Proceedings of the 11th Annual Conference of Asia Pacific Decision Sciences Institute Hong Kong, June 14-18, 2006, pp. 774-790.

A TAXONOMY OF SUPPLY CHAIN INTEGRATION IN CHINA

Baofeng Huo Department of Decision Sciences & Managerial Economics,

The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Tel. 852-2609-8560, Fax: 2603 5104, Email: baofeng@baf.msmail.cuhk.edu.hk

Xiande Zhao

Department of Decision Sciences & Managerial Economics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Tel. 852-2609-7650, Fax: 852-2603 6840, Email: Xiande@baf.msmail.cuhk.edu.hk

Winter Nie Global Business Department,

Thunderbird—Garvin School of International Management, Glendale, AZ 85306 Email:niew@t-bird.edu

ABSTRACT

Several studies have identified different types of supply chain integration. However, no study has developed the taxonomy of supply chain integration. This study aims to define and measure supply chain integration. Based on three main types of supply chain integration, a taxonomy is developed and the impact of supply chain integration on supply chain performance and financial performance of the firm within the supply chain is investigated. We also examined differences in competitive hostility, trust and relationship commitment for companies with different types of integration. The results show that the companies with a full integration supply chain (high degree of integration with the supplier, customer and internal integration) achieve the best company performance, the best supply chain performance and have the highest degree of trust and relationship commitment with their partners. Companies with a low integration supply chain (all three types of integration are low) achieve the worst company and supply chain performance and have the lowest degree of trust and relationship commitment with their partners. It is also found that competitive hostility is one of the important drivers of supply chain integration. Companies facing higher level of competitive hostility are more likely to have a higher level of supply chain integration. Keywords: Supply Chain Integration, Trust, Relationship Commitment, Competitive Hostility, China

INTRODUCTION Supply chain management (SCM) has received much attention from both practitioners and academicians. Supply chain management is the systematic, strategic coordination of traditional business functions within a particular company and across the businesses in the supply chain to improve the long-term performance of the companies and the supply chain as a whole (Mentzer, 2001). In recent years, there is also an increasing emphasis on supply chain integration (SCI). SCI could be defined as the degree to which a firm can strategically collaborate with its supply chain partners and cooperatively manage intra- and inter-organizational processes to achieve effective and efficient flows of products, services, information, money, and decisions to provide the maximum value to the customer with low cost and high speed (Der Vaart and van Donk, 2004; Frohlich and Westbrook 2001; Bowersox et al., 1999; Towill and McCullen, 1999; Stevens, 1989). SCI has been hailed as the key to creating value in SCM (Horvath, 2001). Despite of the importance of SCI, limited research has been done to measure SCI and there is almost no study on the taxonomy of SCI. The first objective of this study is to develop and test measurements for SCI and develop a taxonomy of SCI based on these measurements. Furthermore, SCI should influence supply chain performance and the financial performance of the firm within the supply chain. The second objective of this study is to empirically investigate impact of SCI on supply chain performance and the performance of the firm within the supply chain. Morgan & Hunt (1994) empirically validated the hypotheses of trust and relationship commitment as key mediating variables in ten forms of relationship marketing for tire retailers as well as their suppliers from the relationship marketing perspective. Handfield & Bechtel (2002) demonstrated how suppliers and customers could build relationships based on trust and their findings further suggested that working to trust could improve SC responsiveness. Beth et al. (2003) advocated that trust and relationship commitment are placed in the highest priorities in achieving

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“SCI” that promotes collaboration between SC partners for values and competitiveness. The level of trust and relationship commitment should influence the degree of supply chain integration. However, there is no empirical test for such hypotheses. The third objective of this study is to examine the relationship between trust, relationship commitment and SCI. Competitive hostility refers to the degree to which that the companies face difficult challenges in the competitive environment. These include changes in the marketplace, low profit margin, and more demanding quality standards imposed by the marketplace (Ward et al, 1995). To deal with the hostile environment, companies have to integrate with their trading partners in key processes to reduce cost and increase speed. Therefore, companies facing different competitive hostility should have different level of SCI. The fourth objective of this study is to examine the impact of competitive hostility on SCI. The remainder of this article is organized as follows. First, we review the literature related to SCI, performance, trust, relationship commitment, and competitive hostility and then propose the research hypotheses. Subsequently, we discuss the research methodology and test the hypotheses. Finally, we offer a discussion of the findings and present the conclusions and limitations of the study together with suggestions for future research.

THEORETICAL BACKGROUND AND PROPOSED HYPOTHESES Supply Chain Integration As early as the 1970s, researchers were articulating the need for integration in the physical distribution of products (e.g., Lambert et al., 1978). However, it was not until a decade ago that researchers started to call for a systematic approach to SCI. The main focus of this limited body of research has been the development of a SCI instrument. Various types of integration have been proposed in the existing literature. For example, Stank et al. (2001a) propose internal integration, customer integration, and supplier integration, as well as technology and planning, measurement, and relationship integration. Johnson (1999) develops strategic integration. In examining this literature however, we find that most of it focuses on internal and external integration, with its components customer and supplier integration. Information sharing, communication, demand coordination, relationship building, and so forth have been largely incorporated into the internal and external integration constructs. Furthermore, a general consensus seems to have emerged regarding how to define internal and external integration. As a result, we focus specifically on internal integration, customer integration, and supplier integration. Internal integration refers to the degree to which a firm can structure its organizational practices, procedures and behaviors into collaborative, synchronized, and manageable processes to fulfill its customers’ requirements (Chen and Paulraj, 2004a; Cespedes, 1996; Kahn and Mentzer, 1996; Kingman-Brundage et al., 1995). In contrast, external integration refers to the degree to which a firm can partner with its key supply chain members (customers and suppliers) to structure their inter-organizational practices, procedures, and behaviors into collaborative, synchronized, and manageable processes to fulfill their customers’ requirements (Stank et al., 2001b). External integration can be divided further into customer integration and supplier integration. Customer integration refers to the core competency derived from better coordination of critical customers, whereas supplier integration involves that of critical suppliers, in a company's supply chain to achieve improved service capabilities overall at a lower total supply chain cost (Bowersox et al., 1999). Performance A review of the literature on business performance, supply chain performance, and organizational performance suggests a wide divergence of opinions regarding which measures are the best yardsticks of performance. Chen and Paulraj (2004a) argue that financial performance should be the main measure of business performance because the primary goal of a business organization is to make profits for the shareholders. Financial performance has been used widely as a key measure of firm performance (Boyer, 1999; Boyer et al., 1997; Bender, 1986) and evaluated according to different dimensions. However, other researchers (e.g., Eccles and Pyburn, 1992; Dixon et al., 1990; Johnson and Kaplan, 1987; Hall, 1983; Skinner, 1971) have pointed out several limitations of relying solely on financial performance measures in supply chains. For example, a numerical performance measure may not describe the system’s performance adequately and therefore be as vague and difficult to utilize as simple qualitative evaluations (Beamon, 1999). Because a supply chain is an integrated process through which raw materials are manufactured into final products and delivered to customers, selecting the appropriate performance measures for supply chains also is difficult due to their inherent complexity. Van Hoek (1998) advocates that supply chain firms devise an innovative measurement system as opposed to the traditional return on investment (ROI)-based system. Neely et al. (1995) suggest the use of cost, quality, delivery, and flexibility as measures of supply chain performance. Along the same line, Vickery et al. (2003) use presale customer service, product support, responsiveness to customers, delivery dependability, and delivery speed as dimensions of service performance. Recognizing the inadequacy and inconclusiveness of current supply chain performance measurement systems that rely heavily on cost as the primary (if not sole) measure, Beamon (1999) presents a framework for the selection of performance measurement systems for

776 BAOFENG HUO, XIANDE ZHAO, WINTER NIE

manufacturing supply chains. He provides an overview and evaluation of the performance measures used in some supply chain models. Some recent SCI studies (e.g., Vickery et al., 2003; Tan et al., 1998) have used both operational and financial performance as indicators of the organizational performance. However, many SCI studies continue to use either operational (Stank et al., 2001a, b; Scannell et al., 2000) or perceived financial (Ross, 2002) performance outcomes alone. In our study, we use three categories of performance measures: operational performance, supply chain performance, and financial performance. Operational performance is the manufacturer’s operational outcome performance in serving its customers, that is, how the manufacturer performs for its key customer in the areas of cost, quality, flexibility, delivery, and so forth. Supply chain performance measures the supply chain’s cost, inventory level, quality, flexibility, lead-time, delivery, process time, speed, and so forth. As an overall performance measure for the firm, financial performance evaluates the firm’s profitability and market growth. Extensive literature has attached great importance to SCI for achieving comparative advantages (Bowersox et al., 1989; Burt and Doyle, 1992; Cammish and Keough, 1991; Clinton and Closs, 1997; Lee & Billington, 1992; Freeman and Cavinato, 1990; McGinnis and Kohn, 1993; Morris and Calantone, 1991;Reck and Long, 1988; Syson, 1989), as well as operational performance (Ahmad & Schroeder, 2001;Colin and Mapes, 1993; Frohlich & Westbrook, 2001; Johnson, 1999; Narasimhan & Jayaram, 1998; Stank et al., 2001b). Based on the above literature, we believe that companies with different levels of internal, customer and supplier integration can be classified into different groups and the level of SCI will have a significant impact on performance. Therefore, we propose the following hypotheses: H1: A taxonomy can be developed based on the level of internal integration, customer integration and supplier integration. H2: The Level of supply chain integration will have a significant impact on operational performance. H3: The Level of supply chain integration will have a significant impact on supply chain performance. H4: The Level of supply chain integration will have a significant impact on the financial performance of firms within the supply chain. Trust and Relationship Commitment Collaborative relationships have become one of the most significant resources for building up competitive advantage (Dyer & Singh 1998). Much literature (Kumar 1996; Spekman et al. 1998; Bachmann, 2001) argued that trust is a useful lubricant or fundamental ingredient in maintaining cooperation and avoiding conflicts. Trust can be defined as the willingness to rely on exchange partner in whom one party has confidence (Moorman et al, 1992a, Moorman et al, 1992b,). Handfield & Bechtel (2002) studied the role of trust and relationship structure in improving SC responsiveness using data from North American manufacturing firms. They suggested that trust can improve SC responsiveness and developing trust within the relationship improves supplier responsiveness even if buyers do not possess great control over their suppliers. Understanding trust has become the top priority concern in upholding the relationships among SC partners. Trust can be conceptualized in two different levels: interpersonal trust and organizational trust (Cummings & Bromiley, 1996; Zaheer et al., 1998). On the basis of interpersonal trust, organizational trust is established. Organizational trust can be further classified into two categories: intra-organizational trust (Shockley-Zalabak et al. 2000) and inter-organizational trust (Zaheer et al, 1998). Although the issue of trust within SC has not been studied until recently, the concept of trust has drawn considerable attention in management (e.g., Kramer & Tyler, 1995; Doney, Cannon, & Mullen, 1998) and other disciplines (e.g., Child & Mollering, 2003). Barber (1983) and Morgan & Hunt (1994) suggested that the propensity for relational continuity and the establishment of long-term relationship are primarily in the theme of “relationship commitment”. Relationship commitment can be defined as the willingness of a party to invest resources into a relationship (Dion et al. 1992; Morgan & Hunt, 1994). Gundlach et al. (1995) further pinpointed its importance for developing and sustaining successful relational exchange. Mayer et al. (1995) and Luo (2002) also argued that the commitment is the backbone of strengthening the function of established trust. Relationship commitment can be identified into two levels: interpersonal commitment and organizational commitment (Hornby, 1995). Organizational commitment could be further categorized into Intra-organizational (Porter et al., 1974; Mowday et al., 1982) and inter-organizational commitment (Chen and Paulraj, 2004a). With increased inter-organizational trust and relationship commitment, SC organizations would develop closer relationships with their SC partners; therefore, it enhances the implementation of SCI. There has been an increasing research emphasis on trust and relationship commitment in recent years. Therefore, we propose the following hypotheses: H5: Different levels of supply chain integration will have different level of trust. H6: Different levels of supply chain integration will have different degree of relationship commitment. Competitive Hostility Identifying a company’s competitive hostility has long been considered an important element in strategy research. Ward et al. (1995) investigated the influence that specific business environmental factors have on the operations strategy choices based on the study of 319 firms in Singapore. In their study, the business environment included four factors:

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business cost, labor availability, competitive hostility, and dynamism. In their paper, seven items were used to assess competitive hostility: keen competition in local markets, keen competition in foreign markets, low profit margins, declining demand in local market, declining demand in foreign market, producing to the required quality standards, and unreliable vendor quality. The researchers found that there was a strong relationship between environmental factors and the competitive priorities, further indicating that high performers choose to emphasize different competitive priorities as compared to low performers when faced with the same environmental concerns. Based on the research in Singapore, Amoako-Gyampah & Boye (2001) conducted a similar study among 78 manufacturing firms in Ghana. The path analytic model used showed that perceived competitive hostility had the highest influence on operations strategy choices, and that it had direct significant effects on low cost, quality, and flexibility. Facing different competitive hostility, the companies will develop different levels of SCI to purse competitive advantages. Therefore, we propose the following hypotheses: H7: The degree of competitive hostility faced by the company will significantly influence the levels of SCI.

RESEARCH METHODOLOGY Sampling and Data Collection To test the above hypotheses, we collected data from manufacturing companies in China. Since China is becoming the global factory of the world, manufacturing companies in China play a very important role in many supply chains. To our knowledge, very few empirical studies of supply chain management issues use data from China, which means that data collected and results found from China can be of significant value. We used random sampling to collect the data. Because China is such a large country, we strategically selected five cities to represent the wide gamut of economic development stages in China—Chongqing, Tianjin, Guangzhou, Shanghai, and Hong Kong—as our target samples. Chongqing, a traditional industrial base in inland China, resides in a relatively early stage of economic reform and market formation. Tianjin is an industrial base in the north and reflects the average stage of economic development and market formation in China. Guangzhou and Shanghai, located in the affluent Pearl River delta and Yangtse River delta respectively, have enjoyed a higher degree of economic reform and marketization. In Hong Kong, there is hardly any manufacturing activity per se. Instead, most Hong Kong companies keep their manufacturing facilities in China but use Hong Kong as a hub to control and direct the flow of materials, finished goods, information, logistics, and finances. Thus, we believe that Chongqing, Tianjin, Guangzhou, Shanghai, and Hong Kong are representative of Chinese economic development with varying degrees of market economy, and therefore were selected as sample cities for our study. The strategic selection should capture different economic development stages in today’s China. To obtain a representative sample of manufacturing companies, we used the Yellow Pages of China Telecom in each of the four selected cities in mainland China and the Directory of the Chinese Manufacturers Association in Hong Kong as the sampling pool to get a representative sample of manufacturing companies. We randomly selected companies on the lists and contacted them through telephone calls. These companies represent a wide variety of industries, including food, beverage, alcohol, and cigarettes; chemicals and petrochemicals; wood and furniture; pharmaceutical and medical supplies; building materials; rubber and plastics; metal, mechanical, and engineering; electronics and electrical; textiles and apparel; toys; jewelry; arts and crafts; and publishing and printing. The wide range of industries in the sample is designed to make the sample representative of Chinese manufacturing firms and the results more generalizable. One challenge for this research was collecting reliable data about supply chain integration with the suppliers and customers and about the performance of the supply chain and the companies within the supply chain. After consulting with supply chain executives, we determined that the best method was to obtain one key informant who is knowledgeable about the supply chain management within the manufacturing companies. This person, who usually comes with such titles as supply chain manager, CEO/president, vice president, or director, is supposed to be knowledgeable about the firm’s internal processes, such as those for purchasing, manufacturing, distribution, and customer relationship management. The use of a single informant is not uncommon in this line of research (e.g., Hewett and Bearden, 2001) or, generally speaking, in empirical research (Small and Yasin, 1997). Our pretest of the survey proved that such an informant existed in each company we contacted; we just needed to identify those people before we sent the survey. Therefore, we called each of the selected companies to identify the right contact person. After this telephone call, we mailed surveys to the identified informants. Both follow-up telephone calls and follow-up mailings were used to improve the response rate. A total of 1356 questionnaires were sent out, and 617 returned questionnaires were usable, making the usable response rate 45.50%. This high response rate can be attributed to our targeted sampling process, telephone contacts, and follow-up mailings. We evaluated nonresponse bias by comparing the early and late replies to all variables using t-test (cf. Handfield and Bechtel, 2002; Stank et al., 2001b). No significant differences were found, which suggests nonresponse bias might not be a problem in this study.

778 BAOFENG HUO, XIANDE ZHAO, WINTER NIE

Survey Instrument and Measurement Items The questionnaire includes questions about the demographic profile of the company and the questions relating to the company performance and supply chain performance, internal process integration, customer integration, and supplier integration, and trust, relationship commitment, and competitive hostility. The English version of the questionnaire was first developed and then translated into Chinese by an operations management professor in China. The Chinese version was then translated back into English by another operations management professor in Hong Kong. This translated English version was then checked against the original English version for any possible discrepancies. In mainland China, we used the Chinese version of the questionnaire; in Hong Kong, we used the bilingual version of the questionnaire. At this stage, we submitted the questionnaire to academicians and supply chain executives for their review and incorporated their feedback. Next, we pre-tested the questionnaire using a sample of 15 companies, then made additional modifications and clarifications. Internal integration, customer integration, and supplier integration are measured by nine, eleven, and thirteen items respectively. The internal integration scale, which includes data integration, information integration, and process integration, was adapted from the instrument developed by Narasimhan and Kim (2002). The customer integration scale and supplier integration scale were largely derived from Narasimhan and Kim (2002) and Morash and Clinton (1998), which emphasize information sharing and collaboration. Operational performance is measured by seven items, were developed on the basis of work by Frohlich and Westbrook (2001) and Beamon (1999). The scale of supply chain performance, measured by eight items, is similar to the one used by Stank et al. (2001b). The seven-item financial performance scale is adapted from Narasimhan and Kim (2002). The 2-item trust scale and 3-item relationship commitment scale are from Morgan & Hunt (1994). Competitive hostility is measured by twelve items that are adapted from Ward et al. (1995). In addition to adapting and modifying instruments from existing literature, we also added new measures based on interviews with executives and the pretest results. The indicators are all measured on the seven-point Likert scale. Reliability and Validity Reliability refers to the assessment of the degree of consistency between multiple measurements of a variable (Hair et al., 1998). The most commonly used measure of reliability is internal consistency. Flynn et al. (1990) suggest that the most accepted measure of a measure’s internal consistency is Cronbach’s alpha. The generally agreed lower limit for Cronbach’s alpha is 0.60 (Nunnally, 1994, 1978; Flynn et al., 1990). On the basis of the coefficient values (see Table 1), we deemed the items reliable (alphas from .695 to .944). The data collected through surveys and other empirical designs is of little use unless its reliability and validity can be demonstrated (Flynn et al., 1990). Content validity is established in this study through a thorough domain search of the existing literature, a careful synthesis and critical evaluation of the existing construct, and an iterative review by domain experts (business executives and academicians) of the construct at different stages of our instrument development. As for construct validity -- its methodological issue is generally ignored in most of previous empirical research in operations management area according to O’Leary-Kelly and Vokurka (1998), we use discriminant validity and convergent validity to evaluate construct validity. Discriminant validity reflects the degree to which measures of different latent variables are unique, whereas convergent validity relates to the degree to which multiple methods of measuring a variable provide the same results (O’Leary-Kelly and Vokurka, 1998). In our study, we assess those two kinds of validity with confirmatory factor analysis (CFA) models, as O’Leary-Kelly and Vokurka (1998) suggest that CFA method of assessing convergent and discriminant validity is a more powerful tool and requires fewer assumptions than the traditional Multitrait-Multimethod Matrix method.

Table 1. Reliability Analysis Construct Number of Questions Cronbach’s alpha Internal integration 9 0.924 Customer integration 11 0.900 Supplier integration 13 0.944 Operational performance 7 0.841 Supply chain performance 8 0.864 Financial performance 7 0.938 Competitive hostility 12 0.740 Trust with customer 2 0.695 Trust with supplier 2 0.816 Relationship commitment to customer 3 0.891 Relationship commitment to supplier 3 0.890

In the CFA model that uses LISREL, each item is linked to its corresponding construct and the covariance among those constructs are freely estimated. The model fit indices are as follows: χ2 (2796) = 10289.35, Non-Normed Fit Index (NNFI) = 0.99, Comparative Fit Index (CFI) = 0.99, Root Mean Square Error of Approximation (RMSEA = 0.075), and

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Standardized Root Mean Residual (SRMR) = 0.022. These indices indicate that the model is acceptable (Hu and Bentler, 1999). Furthermore, a construct with either loadings of indicators of at least 0.5, a significant t-value (t > 2.0), or both is considered to have convergent validity (Chau, 1997; Fornell and Larcker, 1981). For our model, the factor loadings are all greater than 0.50, and the t-values are all greater than 2.0. Therefore, convergent validity is achieved in our study. In order to assess discriminant validity, we built a constrained CFA model for each possible pair of latent constructs, in which the correlations between the paired constructs are fixed to 1. We then compared this model with the original unconstrained model, in which the correlations among constructs are freely estimated. A significant difference of the Chi-square statistics between the fixed and unconstrained models would indicate high discriminant validity (Chen and Paulraj, 2004b; Bagozzi et al., 1991; Fornell and Larcker, 1981). In our study, all the differences of are significant at the 0.001 significance level. Therefore, the discriminant validity is ensured in our study.

2χ

Cluster Analysis We use cluster analysis to classify the responding companies based on the mean scores of internal integration, customer integration and supplier integration. Both hierarchical and non-hierarchical cluster procedures are used in the study. Hair et al. (1998) suggested that researchers should use both hierarchical and non-hierarchical methods to escape their potential problems. Hierarchical procedures are used to examine the number of clusters that should be formed, and the non-hierarchical method is applied to produce clusters. We use the analysis of the agglomeration coefficient and the dendrogram to decide the number of the clusters. The agglomeration coefficient is the Squared Euclidean Distance between the two causes of the clusters being combined. A small coefficient indicates that homogenous clusters are merging. The joining of two very different clusters results in a large coefficient (Hair et al., 1998). A large percentage change in the agglomeration coefficient indicates that two non-homogeneous groups will be combined in the further agglomeration. A dendrogram is a visual representation of the steps in hierarchical cluster analysis. It identifies the clusters being combined and the values of the coefficients at each step. When the group number is changed from five to four, the percentage of change in the agglomeration coefficient is the highest (more than 20%). It indicates that the five clusters are enough to describe the problem (Figure 1). A random sampling of dendrograms also confirmed that a classification of five clusters represented the best solution.

Change of Percentage in Agglomeration Coefficient

15%

20%

25%

% o

f Cha

nge

0%

5%

10%

13→12 12→11 11→10 10→9 9→8 8→7 7→6 6→5 5→4 4→3 3→2 2→1

Change of Group Number

Figure 1. Change of Percentage in Agglomeration Coefficient to the Next Level

ANALYSES AND RESULTS Company and Respondent Profiles The profiles of the responding companies are presented in Table 2. The respondents represent a large variety of the companies from a variety of industries. More than a quarter (25.49%) of the companies are from metal, mechanical and engineering, followed by 17.86% in textiles and apparel industry, and 13.15% in electronics and electrical companies. Among the Hong Kong manufacturers, 35.61% are in textiles and apparel industry, followed by 13.66 % in electronics and electrical manufacturing industry and 9.27% in metal, mechanical and engineering. In Guangzhou, the highest response rates are also from Metal, Mechanical and Engineering industry (28.85%), followed by Textile and apparel (14.42%) and Electronics and Electrical (9.62%). In Chongqing, 35.58% are in metal, mechanical and engineering industry, followed by 11.54% in the electronics and electrical, but only 3.85% in textiles and apparel. In Shanghai, the largest percentage of respondents (42.00%) is in the metal, mechanical and engineering industry, followed by 11% in

780 BAOFENG HUO, XIANDE ZHAO, WINTER NIE

the electronics and electrical manufacturing and 10% in textile and apparent industry. In Tianjin, the highest percentage (28.16%) of respondents is also in Metal, Mechanical and Engineering industry, followed by Electronics and Electrical (19.42%), and Chemical and Petrochemical (10.65%). These percentages show the differences in the concentrations of different industries among different cities.

Table 2. Company Profile Industry Type Overall

(N= 617) HK

(N= 206) GZ

(N= 104) CQ

(N= 104) SH

(N= 100) TJ

(N= 103) Food, Beverage, alcohol,

& Cigar 30 (4.87%) 12 (5.85%) 6 (5.77%) 5 (4.81%) 1 (1.00%) 6 (5.83%)

Chem & Petrochem 39 (6.33%) 3 (1.46%) 9 (8.65%) 8 (7.69%) 8 (8.00%) 11 (10.68%) Wood & Furniture 12 (1.95%) 2 (0.98%) 4 (3.85%) 2 (1.92%) 0 (0.00%) 4 (3.88%) Pharm & Medical 11 (1.79%) 5 (2.44%) 0 (0.00%) 4 (3.85%) 0 (0.00%) 2 (1.94%) Building Material 31 (5.03%) 4 (1.95%) 7 (6.73%) 9 (8.65%) 7 (7.00%) 4 (3.88%) Rubber & Plastics 41 (6.66%) 19 (9.27%) 3 (2.88%) 3 (2.88%) 8 (8.00%) 8 (7.77%)

Metal, Mechanical & Engineering

157 (25.49%) 19 (9.27%) 30 (28.85%) 37 (35.58%) 42 (42.00%) 29 (28.16%)

Electronics & Electrical 81 (13.15%) 28 (13.66%) 10 (9.62%) 12 (11.54%) 11 (11.00%) 20 (19.42%) Textiles & Apparel 110 (17.86%) 73 (35.61%) 15 (14.42%) 4 (3.85%) 10 (10.00%) 8 (7.77%)

Toys 8 (1.30%) 8 (3.90%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) Jewelry 3 (0.49%) 2 (0.98%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 1 (0.97%)

Arts and Crafts 12 (1.95%) 1 (0.49%) 4 (3.85%) 5 (4.81%) 1 (1.00%) 1 (0.97%) Publishing and Printing 27 (4.38%) 5 (2.44%) 2 (1.92%) 10 (9.62%) 7 (7.00%) 3 (2.91%)

Company’s Sales Overall

(N= 587) HK

(N= 176) GZ

(N= 104) CQ

(N= 104) SH

(N= 100) TJ

(N= 103) <HK$5 m 190 (32.37%) 16(9.09%) 51 (49.04%) 35 (33.65%) 30 (30.00%) 58 (56.31%)

HK$5 m to < $10 m 83 (14.14%) 16(9.09%) 19 (18.27%) 13 (12.50%) 16 (16.00%) 19 (18.45%) HK$10 m to < $20 m 73 (12.44%) 27(15.34%) 5 (4.81%) 20 (19.23%) 12 (12.00%) 9 (8.74%) HK$20 m to < $50 m 93 (15.84%) 39(22.16%) 13 (12.50%) 17 (16.35%) 15 (15.00%) 9 (8.74%) HK$50 m to < $100 m 60 (10.22%) 24(13.64%) 10 (9.62%) 8 (7.69%) 15 (15.00%) 3 (2.91%)

HK$100 m or more 88 (14.99%) 54(30.68%) 6 (5.77%) 11 (10.58%) 12 (12.00%) 5 (4.85%)

Over 32% of the responding companies have the annual sales of less than HK$5 million, and 14.99% of the respondents have the annual sales of HK$100 million or more. In Hong Kong, only 9.09% of the respondents have the annual sales of less than HK$5 million, and 30.68% of the respondents have the annual sales of HK$100 million or more. In Guangzhou, 49.04% of the responding companies have the annual sales of less than HK$5 million and only 5.77% of the companies have annual sales of HK$ 100 million or more. In Chongqing, 33.65% of the companies have annual sales less than HK$5 million and 10.58% of them have annual sales of HK$100 million or more. In Shanhai, 30% of the responding companies have sales of less than HK$5 million and 12% of them have sales of HK$100 million or more. In Tianjin, 56.31 % of respondents have the annual sales of less than HK$5 million, and only 4.85% of the companies have the annual sales of HK$100 million or more. These numbers show that Hong Kong companies are relatively larger than the Mainland companies. Shanghai also has more large companies than Guangzhou, Tianjin and Chongqing. The profiles of the respondents are listed in table 3 and table 4. From table 3, we can see that about 40% respondents are the top managers of the companies. 57% respondents are functional managers of the companies. About 15.6% respondents are from the departments of marketing in the companies. Table 4 shows that about 27% respondents have been on their positions for 1 to 3 years, and nearly half of the respondents took their positions in the past 6 years. About one quarter of respondents has been in charge of their department or the whole companies for 7 to 12 years. Another one quarter of respondents has stayed in their positions for more than 12 years. About three quarter of the respondents has been in their position for more than 3 years. So the respondents should have been familiar with the companies’ activities we surveyed and the data collected from them should be reliable.

Table 3. Positions of Respondents

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Table 4. Years in the

Positions for the Respondents

Position Sub-position Percentage Top Manager 39.9%

Other Manager 29.8% Marketing Manager 15.6% Operation Manager 5.0%

Accounting/Finance Manager 4.9%

Functional Manager

Logistics/supply chain Manager 1.6% Others 3.2% Total 100.0%

The Taxonomy of Supply Chain Integration

Years in the position Percentage Cumulative Percentage 1~3 years 26.9% 26.9% 4~6 years 22.9% 49.8% 7~12 years 24.6% 74.4% Above 12 years 25.6% 100%

The cluster analysis result shown in section 3.4 indicates that the responding companies can be classified into five groups. Therefore a taxonomy of the SCI can be developed based on the result of the cluster analysis and the first hypothesis is supported. The means of the three indicators and the cluster membership are shown in Table 5 and Figure 2. To examine the characteristics of the five groups of companies, we use the cutoff values of 3.0 and 4.5 to indicate “low”, “medium” and “high” level of integration. When the value of the mean integration score is below 3.0, we label the company as having “low” level of integration. When the integration score is from 3.0 to 4.5, we label it as “medium” level of integration. When the integration score is above 4.5, we label it as “high” level of integration. The characteristics of the five clusters are discussed below: Cluster 1: Medium Customer-oriented Integration Cluster 1 is labeled as “medium customer-oriented integration” because this group of companies pays more attention to customer integration and internal integration than to supplier integration. The internal integration and customer integration scores (4.16 and 3.63 respectively) are all within the medium range, but the supplier integration is low (2.36). These companies are customer-oriented because they integrate the internal and customer’s related processed to serve the customers better, but they put in little effort to integrate with the supplier. Cluster 2: High Customer-oriented Integration This cluster has similar pattern of integration with cluster 1. They place more emphasis on customer integration and internal integration than supplier integration. However, the levels of internal integration and customer integration (5.24 and 4.87) in this group of companies are much higher than those in cluster 1 and are all in the high range. Therefore, we label them as “high customer-oriented integration”. The level of supplier integration (3.59) is also higher than the corresponding level in cluster 1. For a company in such a supply chain, they are more dependent on their customers than their supplier, and thus pay more attention to integrate with their customer and internally in order to better serve them. Cluster 3: Low Integration Companies in this cluster have the lowest levels of SCI for all three types. They have not paid attention to supply chain integration in any of the three areas. We labeled them as “low integration”. Cluster 4: Full Integration Companies in Cluster 4 achieve the highest level of integration in all three areas. They have fully integrated their internal processes, with their suppliers and customers. Therefore we labeled them as “full integration”. Cluster 5: Medium Integration Cluster 5 has the medium scores for all three types of SCI. All the integration scores are between 3.0 and 4.5. So, we label them as “medium integration”.

Table 5. Cluster Analysis

Cluster 1 (n=108)

Cluster 2 (n=136)

Cluster 3 (n=111)

Cluster 4 (n=111)

Cluster 5 (n=151)

F-value

Internal Integration 4.16a (2,3,4,5) b 5.24 (1,3,5) 2.02 (1,2,4,5) 5.43 (1,3,5) 3.38 (1,2,3,4) 400.662*

Customer Integration 3.63 (2,3,4,5) 4.87 (1,3,4,5) 2.66 (1,2,4,5) 5.67 (1,2,3,5) 4.28 (1,2,3,4) 244.546*

Supplier Integration 2.36 (2,3,4,5) 3.59 (1,3,4,5) 1.83 (1,2,4,5) 5.47 (1,2,3,5) 4.06 (1,2,3,4) 492.027*

a The degree of integration based on 7-point Likert scale, ‘‘1’’ represents ‘‘Not at all’’ and ‘‘7’’ represents

782 BAOFENG HUO, XIANDE ZHAO, WINTER NIE

‘‘Extensive.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance. * Significant at 0.05 level.

Supply Chain Integration

1.52

2.53

3.54

4.55

5.56

InternalIntegration

CustomerIntegration

SupplierIntegration

Medium Customer-orientedHigh Customer-orientedLow integration

Full integration

Medium integration

Figure 2. Taxonomy of Supply Chain Integration

These five types of supply chain integration structure can be further classified into two categories: balanced supply chain integration and unbalanced supply chain integration. For the balanced supply chain integration, the three types of integration are all high, all medium, or all low. This type of supply chain integration structure emphasizes all integration to the similar extent. For example, companies with full integration have a high level of internal integration, a high level of customer integration, and a high level of supplier integration. Medium integration supply chain and low integration supply chain are also balanced supply chain. However, some companies emphasize one or two types of integration much more than other types of integration. They have unbalanced supply chain. High customer-oriented integration and medium customer-oriented integration are such examples. The result supports Hypothesis 1. Figure 2 presents the supply chain integration structure based on the taxonomy of three types of SCI. We will use ANOVA to test the proposed hypotheses in next section.

The impact of Supply Chain Integration on Performance Operational performance is measured from four aspects: 1). Flexibility, such as quickly modify products to meet requirements, quickly introduce new products, and quickly respond to changes. 2). Delivery, such as on-time delivery, short lead-time. 3). Quality, such as high level of customer service. 4). Cost, such as low inventory. These items are highly related with SCI activities. From Table 6, we can see that the manufacturers in full integration supply chain have the best operational performance, especially for the item of quickly introducing new products and quickly responding to changes. Manufacturers in low integration supply chain have the worst operational performance. Companies in high customer-oriented supply chain produce the second highest operational performance. Companies in medium customer-oriented or medium integration supply chain achieve the medium operational performance. The result shown in Table 6 supports Hypothesis 2.

Table 6. The Impact of Supply Chain Integration on Operational Performance Medium

Customer-oriented (Cluster 1)

High Customer-oriented

(Cluster 2)

Low Integration(Cluster 3)

Full Integration(Cluster 4)

Medium Integration(Cluster 5)

Total Sig.

Quickly modify products to meet requirements

5.29 a(2,4) b 5.96(1,3,5) 5.05(2,4) 6.23(1,3,5) 5.26(2,4) 5.55 .000

Quickly introduce new products

4.65(2,3,4) 5.35(1,3,4,5) 4.16(1,2,4,5) 5.77(1,2,3,5) 4.68(2,3,4) 4.93 .000

Quickly respond to changes

4.83(2,4) 5.53(1,3,4,5) 4.51(2,4) 6.02(1,2,3,5) 4.79(2,4) 5.13 .000

On-time delivery 5.63(2,4) 6.15(1,3,5) 5.44(2,4) 6.34(1,3,5) 5.44(2,4) 5.79 .000 Short lead-time 5.33(2,4) 5.82(1,3,5) 5.22(2,4) 6.09(1,3,5) 5.23(2,4) 5.53 .000 High level of customer service

5.38(2,4) 5.97(1,3,5) 5.39(2,4) 6.29(1,3,5) 5.32(2,4) 5.66 .000

A TAXONOMY OF SUPPLY CHAIN INTEGRATION IN CHINA 783

Low inventory 4.69(4) 4.98(-) 4.66(4) 5.19(1,3,5) 4.63(4) 4.82 .013 a Based on 7-point Likert scale, ‘‘1’’ represents ‘‘Strongly Disagree’’ and ‘‘7’’ represents ‘‘Strongly Agree.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance. Most of the measures for supply chain performance are similar to the items of operational performance except that supply chain performance focuses on the whole supply chain, but operation performance only focuses on the manufacturer in the supply chain. Table 7 presents the results of the supply chain performance across five levels of supply chain integration. Full integration supply chain and high customer-oriented integration supply chain produces the best supply chain performance. Similar to operational performance, low integration supply chain has the lowest supply chain performance scores and medium customer-oriented supply chain and medium integration supply chain have the medium supply chain performance. The results support hypothesis 3.

Table 7. The Impact of Supply Chain Integration on Supply Chain Performance

Medium

Customer-oriented (Cluster 1)

High Customer-oriented

(Cluster 2)

Low Integration(Cluster 3)

Full Integration(Cluster 4)

Medium Integration(Cluster 5)

Total Sig.

Quickly modify products to meet requirements

4.92 a

(2,4) b5.54

(1,3,5) 4.76 (2,4)

5.88 (1,3,5)

4.92 (2,4) 5.20 .000

Quickly introduce new products

4.31 (2, 4)

4.99 (1,3,4,5)

3.95 (2,4,5)

5.49 (1,2,3,5)

4.45 (2,3,4) 4.64 .000

Supply chain process is getting shorter

4.26 (2,4)

5.10 (1,3,5)

3.97 (2,4,5)

5.21 (1,3,5)

4.46 (2,3,4) 4.61 .000

Satisfied with the speediness of the supply chain process

4.31 (2,4)

4.98 (1,3,4,5)

4.29 (2,4)

5.38 (1,2,3,5)

4.46 (2,4) 4.68 .000

Supply chain process is efficient

4.43 (2,4)

5.12 (1,3,5)

4.34 (2,4)

5.37 (1,3,5)

4.44 (2,4) 4.74 .000

On-time delivery 5.12 (2,4)

5.63 (1,3,5)

5.02 (2,4)

5.81 (1,3,5)

4.98 (2,4) 5.30 .000

Low total inventory 4.43 (2,4)

4.86 (1,3,5)

4.35 (2,4)

5.02 (1,3,5)

4.37 (2,4) 4.60 .000

High level of customer services

4.79 (2,4)

5.50 (1,3,5)

4.73 (2,4)

5.66 (1,3,5)

4.68 (2,4) 5.06 .000

a Based on 7-point Likert scale, ‘‘1’’ represents ‘‘Strongly Disagree’’ and ‘‘7’’ represents ‘‘Strongly Agree.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance. Table 8 shows that level of supply chain integration significantly influences financial performance of the firm within the supply chain. Manufacturers in full integration supply chain or high customer-oriented integration supply chain have the best financial performance. Manufacturers with low integration supply chain have the worst financial performance. Manufacturers in high customer-oriented integration supply chain have better financial performance than those in medium customer-oriented or medium integration supply chain. Manufacturers in medium customer-oriented and medium integration supply chain have better financial performance than those in low integration supply chain. The result strongly supports Hypothesis 4.

Table 8. The Impact of Supply Chain Integration on Financial Performance

Medium

Customer-oriented (Cluster 1)

High Customer-oriented

(Cluster 2)

Low Integration(Cluster 3)

Full Integration(Cluster 4)

Medium Integration(Cluster 5)

Total Sig.

Growth in sales 4.46 a(3) b 4.68(3,5) 3.86(1,2,4) 4.86(3,5) 4.22(2,4) 4.41 .000 Growth in profit 3.92(3) 4.11(3,5) 3.35(1,2,4) 4.35(3,5) 3.58(2,4) 3.85 .000 Growth in market share 4.09(3,4) 4.48(3,5) 3.45(1,2,4) 4.56(1,3,5) 3.82(2,4) 4.08 .000 Return on investment (ROI) 3.86(2,3,4) 4.31(1,3,5) 3.39(1,2,4) 4.33(1,3,5) 3.62(2,4) 3.90 .000 Growth in ROI 3.72(2,3,4) 4.12(1,3,5) 3.16(1,2,4) 4.35(1,3,5) 3.50(2,4) 3.77 .000 Return on sales 4.05(3,4) 4.26(3,5) 3.44(1,2,4) 4.59(1,3,5) 3.76(2,4) 4.01 .000 Growth in return on sales 3.85(3,4) 4.12(3,5) 3.31(1,2,4) 4.48(1,3,5) 3.61(2,4) 3.87 .000

a Based on 7-point Likert scale, ‘‘1’’ represents ‘‘Worst’’ and ‘‘7’’ represents ‘‘Best.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance.

784 BAOFENG HUO, XIANDE ZHAO, WINTER NIE

From the results of above three tables, we can find that full integration supply chain in which all three types of SCI are high achieves the best operational performance, the best supply chain performance and the best financial performance. However, low integration supply chain in which all three types of SCI are low achieves the worst performance in all three areas. High customer-oriented supply chain produces the second best three types of performance and medium customer-oriented and medium integration supply chain have the medium performance. These results clearly indicate that higher level of SCI can help companies to enhance their performance. Supply Chain Integration, Trust and Relationship Commitment In this study, trust is measured by manufacturer’s trust with the customer and manufacturer’s trust with the supplier. From Table 9, we can see that, the manufacturers in full integration supply chain have the highest level of trust with their customers and suppliers. The manufacturers in low integration supply chain, medium customer-oriented integration supply chain, or medium integration supply chain have the lowest level of trust. The manufacturers in high customer-oriented supply chain have the medium level of trust. The result supports hypothesis 5.

Table 9. Trust and Supply Chain Integration

Medium

Customer-oriented (Cluster 1)

High Customer-oriented

(Cluster 2)

Low Integration(Cluster 3)

Full Integration(Cluster 4)

Medium Integration (Cluster 5)

Total Sig.

Our major customer can be counted on to do what is right.

4.84 a

(4) b5.15 (4)

4.77 (4)

5.54 (1,2,3,5)

5.03 (4) 5.07 .000

There is a high level of trust between us and our major customer

5.06 (2,4)

5.65 (1,3,4)

5.14 (2,4)

6.14 (1,2,3,5)

5.37 (4) 5.47 .000

Our major supplier can be counted on to do what is right.

4.62 (4)

4.99 (4)

4.70 (4)

5.56 (1,2,3,5)

4.89 (4) 4.95 .000

There is a high level of trust between us and our major supplier

4.80 (2,4)

5.28 (1,3,4)

4.71 (2,4)

5.81 (1,2,3,5)

5.05 (4) 5.13 .000

a Based on 7-point Likert scale, ‘‘1’’ represents ‘‘Strongly Disagree’’ and ‘‘7’’ represents ‘‘Strongly Agree.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance.

Similarly, relationship commitment is measured by manufacturer’s relationship commitment to the customer and manufacturer’s relationship commitment to the supplier. Table 10 shows that the manufacturers in full integration supply chain have the highest level of relationship commitment to their customers and suppliers. Manufacturers in high customer-oriented supply chain have the medium level of relationship commitment. Manufacturers in low integration supply chain, medium customer-oriented supply chain, or medium integration supply chain have the lowest level of relationship commitment. Therefore, the result supports hypothesis 6. From above analyses, we can find that trust and relationship commitment are highly related with SCI. The manufacturers in full integration supply chain in which three types of integration have the highest level of trust with the customers and the suppliers and the highest level of relationship commitment to the customers and the suppliers. The companies in high customer-oriented supply chain have the medium level of trust and relationship commitment. These results show that trust and relationship commitment are very important for supply chain integration.

Table 10. Relationship Commitment and Supply Chain Integration

Medium

Customer-oriented (Cluster 1)

High Customer-oriented

(Cluster 2)

Low Integration(Cluster 3)

Full Integration(Cluster 4)

Medium Integration(Cluster 5)

Total Sig.

The relationship that our firm has with our major customer is something we are very committed to.

5.96 a

(2,4)b6.27

(1,3,5) 5.85 (2,4)

6.46 (1,3,5)

5.76 (2,4) 6.05 .000

The relationship that our firm has with my major customer is something our firm intends to maintain indefinitely

6.18 (4)

6.29 (5)

6.03 (4)

6.53 (1,3,5)

5.93 (2,4) 6.18 .000

The relationship that our firm has with my major customer deserves our firm's maximum effort to maintain.

6.00 (4)

6.18 (-)

6.04 (4)

6.46 (1,3,5)

5.87 (4) 6.10 .000

A TAXONOMY OF SUPPLY CHAIN INTEGRATION IN CHINA 785

The relationship that my firm has with my major supplier is something we are very committed to.

5.21 (2,4)

5.65 (1,3,4)

5.25 (2,4)

6.07 (1,2,3,5)

5.36 (2,4) 5.51 .000

The relationship that my firm has with my major supplier is something my firm intends to maintain indefinitely.

5.50 (2,4)

5.84 (1,3,4,5)

5.41 (2,4)

6.20 (1,2,3,5)

5.45 (2,4) 5.67 .000

The relationship that my firm has with my major supplier deserves our firm's maximum effort to maintain.

4.99 (2,4)

5.51 (1,3)

5.04 (2,4)

5.88 (1,3,5)

5.28 (4) 5.35 .000

a Based on 7-point Likert scale, ‘‘1’’ represents ‘‘Strongly Disagree’’ and ‘‘7’’ represents ‘‘Strongly Agree.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance.

The Impact of Competitive Hostility on Supply Chain Integration Competitive hostility includes three dimensions: local competition, foreign competition, and operational challenges. Local competition is about the competition in local markets, such as keen competition in local markets, declining demand and excessive supply in local market. International competition is about the competition in foreign markets, such as keen competition in foreign markets, declining demand and excessive supply in foreign market. Operational challenges include low profit margins, producing to the required quality standard, unreliable vendor quality, lack of ability for innovation and technology adoption, rising business costs, and lack of availability of suitable labor.

Table 11. Competitive Hostility and Supply Chain Integration

Medium

Customer-oriented (Cluster 1)

High Customer-oriented

(Cluster 2)

Low Integration(Cluster 3)

Full Integration(Cluster 4)

Medium Integration(Cluster 5)

Total Sig.

Keen competition in local markets

5.07 a

(-)b5.01 (-)

4.99 (-)

5.31 (-)

4.89 (-) 5.04 .446*

Declining demand in local market

4.28 (-)

4.19 (-)

4.75 (-)

4.45 (-)

4.32 (-) 4.39 .209*

Local competition

Excessive supply in local market

4.26 (-)

4.61 (-)

4.66 (-)

4.66 (-)

4.52 (-) 4.54 .464*

Keen competition in foreign markets

3.40 (2,4)

4.17 (1,3)

2.81 (2,4,5)

4.48 (1,3)

3.94 (3) 3.79 .000

Declining demand in foreign market

3.31 (4)

3.76 (3,4)

2.90 (2,4,5)

4.28 (1,3)

3.80 (3) 3.63 .000

International competition

Excessive supply in foreign market

3.21 (2,4,5)

3.84 (1,3)

2.81 (2,4,5)

4.30 (1,3)

4.09 (1,3) 3.69 .000

Low profit margins

4.61 (-)

5.03 (3,5)

4.34 (2)

4.79 (-)

4.38 (2) 4.63 .002

Producing to the required quality standard

5.65 (2,4)

6.26 (1,3,5)

5.54 (2,4)

6.18 (1,3,5)

5.69 (2,4) 5.87 .000

Unreliable vendor quality

5.19 (4)

5.42 (-)

5.50 (-)

5.77 (1)

5.27 (-) 5.42 .046

Lack of ability for innovation and technology adoption

5.11 (2,4,5)

5.31 (-)

4.99 (4)

5.62 (1,3)

5.20 (-) 5.25 .015

Operational challenges.

Rising business costs

5.67 (-)

5.79 (-)

5.68 (-)

5.79 (-)

5.46 (-) 5.67 .184*

786 BAOFENG HUO, XIANDE ZHAO, WINTER NIE

Lack of availability of suitable labor

5.25 (-)

5.16 (-)

4.99 (-)

5.45 (-)

5.07 (-) 5.18 .176*

a Based on 7-point Likert scale, ‘‘1’’ represents ‘‘Strongly Disagree’’ and ‘‘7’’ represents ‘‘Strongly Agree.’’ b Number in parentheses indicates the cluster from which this cluster is significantly different at 0.05 level of significance. *Not significant at 0.05 level of significance.

From Table 11, we can find that there is no difference in local competition among the five types of supply chain integration. However, international competition is significantly different across five groups. Companies facing the highest level of international competition are more likely to be in full integration supply chain or high customer-oriented integration supply chain. Companies facing the lowest level of international competition are more likely to be in low integration supply chain. Companies facing the medium level of international competition are more likely to be in medium customer-oriented or medium integration supply chain. This may be because companies facing highest level of international competition have a stronger drive to implement SCI and SCI can better meet customer’s requirement in markets facing international competition. Most of the items of operational challenges are also significantly different for the five types of supply chain integration. The results are similar to those of international competition. Therefore, hypothesis 7 is partially supported. When companies are facing keen competition in the international markets and higher degree of operational challenges, they tend to adopt SCI measures to deal with the competitive environment.

DISCUSSION AND CONCLUSION

Supply Chain Integration Structure Many previous studies have identified various types of supply chain integration. However, few studies have presented a taxonomy of supply chain integration to structure supply chain. This study firstly uses three types of integration to classify the supply chain and find that there are five different types of supply chain structure. Full integration supply chain that has three high level of SCI is the objective of the companies pursuing SCI. The balanced supply chain pays equally attention to the internal, the customer, and the supplier. The unbalanced supply chain emphasizes more on the customer and most of them are customer-oriented companies. The unbalanced high customer-oriented supply chain may also achieve the top performance. So, the company should pay more attention to the customers. The taxonomy of supply chain integration provides significant contributions to supply chain management research. Through the perspective of integration, supply chain is structured and the special knowledge of the characteristics of different types of supply chain can be developed. Different supply chain integration structure has different performance and different level of trust and relationship commitment. Competitive hostility as the driver of SCI also has different impact on different supply chain. Improving Performance Through Supply Chain Integration The critical role of the supply chain in meeting market changes and the potential benefits of integrating the supply chain can no longer be ignored (Narasimhan and Kim, 2002). Full potential can be realized only if the inter-relationships among the different parts of the supply chain are recognized, and proper alignment is ensured between the design and execution of the company’s competitive strategy (Stevens, 1989). The relationship between SCI and performance has been discussed over a vast body of SC and operations literature. For example, Stank et al. (2001a) suggested that collaboration with SC partners facilitates internal collaboration, which in turn enhances logistics performance. Frohlich & Westbrook (2001) demonstrated that SC companies with the widest degree of the arcs of integration achieve the highest level of performance improvement. Such previous researchers only tested the association between integration and SC performance. This study compares operational performance, supply chain performance, and financial performance across five types of supply chain integration that has different level of internal integration, customer integration, and supplier integration in the context of manufacturing companies in China. With the growing awareness of supply chain integration during the past decade, it has become more and more important to understand the relationship between integration and performance. This research adds to the supply chain management literature by empirically testing the ANOVA models of performance and supply chain integration. It also makes several other contributions. The shortcoming of most existing studies lies in their use of operational performance as the sole performance measure. Few previous articles have included the various types of integration and different measures of performance in a single study. Therefore, our research supplements previous research by linking internal integration, customer integration, and supplier integration with operational performance, supply chain performance, and financial performance. In embracing both internal and external integration and incorporating different measures of performance, our study adds a higher level of

A TAXONOMY OF SUPPLY CHAIN INTEGRATION IN CHINA 787

comprehensiveness and richness in the supply chain integration framework and enhances our understanding of the impact of supply chain integration on performance. These findings enhance our understanding of how SCI influence performance and provide important guidelines for SC managers to enhance their financial performance through SCI. The Impact of Trust and Relationship Commitment on Supply Chain Integration The result of our hypothesis testing shows that the companies with different supply chain integration have different level of manufacturer’s trust and relationship commitment to the customer/supplier. That means that a company with a higher level of trust with customers/suppliers is more likely to get a high level of SCI. The existence of trust can help to reduce the perceived risks in the relationships and thus enhance SCI. This conclusion is also supported by some other researchers. Morgan & Hunt (1994) and Moore (1998) stated that trust and relationship commitment are salient and inter-related elements in structuring the inter-organizational interactions. The results of our study highlight the importance of trust between the manufacturer, its customer and its supplier. Full integration supply chain that have the highest three types of integration have the highest level of relationship commitment. That means that companies with a stronger relationship commitment to customers/suppliers are more likely to have a greater extent of customer/supplier integration. Since SCI requires investment in time and other resources, the partners must have strong commitment in the relationship before they are willing to invest in SC integration activities. With a higher level of relationship commitment, the customers or the suppliers are more likely to cooperate with the manufacturer. So the manufacture will have less difficulty to integrate the customers or suppliers with its own operation process in the SC to achieve the competitive advantages. This finding is partially supported by the results of several previous studies in different contexts. Morgan & Hunt, (1994) and Chen & Paulaj (2004a) found that SC partners become integrated into their key customers’ business processes and more tied to established goals with higher relationship commitment. However, no previous studies have investigated the impact of relationship commitment on the SCI. Our results indicate that a company needs to gain commitment to the relationship with its supplier or customers in order to achieve a higher degree of integration with them. Competitive Hostility as the Driver of Supply Chain Integration Few precious studies investigated the relationship between competitive hostility and SCI. Our study finds that successful firms facing greater perceived competitive hostility respond with a greater emphasis on SCI. The companies have to identify the operational challenges and international competition that they are facing, and integrate the activities of the customers and suppliers to pursue the better performance. Local competition are not different across five types of supply chain may be for that the competition in local markets have a similar impact on the SCI for different companies, it is hard to be a differentiator driver for the companies SCI. With more and more competitors entering the market, companies are facing much fiercer competition and operational challenges. Different companies have to work harder together to survive, as well as share information and participate more in the overall strategic formulation and planning. Limitations and Further Researches There are some limitations in this study. Though this study includes a wide representation in terms of industry types, it might be interesting to focus on two industries and perform a more in-depth comparison. Validating the findings of this study in another country also would be an interesting extension. So, further in-depth studies or cross-cultural researches can be conducted. We establish the taxonomy of supply chain integration through the classification of three types of SCI. We investigated the differences of performance, trust, relationship commitment, and competitive hostility of the companies with different type of supply chain integration. Other aspects of the companies with different supply chain integration can also be examined in the future studies.

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