols, 1999). Trust and. commitment played necessary factors to build long-term cooperative relationship between supply chain partners (Spekman, Kamauff, and Myhr, 1998; Tan, Kannan, and Hanfield, 1998). Achrol et al. indentified commitment, trust, group cohesiveness, and motivation of alliance partnership as critical to inter-organizational strategic alliance (Achrol, Scheer, and Stern, 1990). Also, good inter-organizational relationships based on trust, commitment, and shared vision is necessary to overcome information sharing-related problems in supply chain (Bobby, MacBeth, and Wagner, 2000). And, inter-organization relationship refers to the degree of trust, commitment, and shared vision between supplier companions (Li and Lin, 2006). The table2-1 obviously presents the sub-dimensions of inter-organizational relationships from different scholars’ point of views. In this research, following the antecedent literature studies, the author will consider inter-organizational relationships as containing three sub-dimensions: trust in trading partners, commitment of trading partners, and shared vision among trading partners.. Table 2-1 Summaries of sub-dimensions of inter-organizational relationships Sub-dimensions of IORs. Authors Spekman, Kamauff, and Myhr, 1998;. Trust and commitment. Tan, Kannan, and Hanfield, 1998 commitment, trust, group cohesiveness, and motivation of. Achrol, Scheer, and Stern, 1990. alliance partnership trust, commitment, and shared vision. Bobby, MacBeth, and Wagner, 2000; Li & Lin, 2006. 9.

(18) 2.1.1 Trust A number of studies have investigated “trust” in many fields in business management such as organization theory, relationship marketing, IS adoption In B2B or B2C e-commerce, and supply chain management etc. In this study, the author starts with supplier-buyer perspective to interpret the essential meaning of trust. Trust in trading companion is defined as the willingness to depend upon a trading companion in whom one has confidence. (Monezka, Petersen, Handfield and Ragatz, 1998; Spekman, Kamauff, Myhr, 1998). Trust is through faith, reliance, belief, or confidence in supply chain partner, viewed as a willingness to forego opportunistic behaviors (Spekman, Kamauff, and Myhr, 1998). Mayer, Davis, and Schoorman defined trust as the expectation that the counterpart in any circumstances will act to benefit both parties with willingness to accept the results from the actions of the counterpart (Mayer, Davis, and Schoorman, 1995). Following aforementioned definitions of trust, trust contains several necessary factors, for instance, willingness to rely on partners, mutual benefit consideration, confident to partner’s actions. The table2-2 clearly indicates the composition factors of trust by various scholars’ views. Most importantly, trust can stimulate favorable attitudes and behaviors (Schurr, Ozanne, 1985).For example; trust will reduce the transaction cost and promote cooperation between the involved organizations. That is to say, trust has a positive impact on maintaining the cooperative relationship (Hart and Saunders, 1997). Thus, trust has been considered by many researchers to be essential factor in most productive partner relationship (Wilson and Volsky, 1998).. 10.

(19) Table 2-2 Composition factor of trust Composition factor of trust Willingness to trust. Scholars Monezka, Petersen, Handfield,1998; Spekman, Kamauff, Myhr,1998. Dependency on partner. Spekman, Kamauff, andMyhr, 1998. Confidence to partner. Monezka, Petersen, Handfield,1998; Spekman, Kamauff, Myhr,1998. Mutual benefit consideration. Mayer, Davis, and Schoorman, 1995. 2.1.2 Commitment Commitment of trading partners refers to the willingness of buyer and suppliers to exert effort on behalf of relationship (Monezka, Petersen, and Handfield, 1998; Spekman, Kamauff, and Myhr, 1998). Commitment is a durable desire to maintain a valued relationship. It includes each partner’s intention and expectation of continuity of the relationship, and willingness to invest more involvements in supply chain management (Mentzer, Min, and Zacharia, 2000). Commitment includes relation-specific investments to make both sides feel sincerity in their cooperation and thus, helps to maintain a long-term partnership (Hsieh, 2004). Commitment has been considered as the variable that can distinguish between relationships that continue and that break down (Wilson and Volsky, 1998). Commitment can implicate trusting the suppliers with proprietary information and other sensitive information. More specifically,” commitment “ups the ante” and makes it more difficult for partners to act in ways that adversely affect overall supply chain performance”. (Li and Lin,. 2006). Some scholar argued that commitment makes both sides of partners more willingness to work together, bear risk, and share profit until creating economic rent and synergy (Bowersox, 1990). In sum, the core conception of commitment is the extent to which partner’s involvement for lasting partnership and maximizing mutual benefit. The table2-2 clearly indicates the composition factors of trust by various scholars’ views. 11.

(20) Table 2-3 Composition factor of commitment Composition factor of trust Willingness to work together. Scholars Monezka, Petersen, Handfield,1998; Spekman, Kamauff, Myhr,1998; Bowersox, 1990. willingness to invest more involvements. Monezka, Petersen, and Handfield, 1998; Spekman, Kamauff, and Myhr, 1998; Mentzer, Min, and Zacharia, 2000; Hsieh, 2004. expectation of continuity of the relationship. Mentzer, Min, and Zacharia, 2000; Hsieh, 2004. bear risk and share profit. Li and Lin, 2006; Bowersox, 1990. Source: produced by the study. 2.1.3 Shared vision Shared vision between partners is identified as the degree of similarity of partner of shared values and beliefs between trading partners (Achrol, Seheer, and Stern, 1990; Lee and Kim, 1999). Shared vision is therefore the extent to which partners have belief in common about what behaviors, goals, and policies are important or unimportant, appropriate or inappropriate, and right or wrong. (Ballou, Gillbert, and Mukherjee, 2000) It is obvious that supply chain members with similar organizational cultures and should be more willing to trust their partners. Spekman et al. even suggest that collaboration within a supply chain can be achieved only to the extent that trading partners share a common “world of view” of SCM. Organizational incompatibilities between allied organizations, in term of reputation, job stability, strategic insight, control systems, and goals, will lead to less information sharing (Mentzer, Min, Zacharia, 2000).. 12.

(21) 2.2 Social Network Analysis SNA has been defined as a mapping and investigation of the relations among a group of actors (Carter, Ellram and Tate, 2007). The nodes of the networks can be individuals, a group of individual such as a department within an organization, or organizations within a larger network such as supply chain (Carter, Ellram and Tate, 2007). The following literature reviews were composed of network, network position, graph theory, indicators of Social Network Analysis, and diffusion of innovation.. 2.2.1 Network The perspective of co-evolution and interdependence are important characteristics in an industrial network, and the competitive aspect of strategy turns into less important (Gadde, Hakan, and Lars, 2003). The characteristic of network structure is composed of business relationships and network positions (Huat Low 1997). If the network structure is tight, business interdependencies between actors are strong. Then strong social relationships often pose as high obstacles for new companies. On the other hand, if the network structure is loose, then intends to build a position will be relatively easier because network positions are changeable and the interdependencies and relationships between firms are relatively weak. The several implications were extended from above statement. With a stable network, the firm can plan and define beforehand the type and nature of the relationships it hopes to have with other actors in the network (Huat Low, 1997). Comparatively, in evolving or unstable network, especially when information related to the different types of resources, the performance of what actions and connections to which actors are not known. Despite the fact that network provides lots of advantages, there are still three managerial 13.

(22) paradoxes in networks (Hakansson and Ford 2002). Close relationships are at the heart of a firm’s survival. The first paradox is that a well-developed network of relationships also binds a firm into its operating way and thus restricts its ability from changing in business environment. Second, firm’s relationships are one of the critical means used to affect others. As for the paradox, it is believed that the company itself is a result of those relationships and their development. At last firms usually do their best to control the network around them and to administrate relationships so that their objectives are attained. The last paradox is that the more successful a firm is in its control goals, the less innovative the network becomes.. 2.2.2 Network Position One of the network structural perspectives, network positions depicted how firms have relations with the other firms in the network (Huat Low, 1997). A firm’s position is determined from the outside firms rather than from the inside ones, and is under the condition that the company relates to the firms which it actually possesses business exchanges (Gadde, Hakan, and Lars, 2003). These positions can explain the complexity, connections and dynamics of these relationships (Huat Low, 1997). Thus, it should not be considered that position changes are easily achieved or even always possible (Easton, 1992). Positions and business relationships among the firms are the result of an ongoing historical process (Henders, 1992). It is true that firms may be in a favorable position and defend those positions by any means (Huat Low, 1997). Position in this context, is much like powerful authority inherently which is a relativistic idea (Axelsson, 1992). There is no doubt that information and knowledge can be regarded as external resources. Central network positions supply more opportunities to gain these resources than peripheral positions. Speaking of these resources, they display “fuel” in the innovation process, driving 14.

(23) innovation performance (Tsai, 2001). To be capable of combining the potential resource together, it is important for a firm who take possession of an information-rich position within the network (Gadde, Hakan, and Lars, 2003).. 2.2.3 Network and Graph Theory Social network analysis acquires several of concepts from graph theory. The beauty of network approaches to organizational studies is the extent to which the same network methods and topics apply at different levels (Kilduff and Tsi, 2003). A graph is a set of vertices (points) and a set of lines between pairs of vertices. Vertices and lines understood in graph theory display actors and their ties known in social network analysis, directed graphs with one or two way arrows are used to display the degree of correlation between actors, and so on (Harary, Norman and Cartwright, 1965). Graph theory truly assists us in understanding the extent to which networks reflect segregate social systems, mechanistic organization, organizational effectiveness, and is liable to resolve conflicts (Kilduff and Tsi, 2003). In the research, actor refers to a person, organization, or nation that is involved in a social relation. Moreover, the approach assumes that different configurations of social ties produce dissimilar benefits for actors (Burt, 2000). Therefore, it is obvious that detecting and interpreting patterns of social ties among actors is very important. Once scholars try to orient social network ideas, it is undoubted that the author has had to use a variety of terms that characterize organizations from a network perspective. As for network characteristics of organizations, they are density, centralization, structural hole. These indexes help to distinguish different networks in the same organizational unit, or to contrast networks across organizational units (Kilduff and Tsi 2003).. 15.

(24) 2.2.4 Indicators of Social Network Analysis. Density Social networks often include dense area of nodes who “stick together” (Nooy, 2005). The density of a network is an examination of how many correlations there are between actors compared to the maximum possible number of connections that exist between actors. It takes on a value between 0 and 1. When density is close to 1, the network is considered dense; otherwise it is sparse. In the social network literatures there are a lot of debates about the impact of network structures and their features, that is, sparse (dense) networks and strong (weak) ties, on firm performance. Dense networks may provide communication pathways by which information and resources can be channeled effectively. Other research has shown that too much density can restrict access to outside resources or sources of information and retard adoption of best practices (Valente, Chou, and Pentz, 2007). And, in some cases, larger and more communicative coalition networks might not lead to effective collaborations (Jasuja et al., 2005). About string and weak ties, according to one view, strong ties in a highly interconnected alliances network negatively impact firm performance (Rowley et al., 2000) and weak ties positively impact firm technological performance (Kogut, 2000; Ruef, 2002). And according to an alternative viewpoint, however, strong network ties provide better impact on firm performance than weak ties (Krackhardt, 1992; Uzzi, 1997; Kale et al., 2000; Wong and Ellis, 2002).. Centrality Centrality indicates the degree to which a firm has succeeded in developing a dominant position in the overall network of inter-firm partnerships. The centralization, or the centrality, of the entire network measures the distribution of centrality among firms in a 16.

(25) network. It measures the extent to which a focal firm is more central than all other firms in the network. Social network scholars distinguish between three measures of centrality: degree, closeness and betweenness (Wasserman & Faust, 1994). Degree centrality simply reflects the total number of collaborative ties (scores) that a firm formed in a period. A firm with more ties is considered to be closer to the center of the network and have more opportunities to play an essential role in the network. In contrast, a firm with a low degree of centrality is considered to be isolated from other firms, and consequently expected to play a marginal role in the network. While the degree centrality takes into account only the number of direct ties that a node has, the closeness centrality also considers indirect ties (which are not directly connected to that node). In formal terms, closeness measures the centrality of a point by summing the geodesic distances from that point to all other points in the network. If a firm has high closeness centrality in a network, this means that it is close to most of the other firms, and hence is able to avoid the control of others (Freeman, 1979). To explain betweenness centrality, the author have already known degree and closeness centrality are based on the reach-ability of an actor in the network, however, betweenness centrality may be to what extent an actor dominates the flow of information because of his position within a network.. A node with few ties may play an important intermediary role and so be very. central to the network. It measures the number of geodesics (a geodesic is the shortest path between any particular pair of nodes in a network), and consequently the extent to which a firm, landing on the shortest path between two other companies, has a potential for control. So Betweenness centrality finds the node in a position where it is acting as a” bridge “from one node/group of nodes to another. However, since this study did not include all companies in automobile industry, instead of partial, the author have to use another measures. Both closeness centrality and betweenness centrality take into account the links among all nodes in the network for their 17.

(26) calculation. So, out- and in-degree centrality is more appropriate for the study. Out-degree is the number of other organizations each organization nominates and in-degree is the number of choices received by each organization. Out-and in-degree may be normalized by dividing the number of ties by N-1, the maximum possible number of nominations within each network. Out- and in-degree are local centrality measures calculated by examining each node’s immediate neighborhood. Out-and in-degree centrality are less sensitive to missing data (Borgatti, Carley & Krackhardt,2006; Costenbader & Valente, 2003) and may be more appropriate when less than complete data are available.. Structural holes In several important works, Ronald Burt coined and popularized the term "structural holes" to refer to some very important aspects of positional advantage/disadvantage of individuals that result from how they are embedded in neighborhoods. Burt's formalization of these ideas, and his development of a number of measures has facilitated a great deal of further thinking about how and why the ways that an actor is connected affect their constraints and opportunities, and their behavior. The basic idea is simple. Imagine a network of three actors (A, B, and C), in which each is connected to each of the others as in Figure 2.1.. Figure 2-1 Three actors network with no structural holes. 18.

(27) Let's focus on actor A (of course, in this case, the situations of B and C are identical in this particular network). Suppose that actor A wanted to influence or exchange with another actor. Assume that both B and C may have some interest in interacting or exchanging, as well. Actor A will not be in a strong bargaining position in this network, because both of A's potential exchange partners (B and C) have alternatives to treating with A; they could isolate A, and exchange with one another.. Now imagine that the author open a "structural hole" between actors B and C, as in Figure 2-2. That is, a relation or tie is "absent" such that B and C cannot exchange (perhaps they are not aware of one another, or there are very high transaction costs involved in forming a tie).. Figure 2-2 Three actors network with a structural hole. In this situation, actor A has an advantaged position as a direct result of the "structural hole" between actors B and C. Actor A has two alternative exchange partners; actors B and C have only one choice, if they choose to (or must) enter into an exchange.. Effective size of the network (EffSize) It is the number of alters that ego has, minus the average number of ties that each alter has to other alters, essentially, the number of alters minus the average degree of alters within 19.

(28) the ego network, not counting ties to ego (Burt, 1992). Suppose that A has ties to three other actors. Suppose that none of these three has ties to any of the others. The effective size of ego's network is three, as in Figure 2-3. Alternatively, suppose that A has ties to three others, and that all of the others are tied to one another. A's network size is three, but the ties are "redundant" because A can reach all three neighbors by reaching any one of them. The average degree of the others in this case is 2 (each alter is tied to two other alters), as in Figure 2-4. So, the effective size of the network is its actual size of 3, reduced by its redundancy of 2, to yield an efficient size of 1. All in all, the greater effective size one actor has, the smaller reduplicated network it owns. That is to say, the actor located on structural hole with higher possibility.. Figure 2-3 High effective size for A. Figure 2-4 Low effective size for A. 20.

(29) Constraint index It is a summary measure that the extents to which ego’s connections are to others who are connected to one another. Essentially, it is a measure of the extent to which ego is invested in people who are invested in other of ego's alters (Burt, 1992). For example, if A's potential trading partners all have one another as potential trading partners, A is highly constrained. If A's partners do not have other alternatives in the neighborhood, they cannot constrain A's behavior. The logic is pretty simple, but the measure itself is not. The idea of constraint is an important one because it points out that actors who have many ties to others may actually lose freedom of action rather than gain it -- depending on the relationships among actors.. All in all, the smaller constraint scores one actor has, higher possibility the actor located on has. That means actor who is not limited by partners, can utilize the position advantage and control power.. Figure 2-5 The situation that partners constraint A. 2.3 Diffusion of innovation According to the diffusion of innovations theory (Rogers,2003), central nodes in the organization would be associated with positive perceptions of organizational functioning and performance for several reasons: first, central nodes learn about information and others’ 21.

(30) attitudes and actions at much quicker than other nodes and thus may have more positive perceptions of organizational functioning (Rogers,2003). Second, central nodes possess an inherent bias to support the status quo of the organization so their advantage positions are maintained (Valente & Davis, 1999). Third, central nodes may utilize inappropriate influence on others in the network such that their perceptions become the dominant view of the organizational culture (Friedkin, 1998). Thus, it is hypothesized that central organizations in the CSDS will perceive that the network functions well by being inclusive, has clear leadership, and can effectively take actions. It is also hypothesized that members of central organizations will be less likely to perceive problems attaining CSDS goals and more likely to perceive the CSDS to be successful compared to members of non-central organizations.. 22.

(31) 3. Research Methodology. 3.1 Concept structure The concept structure (Figure 3-1) illustrates the relationship between network structure / prosperities and inter-organizational relationship, perception of corporate synergy development system and information connectivity. This research will investigate the network structure of automobile industry with perspective of network. The concept structure provides a study exploratory direction, not a null hypothesis.. Figure 3-1 The concept structure of this research. 23.

(32) 3.2 Questionnaire design Before questionnaire designed, three times interview have been conducted with three major auto-assemblers (two times by face to face; one by e-mail) (Table 3-1). The current situation of automobile was realized and part of questionnaire was developed based on the results of the interview (see Part II in Appendix). Interview outline has been sent in advance, which includes the current situation of CSDS, the way to interact, maintain, and collaborate with auto-components manufacturers; what relationship types say competition, co-research and developing, knowledge-sharing and proprietorship exist between them, and the perception of function or performance in the CSDS (Table 3-2). Table 3-1 The list of preliminary interview Company name. Interviewer. Date. Contact. Kouzui Motors, LTD.. Mr. Chien (Senior general. 2009/2/12. Face to. manager of purchasing division ) Yulon Nissan Motor. Mr. Fu(Manager of purchasing). face 2009/2/27. CO.,LTD. China Motors. Face to face. Mr. Yuan(Senior specialist). 2009/2/26. e-mail. Corporation. Table 3-2 The interview questions outline Interview content 1. Introducing the past and current operation or activities of your firm’s CSDS.. 2. What kind of mechanism or function did your company interact with the members of CSDS?. 3. How to build the trust and improve the commitment with the members of your own CSDS for achieving the product quality and fulfill the delivery? 24.

(33) 4. How auto-component manufacturers to be qualified membership in your company’s CSDS? How many categories of qualified members in your CSDS?. 5. How does your company forward the market information to your CSDS member to meet the market demand?. 6. Does your company have any kind of cooperative activities, such as joint venture, co-R&D with any CSDS members? How about member-to-member?. 7. Does your company share the managerial skill or knowledge with the member of CSDS?. 8. Is there any competition between members within your own CSDS?. 9. Do you agree with that CSDS actually bring the significant benefit for your company? If not, are there any progresses to improve in the future?. 10 Does your company have the vision and share it with the member of CSDS?. The questionnaire of study is modified from and refers to other researchers’ works (e.g. Friedman et al., 2007; Valente et al., 2008; M'Chirgui, 2007). The questionnaire includes four main parts. And the questionnaire will be filled by at least one representative from each firm who is responsible for procurement department or sales department (those have the experience or familiar to CSDS operations and activities and manager preferred). The first part is about network measurement. The study measures the transaction network with suppliers and buyers, and asks each representative to write down the transaction partners and rate the frequency of contact ranging from 1 to 7 (see Part I in Appendix). Also, four types of relationship arranged in this study will be used to find out the sub-relationships in the automobile industry. In the second part, firm representative are ask to write down the firms for each type of relationship respectively without rating contacting frequency (see Part II in Appendix). The third part of questionnaire is about the survey of inter-organizational relationships (IORs) (see Part III in Appendix) within the automobile industry. 25.

(34) Likert-type scale was adopted in this part to measure IORs, including trust, commitment, and shared vision. Trust is measured with five 7-points Likert items. Commitment and shared vision are measured with five and three 7-points Likert items respectively. These are based on Li & Lin, 2006. Fourth part is about degree of perception of CSDS function, perceptions of benefits and performance of participating CSDS (see Part IV in Appendix). Organizational function is measured with seven 5-points Likert items asking representatives of firm to indicate how much they agree or disagree with following statements: (1) every CSDS members participate in discussion activity, not just a few; (2) the CSDS is hierarchically managed (top-down decision-making); (3) the CSDS meetings are well organized and efficient; and so on (Jasuja et al., 2005; Valente et al., 2008). Perception of benefits is measured with five 5-points Likert items asking “how well the benefits that firms obtained from CSDS such as financial, human resources, physical support, power of negotiation with government, and flexible reaction?” (Valente et al., 2008) Perception of performance is measured with seven 5-points Likert items asking “how successful has firm achieved the specific performance within CSDS such as implementing cost-leading strategies, shortening the delivering time, improving product quality, learning about dynamic industrial information, obtaining management or production techniques, more stable order/sourcing, and becoming more competitive?” Besides, the survey measured organizational characteristics (firm name, size, capital, major product, length of time been involved in CSDS, attendance meeting frequency in CSDS) and firm position (auto-assembler or auto-component manufacturer) and firm representative information (department, job position). The pre-testing is necessary for the data collection. In the pre-testing stage, all 26.

(35) items for each section and for network data collection were reviewed by one academician and evaluated through structured interviews with three practitioners who were asked to comment on the appropriateness of the whole questionnaire structure. Based on the feedback from these academicians and practitioners, redundant and ambiguous items were modified, simplified or eliminated. Since items were modified by practitioners who are familiar to CSDS operations and activities, it is believed that this could have lowered method variance and avoided unnecessary errors.. 3.3 Sample The firm list is collected from three major auto-assemblers. They provided their firm list partially. Boundary specification may seriously influence the structure of a network, so it is worth to consider it carefully (Nooy, 2005). The author also refer to the membership directory 2008 published by Taiwan transportation vehicle manufacturers association (TTVMA). The membership directory includes only manufacturers who have registered for membership prior to the end of December 2007. The amounts of auto-assemblers and auto-components manufacturers are 476. If research proceeds to study the entire world, the network will become too large to be carried out (Nooy, 2005). So, the study will use the member list provided from three majors which about 327. Due to the survey item (perceptions of CSDS function and performance) of this study, the author cannot help deleting some firm who did not have the operations/activities experience within the CSDS through phone check. Some of these lists are not domestic company in Taiwan, some is bankrupt and some is overlapping. All of above not qualified should be discarded. Adding the rest of five auto-assemblers,. our. final. sample. includes. 8. auto-assemblers. and. 192. auto-components manufacturers, 200 in sum. There are 32 qualified lists is not 27.

(36) membership in TTVMA. Besides the assemblers, sample list include engine, engine electronics, fueling system, intake & exhaust system, cooling system, lubrication system, air-conditioning system, body stamping, transmission, steering system, suspension system, braking system, wheeling system, lamp, electric & electronic, exterior trim, interior trim, fastener, raw material companies as well. In the first stage, all of the companies were contacted through personal phone-call.. Some of the firms received the questionnaire by mail, and the rest were. delivered directly in person. Meanwhile, the author collected a few personally. Then, the author reminded company to fill the questionnaire, and send it back as soon as possible. After that, in the second stage, unfortunately, because of extremely low response rate, the author delivered the questionnaire directly again. At the third stage, one of three auto-assemblers assisted the author to deliver questionnaire on March 26, 2009. Data collection started on March 12, 2009, and end on April 17, 2009.. 3.4 Network design As mentioned above, in order to handhold and understand the overall industry status, the analytic network is composed of five kinds of relationships, including overall collaborative relationship in automobile industry, co-R&D relationship, knowledge sharing relationship, proprietary relationship and competition relationship. Moreover,. in. the. research,. the. study. focuses. on. auto-assemblers. and. component-manufacturing firms. What’s more, the study try to take a closer look at specific auto-assemblers’ owned corporate synergy development system and understand variation of individuals in their local circumstances. So the author try to extract these three list providing auto-assemblers from overall collaborative network in automobile industry with extract approach by using UCINET. That means there are 28.

(37) three auto-assemblers ego networks. In the following chapters, the author code auto-assemblers and component-manufacturing firms with prefixed alphabet A and B respectively. The five kinds of relationship network are abbreviated to OCN (overall collaborative network), CORDN (co-R&D network), KSN (knowledge sharing network), PRON (proprietary relationship network), and COMPN (competition network) respectively. Three auto-assemblers ego networks are also abbreviated to CSDS-A02 (n=52), CSDS-A04 (n=42), and CSDS-A07 (n=36).. 3.5 Analytical approach Factor analysis was conducted to determine whether the scales were uni-demensional or multi-dimensional such as perception of function, benefit and performance. In addition to factor analysis, the author also conduct the Cronbach’s alpha coefficient to measure internal consistency reliability, and check the items belonging to each construct such as trust, commitment, and shared vision. And see if each construct are reliable within the acceptable interval (upper to 0.7 high reliability; 0.7~0.35 acceptable; lower than 0.35 low reliability). Nonresponse bias will be assessed by verifying that (1) respondent’s demographics are similar to each other, and (2) early and late respondents were not significantly different (Armstrong and Overton, 1977). The network software UCINET 6.205 was used to analyze firm’s network indicators, including degree, closeness, and betweenness centrality. Due to different stress on network, these indicators separately provide insight on how and for what they communicate with one another. The research will report centralization scores, and conduct the core-periphery analysis (Borgatti and Everett, 1999; Borgatti, Everett 29.

(38) and Freeman, 2002) for understanding the network position. As soon as possible, these variables will be used to construct network structure graph and data with NetDraw (Borgatti, 2002). The author is also eager to explore the different CSDS, and to figure out and compare the network characteristics belonging to them. Then, the study is going to analyze the four relationships mentioned before with some SNA techniques for further insight. Then, regression was adopted to analyze perceived function, benefit, and performance on participation centrality in CSDS. With the presses, the author hopes to explore that the structure or pattern of ties in automobile industry social network which will be helpful to the researcher and even practitioners. Finally, the in-depth interview was conducted with practitioners in order to learn more detail about the analysis results. This would make the study presenting the current facts of the automobile industry.. 30.

(39) 4. Findings and Results. 4.1 Sample characteristics Questionnaires were collected using four ways such as postal mail, e-mail, personal collection and auto-assembler assistance. Of the 68 respondents, they are representing 56 firms completed the survey. At least one representative from every company completed the questionnaire. Three respondents is invalid response and one firm is excluded. The total response rate is 27.5% (55/200), containing 3 auto-assemblers and 52 auto-component manufacturers. The table 4-1 will present the characteristics of the responding firms. On average, the amount of firms’ employee falls into 50-100 people (average 2.91where 1=less than 50, 2=50-100, 3=101-300, 4=301-500, 5=more than 501). It is about 65.4 percent of responding firms have employee over the interval 101-300. (Table 4-2) Most of firms have over 30 million capitals which are close to 86%. (Table 4-3) However, the average firms’ capital is more than 10 million NT$ (average4.80 where 1=less than 1million, 2=more than 1million, 3=more than 5 million, 4=more than 10 million, 5=more than 30 million NT$). Although it is about 92.7 percent of firms have established for over 16 years, the average firms’ established years is about11-15years (average 3.91 where 1=less than 5 years, 2=6-10 years, 3=11-15 years, 4=more than 16 years). From the above statistics, most of respondents are medium to large companies and had stayed in the automobile industry for many years. 76.4 percent of firms have attended the CSDS for over 16 years. Respondents indicated. average 6-10 times CSDS meeting last years (average 2.65 where 1=less. than 5 times, 2=6-10 times, 3=11-20 times,4=21-30 times ,5=more than 31 times). 31.

(40) There are about 63% of company attended CSDS meeting for 6-10 times and 1/3 of company attended for 11-20 times. Table 4-1 Demographic statistics Mean(S.D.). Range. employee capital Established years. 2.91(1.251) 4.80(.524) 3.91(.348). 1-5 1-5 1-4. Involved years Involved frequency/per year. 3.60(.807) 2.65(1.250). 1-4 1-5. Table 4-2 Distribution of employee less than 50 9 frequency Percentage% 16.3%. 51-100. 101-300. 301-500. 10 18.1%. 21 38.1%. 7 12.7%. more than 501 8 14.5%. more than 10 million 5 9.1%. more than 30 million 47 85.4%. Table 4-3 Distribution of capital (NT$). less than 1million 0 frequency Percentage% 0. more than 1million 0 0. more than 5 million 3 5.4%. The following table 4-4 stands for each CSDS response rate from three auto-assemblers respectively. Firm A has the highest response rate of 58%, next is firm C about 36% and firm B gets the lowest 28% which might be caused by too large amounts of provided lists. However, study did not include all companies in automobile industry; there are missing data because of lower response rate in the study.. 32.

(41) Table 4-4 Response rate three list providing firms Auto-assembler. A. B. C. Total. Returning amounts. 33. 49. 35. 55*. Amounts of firms from Provided lists. 57. 175. 98. 200*. Returning rate (%). 58%. 28%. 36%. 27.5%. amounts. Note: *An overlap exits because a firm may be belonged to two or three provided list Next, statistics analysis was conducted to make certain that nonresponse bias is absent. All the respondents were separated into two groups depended on the specific date of return (i.e. 2009/3/25). It shows no significant difference between the two groups based on the independent sample t-test (p=0.51 and 0.711, respectively). Consequently, it is no doubt that non-response bias would not be a trouble in the research.. 4.2 Factor analysis and reliability/validity analysis Actually, the study attempted to survey all members from lists which provide by three assemblers as possible as the author can. Recognizing that a few respondents represent the same organization, the author collected the responses together from the same organization. The author aggregated responses to the respondent firm since the network questions asked respondents to nominate other firms they connected. If there are multi-respondents in the same firm, the study combined survey responses by using the average of respondent on the IORs and perception (e.g. part III and part IV in Appendix). Network data were combined by adding responses, with redundant nomination removed. If two respondents from the same firm nominated the same organization, only one link was recorded. The items in the questionnaire are exerted to measure the trust, commitment, 33.

(42) and shared vision. In addition to IOR items, the author also exam whether the scales were uni-demensional or multi-dimensional such as perception of function, benefits and performance which extraction method is principle component method and rotation method is varimax method. After factor analysis, Cronbach’s alphas coefficient was conducted to measure internal consistency reliability for each construct. Following tables 4-5 shows the result of factor analysis and cronbach’s alpha coefficient for each construct. One item was subtracted from trust construct because of too small factor loadings (<0.5). Table 4-5 Analysis of IRO constructs Construct. Original number of items. Number of items after factor analysis. Cronbach’s alpha. Trust Commitment Shared-vision. 5 5 3. 4 5 3. 0.885 0.656 0.894. Note: N=55 The study deleted one item from perception of performance and extracted two new factors from benefit section. The section of function includes involved discussion, efficient decision-making, organized and efficient meeting, clear leadership, practical action from talk, well usage of aggregate capability from members, member trust. The section of internal-benefit contains financial and human resources; the external-benefit includes negotiation power with government and common flexible reaction. The section of performance stands for implementing cost-leading strategies, shortening the delivering time, quality improvement, learning about dynamic industrial information, obtaining management or production techniques, and becoming more competitive. Refer to table 4-6.. 34.

(43) Table 4-6 Factor analysis of function, benefit and performance construct Section. Original number of. Number of Items after factor. items. analysis. Function. 7. Benefit. 5. Performance. 7. new factor. Cronbach’s alpha. 7. Function. 0.869. 5. Internalbenefit. 0.838. Externalbenefit. 0.737. Performance. 0.759. 6. Note: N=55 Table 4-7 Summary statistics for new factor Trust Commit Shared vision Function Internal External Performance. Mean(S.D.). Range. Alpha. 5.819(0.13) 5.901(0.87) 6.155(0.06). 5.642-5.971 4.495-6.77 6.079-6.201. 0.885 0.656 0.894. 3.945(0.19) 3.116(0.26) 3.507(0.4) 3.724(0.202). 3.708-4.278 2.815-3.306 3.222-3.792 3.491-3.940. 0.869 0.838 0.737 0.759. Note: N=55 All above of the cronbach’s alpha were located within the acceptable range (see table 4-7). In light of validity, the content of the questionnaire is based on both theories and past similar research. Besides, questionnaire was pretested with two academicians and revaluated through structured interviews with three practitioners in automobile industry. The procedures lead to some item modification. From this view, validity should be established.. 35.

(44) 4.3 Network analysis When a firm builds a position in a network, it establishes relationships to other firms who already are embedded in the network. Perspectives of social network of OCN, CORDN, KSN, PRON and COMPN are worthy to analyzing further in detail. In the following place, these five overall industrial networks will be proceeded to describe network’s indicators and characteristics. Because of missing data in the research, the study will use in- and out-degree centrality to analyze these five networks. Out-and in-degree centrality are less sensitive to missing data (Borgatti, Carley & Krackhardt,2006; Costenbader & Valente, 2003) and these may be more appropriate measures when complete data are. not easily available. In addition to. out-and in-degree centrality, the author also conduct some structural hole measurements for analyzing the information sharing circumstance/connectivity for each networks. The missing data will be ignored and not shown in the related figures and tables.. 4.3.1 Overall collaborative network (OCN) Figure 4-1 visualizes the OCN, the prefixed alphabet of node stands for different type of firm where “A-”represents auto-assemblers and “B-” stands for component-manufacturing firms. The different color also have distinct business category of firms. Red circle means auto-assemblers; light green square stands for frame-manufacturing firms; Pink up-triangle is body-stamping-manufacturing firms; Blue box means interior & exterior manufacturing firms; Gray down-triangle stands for electric and electronic firms; blackish green circle-in-box are engine-related firms; Black diamond represent the rest of others components such as rubbers, glass, 36.

(45) chemical, fastener, battery, tire and so on. First, it is clear from Figure 4-1 that the size of nodes is weighted by in-degree centrality. As for a directed network, it is recognized that in-degree centrality is superior to observe the amount of arcs received by a node. In the first level of in-degree centrality, Figure 4-1 distinctly reveals that the firm A-02 is the core key player in OCN and the firm A-07 represents the second key player. Then, it is also presented that A-08, A-04, A-01, A-06 and A-05 are the second level of degree centrality according to the data of normalized in-degree centrality which is shown in Table 4-8. Nearly all of the component-manufacturing firms in OCN are linked to all of auto-assemblers, especially for A-02 which is the most powerful firms to mobilize social resources. Briefly speaking, the stronger ties one key player obtains, the more sources of information it gains at its disposal, the faster information the key player attains, so the more capabilities of transmitting industrial news the key player keeps. Second, according to Table 4-8, there are some zero score in the out-degree column because of missing data caused. Although, some data were missed, the author still can find out that most component-manufacturing firms try to build collaboration relationships with auto-assemblers. These firms include frame (light green square, B-008), in-and exterior manufacturing (blue box, B-045, B-017 and B050), electric and electronic (gray down-triangle, B-046), engine (blackish green circle-in-box, B-007), other parts (black diamond, B-016) and auto-assemblers (red circle, A-02) (Figure 4-2). Third, referring to Table 4-8, the columns of effective size and constraint score represent the extent to the actor locate on the good information position, say structural hole. Large part of top 10 structural holes holders are auto-assemblers. Judging from above statement, results reveal that the ranking of each indicator in each firm is very similar. Therefore, the author can conclude that the 37.

(46) auto-assemblers play an influential role in OCN, which may have a great impact on information flow and business collaboration performance. On the other hand, large part of seeking collaboration actors was component-manufacturing firms who see auto-assemblers as major collaborative objectives.. Figure 4-1 Visualization of OCN’s in-degree centrality. 38.

(47) Figure 4-2 Visualization of OCN’s out-degree centrality Table 4-8 Network indicators of OCN firms In-Degree. Out-Degree. EffSize. Constraint. A-02. 229.00. 75.00. 49.13. 0.05. A-07. 210.00. 0.00. 33.10. 0.08. A-08. 169.00. 0.00. 36.38. 0.06. A-04. 145.00. 51.00. 42.82. 0.05. A-01. 105.00. 67.00. 29.63. 0.05. A-06. 93.00. 0.00. 19.00. 0.09. A-05. 84.00. 0.00. 26.22. 0.07. B-006. 37.00. 52.00. 12.73. 0.21. B-099. 37.00. 0.00. 5.07. 0.42. B-025. 31.00. 14.00. 4.80. 0.42. B-124. 27.00. 0.00. 4.00. 0.52. B-073. 26.00. 0.00. 3.67. 0.50. A-03. 24.00. 60.00. 16.89. 0.07. B-031. 22.00. 20.00. 4.50. 0.41. B-001. 20.00. 25.00. 5.89. 0.38. B-191. 20.00. 0.00. 3.00. 0.53. B-057. 20.00. 0.00. 2.50. 0.68. 39.