Power source and its effect on customer–supplier relationships: An empirical study in Yangtze River Delta

Int. J. Production Economics 146 (2013) 118–128

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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

Power source and its effect on customer–supplier relationships: An empirical study in Yangtze River Delta Yongyi Shou a, Yi Feng b,*, Jingjing Zheng a, Guofeng Wang b, Nyamah Edmond Yeboah b a b

School of Management, Zhejiang University, 310058, Hangzhou, PR China School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, PR China

art ic l e i nf o

a b s t r a c t

Article history: Received 29 January 2012 Accepted 3 March 2013 Available online 13 March 2013

Power has been considered as an important factor in customer–supplier relationship management and supply chain integration. Many previous researches borrowed the definition of power from marketing, economics or sociology. Quite few researchers studied it from the perspective of operations management. However, power effect can be observed from pricing control, inventory control and Just-In-Time (JIT), operations control, channel structure control, and information control. This study tries to fill in the gap based on the empirical study in Yangtze River Delta. It is found that there is a close relationship between the firm's resources and power in operations management. Also, firm's power is also critical in determining its power position with respect to its supplier or customer. & 2013 Elsevier B.V. All rights reserved.

Keywords: Customer–supplier relationship Resource dependency theory Power Yangtze River Delta

1. Introduction With the intensive competition in market, cost efficient customer–supplier management and supply chain management (SCM) are not sufficient for survival. Being triple-A (agility, adaptability, alignment) is the approach for competitiveness (Lee, 2004) and managing according to reason is a new philosophy in supply chain management (Xu and Xu, 2011). Supply chain integration, customer–supplier collaboration and partnership have been the trend in business practice and management across industries. Supply chain integration is also technically feasible. Based on SCOR (Supply Chain Operations Reference) model, Zdravković et al. (2011) tried to develop a semantic language to describe the operations, thus system interoperability in supply chain can be possible. Applying SCOR, Li et al. (2011a, b) found each decision area of the model has positive impacts on both supply chain quality performance and firm level business performance. Technological applications such as RFID (Radio Frequency Identificator) (Kumar et al., 2011), lean supply chain modeling with Petri nets (Ma et al., 2011), grid-based supply chain modeling (Sepehri, 2012), information systems and enterprise systems (Li, 2006; Xu 2011a, b) and infrastural manufacturing decisions (Li, 2005) provide means to improve supply chain collaboration and market performance. Li and Warfield (2011) described the latest research in supply chain quality coordination and assurance. * Corresponding author. Tel.: þ 86 15928720216. E-mail addresses: [email protected] (Y. Shou), [email protected] (Y. Feng), [email protected] (J. Zheng), [email protected] (G. Wang), [email protected] (N.E. Yeboah). 0925-5273/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ijpe.2013.03.003

van der Vaart and van Donk (2008) reviewed 33 survey-based research and found that supply chain integration greatly improved company performance. Various researchers have also described and analyzed the potential benefits of the customer–supplier strategic integration partnerships and long-term relationship (Watts and Hahn, 1993; Cooper et al., 1997; Carr and Pearson, 1999; Zsidisin and Ellram, 2003; Perona and Saccani, 2004; Li, 2011; Li et al., 2011a, b). The rationale behind customer–supplier partnership is to combine partners' resources and perspectives into a firm's value propositions, thus allowing both to excel in performance (Yeung et al., 2009). However, it is also found that hidden information and actions among the partners (Narayanan and Raman, 2004) refrain companies from cooperating with their supply chain partners (Fawcett and Magnan, 2002). Cox and Chicksand (2005) studied UK fresh/frozen beef supply chain, and observed that when there is a dominant buyer in the chain, one-sided commercial benefits will flow from the suppliers to the buyer (hereafter, we use buyer and customer interchangeably). Similar phenomenon has also been observed by other researchers. For instance, based on the empirical studies in UK grocery industry, Ogbonna and Wilkinson (1998) found that the relationship between retailers and manufacturers cannot be simply characterized by partnership. There are different relationships between major brand manufacturers and the top three or four retailers, between some large retailers and secondary manufacturers, and between retailers and manufacturers of own label brands. Fishman (2003) also described a WalMart supplier went to bankruptcy because of Wal-Mart's low pricing, where partnership and win-win perspective cannot be observed. Ketchen and Giunipero (2004) suggested that while the

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SCM literature often seem to assume that “a rising tide lifts all boats… a chain member may exploit its partners for its own gain.” In summary, as pointed out by van der Vaart and van Donk (2008), there is still little consensus on how to capture the essence of supply chain integration and customer–supplier partnership. Investigation in customer–supplier power distribution is critical to understand the customer–supplier partnership attitude, patterns and practices. Focusing on Chinese practices, Li (2000) tried to find the sources of competitiveness and performance of Chinese manufacturers. Competencies in marketing, product innovation, manufacturing and human resource development have been analyzed. The author found that besides market competency, delivering order on time was highly emphasized by Chinese managers. Li (2012) explored how information technology facilitates supply chain collaboration. It is found that collaborative forecasting and replenishment significantly benefit operations performance, and better operations performance has significant impact on firms' marketing performance. Liu et al. (2009) further provided an integrated framework of relationship stability, trust, Chinese Guanxi and relational risk in marketing channel in order to achieve relational benefits and competitive advantages. The study indicated that buyers locked in a stable relationship will face relational risk which is the result of power and dependence (Delerue, 2004). Thus, by looking into the success of Chinese manufacturing, factors such as marketing competency, technology facilitation, and trust versus Chinese personal relationship have been disclosed. More complex or systematic factors such as power and dependence in customer–supplier relationship and supply chain are needed to be investigated deeply. Recently, investigation in power and customer–supplier partnership or supply chain integration has attracted great research interest. Researchers study the power in SCM from different angles and perspectives. Most research focuses on the effects of the relationship of power and SCM (e.g. Crook and Combs, 2007; Griffith et al., 2006), trust and SCM (e.g. Johnston et al., 2004; McCarter and Northcraft, 2007), power and trust in SCM (e.g. Yeung et al., 2009), power, relationship commitment and SCM (e.g. Sheu and Hu, 2009), and power (a)symmetry or power and dependency (e.g. Brown et al., 1995; Duffy and Fearne, 2004; Cox and Chicksand, 2005). Specifically, Crook and Combs (2007) argued that strong members reap most of the direct benefits, and weak members can often gain by building switching costs with strong members. Griffith et al. (2006) found that the perceived procedural and distributive justice of a supplier's policies enhance the long-term orientation and relational behaviors of its distributor. Johnston et al. (2004) found that higher levels of inter-organizational cooperative behaviors are strongly linked to the supplier's trust in the buyer firm. McCarter and Northcraft (2007) proposed that the presence of trust and power in the supply chain increases the probability of a firm's investment in a supply chain alliance. Based on the data from Chinese supply chains, Yeung et al. (2009) found that both trust and coercive power improve internal and supplier integration, and that coercive power improves supplier integration with or without the presence of trust. Sheu and Hu (2009) found that the sophisticated utilization of independent incentives through channel relationship commitment as the key mediator determines the channel performance. Brown et al. (1995) investigated how retailer commitment affects performance in the channel and argued that the symmetry of power within the channel moderated the linkage. Duffy and Fearne (2004) proposed that partnership can help a firm to improve its performance, and power imbalances have a detrimental effect on the sharing of partnership benefits. Most researches define power from marketing, economics and sociology perspectives. Dahl (1957) defines power as the ability of

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one individual or group to get another unit to do something that it would not otherwise have done. From marketing strategy perspective, El-Ansary and Stern (1972) applied this notion to distribution channels by operationally defining power as the ability of a channel member to control the decision variables in the marketing strategy of another member at a different level in the channel of distribution. French and Raven (1959) focused on social and psychological dimensions of power-dependence relationship, and defined five powers which seem common and important: reward, coercive, legitimate, referent, and expert powers. These were further classified into two categories of coercive and non-coercive powers. Summarizing the previous researches, marketing literature is dominated by the focus of power implication in channel relationships, and economics literature is concerned with the market structure and its locus of control within it (Ogbonna and Wilkinson, 1998). Few researches studied the power in customer–supplier relationships and supply chain integration from operations management perspective. For a company, power affects its pricing strategy, inventory control and JIT, operations control, channel or distribution structure management, and information management (Munson et al., 1999). Clearly, power and its effect will address most aspects of operations management, thus affect companies' performance. Thus, we argue that power is also an operations management area, without understanding power and its implication to this perspective, it could not be managed effectively. This paper is a step towards filling this gap. It presents the results derived from empirical study in Yangtze River Delta of China, which includes the city of Shanghai and two provinces of Jiangsu and Zhejiang. Since China is an extremely diverse country, although economic reform has helped all regions to develop, it has also served to increase regional disparities (Lin et al., 2002). In the recent years, the Gross Domestic Product (GDP) of Yangtze River Delta is around 20% of the whole country (National Bureau of Statistics of China: www.stats.gov.cn). Especially, Shanghai is also home to one of the largest container ports and to one of the two stock markets in China. Thus, Shanghai is not only a manufacturing base, but also a major financial and logistical center, playing the leading and supporting roles in the economic development of the region and the whole country. Therefore, Yangtze River Delta is a typical representation of the success of fast growing Chinese manufacturing and economics. As pointed out by Flynn et al. (2007), manufacturing has probably made the greatest contribution to China's stunning rate of growth. Moreover, as the global market has become more cost competitive after China's WTO accession, Chinese manufacturers have been understanding the importance of competing on other competitive dimensions including customer–supplier relationship besides cost. Personal networking (in Chinese: Guanxi) and trust (in Chinese: Xinyong) based customer–supplier collaboration (Lee and Humphreys, 2007; Yeung et al., 2009) contributes to the Chinese manufacturing advantage. It is because such relationship mechanism is more adaptive to changing environments due to its flexibility. Especially, in emerging markets where national economies grow rapidly in a context of immense market uncertainty and regulatory variability, relational norms and mutual trust provide supply chain partners with much needed flexibility (Liu et al., 2009). For instance, Chinese fiber production amounted 60% of the total world production in 2010, and had observed 13% annual increase for the past five years (Economy Daily, 2011). By the on-site visit and interviews to the fiber and fabric companies in the Yangtze River Delta reported in Feng et al. (2007), some interesting practices in customer–supplier relationship have been observed. For instance, the sales transaction can be conducted without a formal contract since personal relationships are viewed as more reliable than a written contract (legitimate power) in

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China (Leung et al., 2005); information system is not common, however, information exchange is still efficient. On the other hand, power is also observed in the customer–supplier relationship. For instance, managers try to hide inventory information since they are afraid that if their supplier knows their inventory is low, the supplier may raise price. Therefore, deeply looking into the complex customer–supplier relationship will help companies balance their position well in the supply chain and make the chain collaborate smoothly. Thus, this research tries to identify the common features in power and its effect on operations management, distinguish the relationship of power and customer–supplier relationship, and show the common strategies of managing power. The key research questions this study tries to address are as follows: (1) From the viewpoint of operations management, what are the key features of power and its effect on operations management? (2) What are the relationships between a firm's power and its resources? With these key features, we also can understand the causes of the common practices between customers and suppliers in Chinese manufacturing, and the causes of one party's dependence on the other to reduce cost or improve competitiveness.

2. Literature review and research hypotheses Resource dependency theory provides the major organizational view regarding power's formation and management in interorganizational relations. Here, firms are viewed as embedded in a web of exchange relationships within an uncertain environment, and the firms are interdependent entities seeking to manage uncertainties that are affecting them (Pfeffer and Salancik, 1978; Pfeffer, 1988). The interdependencies create patterns of dependency, a situation in which firms that own or control valuable, scarce resources hold power over firms seeking those resources to the extent that the dependency is not mutual (Pfeffer and Salancik, 1978). A firm's power resides in others' dependence on it for resources (Emerson, 1962), especially from its customer or supplier. Resources create dependency when they are important, when control over them is relatively concentrated, or both (Pfeffer and Salancik, 1978). As mentioned before, most researches borrow terminologies of power from marketing, economics and sociology. Power sources are viewed by personal attributes such as expert and referent power, and positional attributes such as reward, coercive and legitimate power (Munson et al., 1999). Thus, many researches (to name a few, Johnston et al., 2004; McCarter and Northcraft, 2007; Yeung et al., 2009) study the relationship of power, trust and SCM with the above power definition. From another viewpoint, power affects a firm's operations management, thus can be observed in pricing strategy, inventory control and JIT, operations control, channel or distribution structure management, and information management (Munson et al., 1999). A firm's power with respect to its customer or supplier stems from several sources, including the number of major customers of a supplier's component, a supplier's market share of a given component, the number of suppliers from which a buyer purchases a particular component, the number of potential suppliers for a given component, and the amount of revenue a supplier generates from a single buyer (Krajewski et al., 2005). In each case, the source of power resides in a firm possessing or controlling a scarce resource (Ireland and Webb, 2007).

Since there are various firm resources, it is not surprising to find a number of resource typologies proposed in the literature (Das and Teng, 2000). Overall, there are two viewpoints to classify the resources in a firm. One is from the view of resource characteristics, and the other is from the view of resource types. From the view of resource characteristics, Grant (1991) differentiates the resources by tangible and intangible resources. In Das and Teng (2000), there are three kinds of resource characteristics: imperfect mobility, imperfect imitability and imperfect substitutability. From the view of resource types, Miller and Shamsie (1996) suggest that all resources may be classified as two broad categories of property-based resources and knowledge-based resources. Barney (1991) categorizes the resources by physical, human and organizational capital resources. Das and Teng (1998) propose four specific kinds of resources that are financial, technological, physical and managerial resources. Hofer and Schendel (1978) classify a firm's resource profile as financial, technological, physical, managerial, human, and organizational resources. By the discussion with managers in sample companies in Yangtze River Delta, this research follows the categorization of resource types in Grant (1998) and the resources are viewed as financial resource (e.g., cash, return on investment), production and operations resource (e.g., facility, raw materials, purchasing channel), reputation resource (e.g., brand, good industrial relationship), human resource (e.g., employee skill level, work attitude), organizational resource (e.g., a firm's capability of planning and coordination), technological resource (e.g., patents, technology, R&D devices), innovative resource (e.g., the capability of being sensitive to the market change and new technology, and developing new products to new market demands). A firm's resources affect its power in operations management, which has been observed in many business practices. For instance, because of their market brands and dominance, Wal-Mart, Ford and General Motors had the pricing control and squeezed the margins from their small suppliers by setting low price (Mottner and Smith, 2009; www.theautochannel.com, 2003). A small company may possess unique knowledge and expertise, thus also can have control in operations management. The empirical study by Zhao et al. (2008) confirms human resource such as experts in a firm brings power over its supply chain partners. Operations control includes quality certification, packaging requirements and so on. For instance, because of their POS machine, the retailers require suppliers provide electronic code on package to allow for scanning at the checkout line. Some retailers in China even misuse their bargaining power and charge suppliers for the coding (www. cq.xinhuanet.com, 2011). For the channel or distribution structure management, franchisor or manufacturer distribution network always faces the challenge of allocating sales areas to the distributors. Sometimes, depending on their power, they also try to control the channel by pressuring the retailers not to sell the competitors' products (Munson et al., 1999). For the inter-organizational relationship, Guanxi is emphasized in Chinese business and investigated by many research (for instance, Yeung and Tung, 1996; Tsang, 1998; Park and Luo, 2001; Zhao et al., 2008). Guanxi, which stems from the strong emphasis on personal relationships, has been suggested as relationships or social connections based on mutual interests and benefits (Yang, 1994). Zhao et al. (2008) think that China's culture such as high power distance national culture and cultural collectivism lays the foundation of power and dependency in inter-organizational relationship. Zhuang and Zhou (2004) found that power and dependency can be transferred through the extended Guanxi network in China. Therefore, it can be expected that power and dependency relationship could be observed obviously in Chinese business. As pointed out by Crook and Combs (2007), it is an important first step to understand how resources shape dependencies and

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hence bargaining power. Thus, it raises the questions of whether resource type has any connection with powers in operations management, which is not investigated clearly. Therefore, the following hypothesis is made to address the issue. Hypothesis 1 tries to make a connection between resources and power in operations management. H1. Different power source(s) from finance, production and operations, reputation, human, organization, technology or innovation resources will create different power of pricing control, inventory and JIT control, operations control, channel or distribution structure control or information control. Arkader (2001) found that customers' perspectives are different from suppliers' in regard to both the facilitators and the barriers of buyer-supplier relationships. Kim et al. (2010) reported that the switching costs and inter-organizational trust are significant determinants of cooperation for buyers, and technological uncertainty and the reciprocity of the relationship are significant determinants for the suppliers. Investigating buyer–supplier relationship in Chinese manufacturing, Song et al. (2012) found that the value of the relationship is different from the both parties' perspectives due to various reasons including the dependence on external resources. Thus, probably the same resource at the customer or supplier side impacts the power differently. Therefore, the following hypothesis:

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Resources

Power Categories:

Categories:

Power Position Categories:

Finance

Pricing control

Dominant

Production and operations

Inventory and JIT control

Dominated

Reputation

Operations control

Interdependence

Human

Channel or distribution

Independence

Organization

structure control

Technology

Information control

Innovation

Fig. 1. Research framework.

following Hypothesis 3 is expected to investigate the connection between power position and power in operations management. H3. A firm's power is dominant, interdependent, independent or dominated relative to its supplier or customer is closely related with its control in pricing, inventory and JIT, operations, channel or distribution structure and information. The framework this research intends to establish is presented in Fig. 1.

3. Research methodology 3.1. Instrument design

H2. From the perspectives of a buyer, same power sources from finance, production and operations, reputation, human, organization, technology or innovation resources will generate different power from the perspectives of a supplier. Hunt and Nevin (1974) investigate power distribution in the franchisor–franchisee channel. The authors measure the coercive and non-coercive power with specific variables such as who determines operating hours and how a franchisor assists in site location and so on. Without viewing power as the coercive and non-coercive, Cox and Chicksand (2005) study UK fresh/frozen beef supply chain, and classify the buyer supplier power as buyer dominance, supplier dominance, interdependence and independence by the criteria of buyer and supplier's search cost, switch cost, information asymmetry, market share, channel structure and so on. Cox and Chicksand (2005)'s work provides some clues of studying a firm's power position related with its power in operations management. For instance, in their analysis for the UK fresh/frozen beef supply chain, if the supplier has substantial information asymmetry advantage over buyer, where information control is observed, the supplier has dominance position in the chain. If the market has many buyers and few suppliers, and the supplier's offering is relatively unique, the supplier may also have the dominance position. In the case, channel or distribution control is observed. Crook and Combs (2007) use strong and weak members to describe the party at different power position in the supply chain. Liu et al. (2010) describe the customer–supplier relationship as “buddy”, “relier”, “arm's-length” and “initiative” by the criteria of trust and commitment. Cox and Chicksand (2005)'s classification criteria incorporates more factors in operations management such as information asymmetry and channel structure. The buyer– supplier relationship of Chinese manufacturing is maintained by Guanxi practice (Yeung and Tung, 1996; Chen et al., 2011), and different power such as reference power, expert power, reward power, legitimate power and coercive power are exploited to balance the relationship (Zhao et al., 2008). This research categorizes the power position between as dominance, being dominated, interdependence and independence. Therefore, the

The questionnaire is designed by a two-stage process which is commonly used. At the first stage, an extensive literature review is conducted to generate the relevant statement items. At the second stage, the research team had several face-to-face meetings with the top and middle level managers of some sample companies located in Yangtze River Delta. The main purpose is to ensure that the questionnaire is easy to understand and relevant to practices in China. Advice was obtained to refine the research questions and the design of the questionnaire. Pilot testing was conducted in a larger group of industry experts. This process helped to generate the questionnaire that could produce reliable and unbiased data. The measures for power in operations management (pricing control, inventory or JIT control, operations control, channel or distribution structure control, or information control) were adapted from Munson et al. (1999). Every item has two dimensional measures of “strategic importance to your firm” and “power relative to your firm”. Respondents were asked to indicate their agreement with statements concerning the power sources and relationship with their primary customer and supplier, respectively, using a 7-point Likert scale. The resource category and characteristics was extracted mainly from Grant (1998), Barney (1991), Peteraf (1993) and Collis and Montgomery (2008). Respondents were requested to make multiple choices accordance with their actual situation. The choices were measured by dichotomous scale, namely whether or not the respondents consider a resource is valuable. The main part of the questionnaire is in the Appendix A. The questionnaire was written in English, and then translated into Chinese and then back-translated into English, according to the steps suggested by Brislin (1970) and Sekaran (1983). The back-translated English version was checked against the original English version for accuracy. 3.2. Sampling and data collection In collecting data, we first tried to find out the top manufacturers and sample companies in different industrial sectors in

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Yangtze River Delta by surveying the yellow pages, governmental and industrial association websites of major cities in the area and other sources. After the information collection, we contacted the companies, explained our motivations and expectation and saw if they would like to cooperate in the project. If they agreed to participate, then we sent the questionnaires to them by mail or email. Out of the 550 companies contacted, a total of 280 agreed to receive the questionnaire. After several follow-up calls, 254 questionnaires were received. 40 cases were dropped because of missing data or incompleteness. The overall response rate, based on the number of companies contacted via telephone, was 38.9%. We evaluated non-response bias by comparing the early and late replies to all variables using a t-test. No significant differences were found, which suggests non-response bias is not considered an issue in this study. The sample companies and respondents represent a wide variety in terms of industry sector, size, and respondents' job title, as described in Table 1. Manufacturing industries such as Electronic and communication, Mechanical manufacturing, Electric engineering took up 80.5% in the whole sampled companies. From firm size which was measured by number of employees, 77.62% of companies were with less than 1000, and 84.59% of companies had revenue less than 100 million. For 90% of companies in Yangtze River Delta were SMEs, the distribution really made sense (China Statistical Yearbook 2009). For the respondents' job title, 85% were production managers, marketing managers, inventory manager and general managers, offering the confidence in their ability to provide valid responses to the survey questions. Besides, they were all encouraged to refer to other colleagues for accurate information. It is possible that almost all the questions in the questionnaire were answered by one respondent, thus there is the possibility of Common Method Variance (CMV) (Podsakoff et al., 2003). In order to assess it, Harman's one-factor test via EFA was performed. In the test, all of the items in a study are subject to EFA. As a result, eight factors rather than one single factor emerged from unrotated factor solutions, and the first factor, accounting for 21.29%, did not explain the majority of the variance in the variables. This result revealed that CMV is not a major problem in this study.

4. Analysis and results There are three analytical techniques in our study: (1) exploratory factor analysis (EFA), reliability analysis and confirmatory factor analysis (CFA) were adopted to test the validation of power construct; (2) multiple regression was used to examine the relationship between firm resource and power in supply chain; and (3) multinomial logistic regression for the analysis between resource and power position. 4.1. Measurement development Firstly, we use a rigorous process to develop and validate the instrument. After data collection, we performed a series of analysis to test the reliability and validity of the constructs. For the content validation, the literature review and in-depth interviews with business executives established the basis of content validity for the survey instrument. For the construct validity, there are two steps. First, EFA was adopted to ensure unidimensionality of the scales for the power, then Cronbach's alpha was used for assessing reliability. We used principal components analysis for data reduction and Varimax rotation with Kaiser Normalizations for clarifying the factors (Loehlin, 1998). Some measurement items were dropped because they were split with comparative loading on two or three factors, making them difficult to sort. Cronbach's alpha was then computed for each construct, to test internal consistency. The above steps were conducted iteratively. Next was to investigate the dimensionality of the power construct. The scales were tested for normality and outliers by the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and the Bartlett test of sphericity. The result, which showed KMO value of 0.799 with the significance of Bartlett's test at 0.001 level, indicates the data fitting for EFA. EFA was conducted without specifying the number of factors. The Eigenvalues for the fourfactors were above 1.0, but the four-factor results were somewhat confusing because the items of inventory control and pricing control were converged, making them difficult to explain conceptually. Thus, the four-factor solution was discarded. The five-factor solution was retained, and the results were consistent with the

Table 1 Profile of respondents and companies (Total¼ 214). (a) Job title General manager Production manager Sales/marketing manager Admin. manager Financial manager Sales executive Not reported

12 82 72 16 7 15 10

5.61% 38.32% 33.64% 7.48% 3.27% 7.00% 4.68%

(b) Firm size (number of employees) 45000 3000–5000 1000–3000 500–1000 200–500 o 200

14(6.5%) 12(5.6%) 22(10.28%) 57(26.63%) 63(29.44%) 46(21.55%)

(c) Revenue (in million) 43000 1000–3000 300–1000 100–300 10–100 o10

19(8.87%) 14(6.54%) 39(18.22%) 62(28.97%) 58(27.1%) 22(10.3%)

(d) Industry Electron & communication Printing & paper Mechanical manufacturing Electric engineering Rubber & plastics Foods Textile Chemistry

47(21.96%) 22(10.28%) 19(8.87%) 15(7.01%) 14(6.54%) 13(6.07%) 13(6.07%) 12(5.61%)

Sports apparatus Metallurgy Retail Building materials Logistics Medical appliance manufacturing Others Not reported

11(5.14%) 10(4.67%) 10(4.67%) 7(3.27%) 6(2.80%) 6(2.80%) 6(2.80%) 3(1.40%)

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five dimensions of power identified in the literature. The results are in Table 2. The Eigenvalues for the first four factors are all above 1.0, and the Eigenvalue for the fifth factor is slightly lower than 1.0, thus five factors can be extracted to represent the power construct, which is supported by a scree plot. The total variance explanation reaches 72.10%. All these items have strong loadings on the construct that they are supposed to measure and lower loadings on other constructs, indicating unidimensionality. Cronbach Alpha was applied to test reliability with the following results: pricing control (0.719), inventory and JIT control (0.845), operations control (0.852), channel control (0.728), and information control (0.712). The reliability coefficients for all the power factors are all well above the cutoff value of 0.7 (Cronbach, 1951; Nunnally, 1978). Thus, these results established the reliability of all the measures. CFA was used to further justify the factor structure (O'Leary-Kelly and Vokurka, 1998). The model fit indices are χ2(214) ¼ 145.5, RMSEA ¼0.060, NNFI¼0.902 and CFI¼0.953, indicating that the model is acceptable (Hu et al., 1992). Given the results of the above assessment, the validity of the power constructs is established.

4.2. The scoring of power We adopted the way of scoring each power in Munson et al. (1999). Multiplying the importance (I) and power (P) scores together for each item results in numbers between 1 and 49. For each of the five areas, adding over all of the items and dividing this sum by the number of items in that area (excluding items with “zero” importance). Taking the square root of this value which was a number between 1 and 7, the score of each power was obtained. The total power can be defined by adding over the “averages” of all five areas, dividing by 5 and also taking the square root of the result. The closer the value is to 7 (or 1) means that the more (or less) this company has power. Thus, we provide the descriptive statistics of the five kinds of control and the total power, and the results are presented in Table 3. According to standard deviation, skewness and kurtosis, they present nice performance.

4.3. Results of power analysis In this section, we use ANOVA to test the hypotheses presented in Section 2. Table 2 Factor analysis of power. Factor loadings Operations control O3.2 0.808 O3.3 0.788 O3.4 0.784 O3.1 0.753 C4.1 −0.019 C4.2 0.156 C4.3 0.091 C4.5 −0.221 S2.3 0.306 S2.4 0.327 S2.1 0.329 P1.3 0.180 P1.1 0.265 I5.2 0.029 I5.3 0.202 Eigenvalue 4.706 Total variance explained

Channel control

Inventory control

Pricing control

Information control

−0.025 −0.015 0.043 0.073 0.817 0.793 0.706 0.658 0.024 0.023 0.173 −0.062 0.061 0.014 −0.384 2.639

0.135 0.167 0.235 0.275 0.138 0.056 −0.241 0.334 0.840 0.812 0.616 0.160 0.286 0.002 −0.111 1.428 72.102%

0.214 0.136 0.070 0.161 0.011 −0.026 0.061 −0.054 0.173 0.185 0.323 0.871 0.762 −0.037 0.029 1.140

0.057 −0.047 0.107 0.121 −0.073 −0.200 −0.111 0.216 −0.067 −0.070 0.025 −0.041 0.022 0.912 0.759 0.902

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Table 3 The descriptive statistics of power.

N Range Minimum Maximum Mean Std. error Std. deviation Variance Skewness Std. error of skewness Kurtosis Std. error of kurtosis

Pricing control

Inventory control

Operations control

Channel control

Information control

Total control

214 6 1 7 4.39 0.07 1.09

214 6 1 7 4.27 0.08 1.21

214 5.68 1.32 7 4.47 0.08 1.17

214 5.26 1 6.26 3.68 0.09 1.30

214 3.79 1.87 5.66 3.83 0.04 0.53

214 4.33 1.69 6.02 4.22 0.05 0.76

1.19 −0.19 0.17

1.45 0.17 0.17

1.36 0.41 0.17

1.68 −0.38 0.17

0.29 −0.02 0.17

0.57 0.15 0.17

0.08 0.33

−0.20 0.33

−0.61 0.33

−0.55 0.33

1.68 0.33

−0.38 0.33

4.3.1. Resource categories At first, for all samples, based on whether or not the respondent selected each type of resource category, we divided the total sample into two clusters. Next we did ANOVA with the test of homogeneity. As there were seven resource categories, these steps were performed iteratively. As shown in Table 4a, innovation is the only resource which has significant influence on total control (p ¼0.016) while finance is not significant on total control but significant on both channel and information control (p ¼0.020 and 0.004, respectively). Therefore, the following results can be concluded. Proposition 1. The more a firm's resources are characterized by finance, the more likely the firm will have power in channel control and information control. Proposition 2. The more a firm's resources are characterized by innovation, the more likely the firm will have power in pricing control, inventory control, operations control and the total control. Besides Propositions 1 and 2, production resources are marginally critical to inventory control (p ¼0.056) probably due to the fact that production resources directly affect inventory control. Human resources are marginally critical to channel control (p ¼0.066), and organization resources are potentially critical to operations control (p ¼0.096). Technology resources mean a firm's patent, R&D or advanced technology, thus it is understandable that technology resources are similar with innovation resources and potentially critical to price control (p ¼0.097). As to the supplier's power sources in the views of the buyer, the results are shown in Table 4b. It is noted that no resource is significant to total control. Human resource is significant to channel control (p ¼0.037) and innovation resource is significant to both pricing control and inventory control (p ¼0.006 and 0.003, respectively). Therefore, the following conclusions can be drawn. Proposition 3. The more a supplier's resources are characterized by human, the more likely it will have power in channel control. Proposition 4. The more a supplier's resources are characterized by innovation, the more likely it will have power in pricing control and inventory control. Moreover, from the supplier's perspective, inventory control is marginally influenced by production resource (p ¼0.092) which is similar with the result in Table 4a, and influence by reputation resource (p ¼0.086) which implies that the reputation has marginally significant impact on product sales.

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Table 4a ANOVA of resource categories and power. Resource type

Total control

Pricing control

Inventory control

Operations control

Channel control

Information control

Finance Production Reputation Human Organization Technology Innovation

0.106 0.593 0.937 0.437 0.642 0.185 0.016**

0.871 0.320 0.714 0.268 0.672 0.097* 0.008***

0.796 0.056* 0.233 0.449 0.934 0.145 0.015**

0.875 0.377 0.894 0.237 0.096* 0.146 0.043**

0.020** 0.427 0.880 0.066* 0.965 0.454 0.109

0.004*** 0.715 0.187 0.923 0.886 0.491 0.988

* **

p o 0.1, po 0.05, p o 0.01.

***

Table 4b ANOVA of resource categories and power-buyer's perspective. Resource type

Total control

Pricing control

Inventory control

Operations control

Channel control

Information control

Finance Production Reputation Human Organization Technology Innovation

0.398 0.412 0.855 0.616 0.868 0.313 0.116

0.256 0.281 0.377 0.878 0.334 0.236 0.006***

0.994 0.092* 0.086* 0.479 0.774 0.523 0.003***

0.654 0.299 0.952 0.254 0.904 0.192 0.332

0.121 0.918 0.823 0.037** 0.830 0.467 0.237

0.245 0.598 0.429 0.606 0.875 0.575 0.197

* **

p o 0.1, po 0.05, p o 0.01.

***

Table 4c ANOVA of resource categories and power-supplier's perspective. Resource type

Total control

Pricing control

Inventory control

Operations control

Channel control

Information control

Finance Production Reputation Human Organization Technology Innovation

0.458 0.471 0.907 0.691 0.679 0.108 0.066*

0.947 0.785 0.169 0.371 0.762 0.463 0.253

0.590 0.604 0.823 0.823 0.776 0.188 0.722

0.688 0.667 0.842 0.583 0.020** 0.352 0.061*

0.783 0.495 0.549 0.653 0.841 0.068* 0.222

0.020** 0.695 0.390 0.731 0.740 0.837 0.051*

* **

p o 0.1, po 0.05.

Similarly, as to the buyer' power sources in the views of the supplier, the results are shown in Table 4c. It is observed that finance and organization resources have significant influence on information control (p ¼0.020) and operations control (p ¼0.020), respectively. Therefore, the following results can be obtained.

Proposition 5. The more a buyer's resources are characterized by finance, the more likely it will have power in information control. Proposition 6. The more a buyer's resources are characterized by organization, the more likely it will mainly have power in operations control. Similarly, from the buyer's perspective, technology resource has a marginal significant influence on channel control (p ¼0.068). Also, innovation resource is marginally significant to total control (p ¼0.066), operations control (p ¼0.061) and information control (p ¼0.051). It is very interesting to see in Table 4c that from the buyer's perspective, innovation resource is still the most critical one. It also reinforces Proposition 2. In summary, H1 and H2 are regarded as being partially supported.

4.3.2. Power position This section is to test whether the controls in pricing, inventory and JIT, operations, channel and information have influence on the firm's power position of being dominant, interdependent, independent or dominated with respect to its supplier or customer. Here, we adopted multi-logistic regression. Surprisingly, as presented in Table 5, only the control in inventory has significant impact on the firm's position. Further exploration about the influence of the inventory control is shown in Table 6. In more detailed fashion, we can say that holding all the other variables constant, the effect of inventory control is 1.6865 on the relative risk of choosing 3 (interdependence) over 1 (buyer-dominant). It means that the percent increase of relative risk of choosing 3 over 1 from inventory control to other controls is about 68%. Similarly, the effect of inventory control is 1.523 on the relative risk of choose 3 over 2 (supplier-dominant), namely that the percent increase of relative risk of choosing 3 over 2 from inventory control to other controls is about 52.3%. In conclusion, the inventory control would be apt to choose interdependence relationship rather than buyer-dominant or supplier-dominant at least at 52% risk. Thus, Hypothesis 3 is supported and following proposition is obtained.

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Table 5 Multi-logistic regression of dominant position.

1

2

4

Var1

Coef.

Std. err.

Z

P 4|z|

[95% conf. interval]

Information Channel Operation Inventory Pricing _cons Information Channel Operation Inventory Pricing _cons Information Channel Operation Inventory Pricing _cons

−0.03039 0.185545 0.237483 −0.52263 0.18913 −0.08526 0.011695 −0.1287 0.337853 −0.4207 0.192371 −1.10723 0.183365 0.100104 0.209041 −0.26751 0.223014 −3.17588

0.133582 0.13831 0.221843 0.159136 0.201447 0.834874 0.174154 0.174593 0.268871 0.20115 0.249633 1.102236 0.168542 0.179254 0.263782 0.188921 0.245957 1.186909

−0.23 1.34 1.07 −3.28 0.94 −0.1 0.07 −0.74 1.26 −2.09 0.77 −1 1.09 0.56 0.79 −1.42 0.91 −2.68

0.82 0.18 0.284 0.001** 0.348 0.919 0.946 0.461 0.209 0.036* 0.441 0.315 0.277 0.577 0.428 0.157 0.365 0.007

−0.29221 −0.08554 −0.19732 −0.83453 −0.2057 −1.72158 −0.32964 −0.4709 −0.18912 −0.81494 −0.2969 −3.26757 −0.14697 −0.25123 −0.30796 −0.63779 −0.25905 −5.50218

0.2314232 0.4566277 0.6722865 −0.21073 0.5839598 1.551062 0.3530309 0.2134938 0.8648299 −0.026449 0.681642 1.053114 0.513701 0.451436 0.7260432 0.1027702 0.7050808 −0.849581

(var1 ¼ 3 is the base outcome). 1¼ Buyer-dominant. 2¼ Supplier-dominant. 3¼ Interdependence. 4¼ Independence. * **

p o 0.05, po 0.01.

Table 6 Multi-logistic regression—inventory.

5. Discussions 5.1. Theoretical implications and validation

Variable: inventory (sd¼ 1.5285135) Odds comparing Alternative 1 to alternative 2 −2 −4 −3 −1 −4 −3 −1 −2 −3 −1 −2 −4

1 1 1 2 2 2 4 4 4 3 3 3

b

z

P4 |z|

ê b

−0.10194 −0.25512 −0.52263 0.10194 −0.15319 −0.4207 0.25512 0.15319 −0.26751 0.52263 0.4207 0.26751

−0.487 −1.28 −3.284 0.487 −0.657 −2.091 1.28 0.657 −1.416 3.284 2.091 1.416

0.626 0.201 0.001** 0.626 0.511 0.036* 0.201 0.511 0.157 0.001** 0.036* 0.157

0.9031 0.7748 0.593 1.1073 0.858 0.6566 1.2906 1.1655 0.7653 1.6865 1.523 1.3067

êbStdX 0.8557 0.6771 0.4498 1.1686 0.7912 0.5257 1.4769 1.2638 0.6644 2.223 1.9023 1.5051

1¼ Buyer-dominant. 2¼ Supplier-dominant. 3¼ Interdependence. 4¼ Independence. b¼ Raw coefficient. z ¼z-score for test of b¼ 0. P4 |z|¼ p-value for z-test. ê b ¼exp(b) ¼Factor change in odds for unit increase in X. ê bStdX ¼ Exp(b  SD of X) ¼Change in odds for sd increase in X. * **

p o 0.05, po 0.01.

Proposition 7. A firm's power is more likely interdependent than buyer-dominant, supplier-dominant or independent with respect to its supplier or customer is closely related with its control in inventory and JIT. In other words, if one side adopts inventory and JIT control, it is more likely that the customer–supplier relationship will be interdependence.

The only firm resource significantly contributing to total control in the customer–supplier relationship is innovation, which is presented in Proposition 2. Innovation most probably helps the firm create its power in pricing control, inventory and JIT control, operations control and the other controls. For instance, with an evidence of the innovative products of iPhone and iPad, Apple dominates its supply chain: not only controlling the retailing market, but also trying to control the component supply (Forbes. com, 2012). The company even has the power to audit its worldwide suppliers (Reuters.com, 2012). Innovation implies uniqueness in the market, which is probably the reason why the companies with innovative resources can have such powers. Innovation is more important to the supplier rather than to the buyer. It is because that innovation helps the supplier create its power in pricing and inventory control, which is presented in Proposition 4. However, according to our data analysis innovation does not help the buyer to create its power in operations management significantly. Since supplier is at the upper-side of a supply chain, innovative resources will help supplier to have more control on its buyers, which means the supplier has power in more supply chains than the buyer has. Thus, from this view, innovation is more important to the supplier. Moreover, selling price and quantity are probably the two most critical concerns for the supplier. Therefore, pricing control and inventory control are the two main powers the supplier is eager to occupy. Apple's case is a good example in innovation for Chinese manufacturing. Most Chinese manufacturers suffer from lack of innovation competitiveness, thus only compete on cost. Because of the lack of reputed brands and innovation in design and manufacturing, necktie manufacturers in Zhejiang province of Yangtze River Delta failed to raise even $0.1 per necktie for the oversea buyers in 2008 (Finance.sina.com.cn, 2008). On the other hand, Alibaba Group, founded in 1999 with leading internet technology

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and innovative business model, has successfully maintained its leading position in Chinese e-commerce market. In the business models of Taobao.com, TMALL.com and Juhuasuan.com which are in Alibaba business group, the company has the power of setting membership fee, monitoring the buyers and sells, and even canceling one buyer or seller's membership (www.tmall.com; www.juhuasuan.com). The company also has the rights of sharing profit with the seller in TMALL.com (www.tmall.com). Finance is another critical resource. Generally speaking, it helps the firm to create its power in channel and information control significantly, which is observed in Propositions 1 and 5. Finance resource is more important to the buyer rather than to the supplier. It matches the observations in business, even for big companies. As reported in Money.cnn.com (2011), “Apple aggressively uses its size and vast array of resources—including its very deep pockets—to get the deals it wants with component makers. The company sent executives to its Japanese suppliers literally with cash in hand to make sure supply remained adequate…”. In Yangtze River Delta, observed in the fiber and fabric companies by Feng et al. (2007), depending on their strong finance and production capability, some fiber manufacturers request their customers transport the ordered products at the same day as the order purchasing. They also affect the channel price by adjusting production capacity and inventory, and control the information. The firms with financial resource have the capability to keep the channel running smoothly to acquire profits. Information is critical for firms to accurately manage or adjust its sales areas or inventory distribution. Therefore, with financial resource the firms tend to gain powers in channel and information controls. Organization resource helps the buyer create its power in operations control over its supplier, which is presented in Proposition 6. A similar result is not true for the supplier. This is probably because if the buyer has good organization capability, most probably it will be the benchmark for the supplier, or it has more or less power to affect or control the supplier's operations. This is true in China where production is based on orders, especially the orders from oversea. When Chinese manufacturers receive orders, most of them need to investigate suppliers' capability and coordinate with suppliers for the part/component design, production process design, inventory control, delivery time, and cost (Feng and Liu, 2011). Therefore, supplier's operation will more or less be affected or decided by the buyer. However, human resource is helpful for the supplier to create its power in channel control, which is stated in Proposition 3. A similar result does not hold for the buyer. This implies that at the interfaces of supplier–buyer, qualified employees will help the supplier to create power in channel control over the buyer. The reason behind it is probably because in Chinese business, sales often have more pressure than purchasing. Therefore, a qualified employee will be easier to communicate with the buyer and thus integrate the buyer together in the channel. Therefore, in Chinese manufacturing, in order to be competitive in the supply chain, suppliers should acquire technical know-how while manufacturers should be a good organizer of the chain. This is consistent with the finding in Zhao et al. (2008), the authors found it is easier for a company with expertise to improve relationship commitment in Chinese manufacturing. It is also not surprising that inventory control and JIT is significantly related with the interdependence customer–supplier relationship, as presented in Proposition 7. JIT philosophy does need close collaboration with the firm's suppliers and customers. As pointed out in Panizzolo (1998), practices of building close and long term partnership with supplier are not only at the logistics level, but also at the technological and strategic level. Lean production also means the customer involvement in product design, frequent and rapid order delivery, etc. With the interdependent relationship and collaboration, the lean system which is

fundamentally fragile can work smoothly (Albino and Garavelli, 1995). As proposed in Feng and Liu (2011), lean manufacturing is becoming the basic requirement in make-to-order environment in Yangtze River Delta. Theoretically, based on resource dependency theory, our model demonstrates the power advantages the company can develop in the buyer–supplier relationship. Other than the existing literature, this study looks into the power from the operations perspective, and proves that power exists in a company's pricing strategy, inventory control and JIT, operations control, channel or distribution management, and information management with its different resource advantages in the buyer–supplier relationship.

5.2. Managerial insights Our findings provide guidelines for managers in developing power in buyer–supplier relationships. In summary, innovation capability is most critical for any company since it can create power in total control. When a company needs to control information in the channel, it is better for the company to select a supplier whose finance situation is not so strong as it. Moreover, when a company needs highly cooperation in operations from its supplier, it needs to have a stronger organization capability than its supplier. From a supplier's perspective, hiring professional employees is helpful to create its channel control. These guidelines are important for investors especially foreign investors who often experience highly transactional behavior in China (Styles et al., 2000). It is because it becomes very easy for both sides to understand each other's resource competitiveness and thus the operational power advantages. Therefore, this study provides a method for both buyers and suppliers to have a better mutual understanding and further see the cooperation possibility.

6. Conclusions and future research Based on the data collected in Yangtze River Delta, this paper studied power from the perspective of operations management, and tried to understand the power causes with the theory of resource dependency. It is found that innovative, financial, human and organizational resources are significant important for the companies in the area to create whole control, channel and information control, channel control, and operations control, respectively. To be competitive in the supply chain, it is better for a supplier to have technical know-how and a manufacturer should be a good chain organizer. Moreover, inventory control and JIT is significantly related with the interdependence customer–supplier relationship. Clearly, the theoretical framework proposed in this paper is not completed. Future research will be focused on the following critical questions: how the power affects the profit allocation generated by supply chain integration? and how the power or profit allocation affects customer's or supplier's motivation to participate in supply chain integration and collaboration? After obtaining answers to the two preceding research questions, we can have a deeper understanding about supply chain management.

Acknowledgments The authors thank two anonymous referees for their constructive comments. The second author's research is supported by Social Science Funding of China Education Ministry (No. 10XJC63005) and National Nature Science Foundation of China (No. 70902019).

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Appendix A Power construct measurement (selected from Munson et al., 1999) Rate each of the attributes according to their degree of importance (from very low to very high) and degree of power (from very low to very high) with respect to your firm. Use 7 point Likert scale, 1 means highest and 7 is lowest. 1 Pricing control. 1.1 Demands lower prices. 1.2 Demands quality discounts. 1.3 Influences retail prices. 2 Inventory control. 2.1 Demands just-in-time production process. 2.2 Demands return of goods that are not in use or not sold out. 3 Operations control. 3.1 Demands special service during pre-sale, sales and postsale stages. 3.2 Demands easy return policies. 3.3 Demands unique forms of customization. 4 Channel control. 4.1 Uses or threatens allocation of sales areas. 4.2 Uses or threatens channels of distribution. 4.3 Uses or threatens vertical integration. 5 Information control. 5.1 Demands electronic data exchange. 5.2 Demands the investment in information system.

Resource category measurement (selected from Hitt et al., 1995; Grant, 1998) Choose the resource category that is the most valuable with respect to your firm. More than one can be chosen. 1 Financial resource (e.g. cash, return on investment). 2 Production and operations resource (e.g. facility, raw materials, purchasing channel). 3 Reputation resource (e.g. brand, good industrial relationship). 4 Human resource (e.g. employee skill level, work attitude). 5 Organizational resource (e.g. a firm's capability of planning and coordination). 6 Technological resource (e.g. patents, technology, R&D devices). 7 Innovative resource (e.g. the capability of being sensitive to the market change and new technology, and developing new products to new market demands). 8 Others.

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