Article 1, Article 2, Article 3

Joint supply chain risk management: An agency and collaboration perspective

Gang Li a, Huan Fan a,b, Peter K.C. Lee b,n, T.C.E. Cheng b

a School of Management, The State Key Lab for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28, Xianning West Road, Xi’an 710049, PR China b Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

a r t i c l e i n f o

Article history: Received 5 November 2014 Accepted 15 February 2015 Available online 4 March 2015

Keywords: Supply chain risk management Inter-firm practices Agency theory perspective Collaborative relationships

a b s t r a c t

As supply chain risks refer to the risks transmitted among supply chain members and supply chain management (SCM) is concerned with close collaboration among chain members to enhance the chain's overall performance, we argue that we need to use an SCM perspective in supply chain risk management (SCRM). We identify risk information sharing and risk sharing mechanism as two important joint SCRM practices. Drawing on the literature on agency theory and collaborative relationships, we argue that the effectiveness of these two joint practices in improving financial performance can be strengthened by collaborative relationship characteristics including relationship length, supplier trust, and shared SCRM understanding. We empirically test our conceptual model using the data collected from 350 manufac- turing firms in China. The results suggest that both risk information sharing and risk sharing mechanism improve financial performance, and the effectiveness of the former is strengthened by relationship length and supplier trust, while that of the latter is strengthened by shared SCRM understanding. We contribute to research and practice by identifying two useful joint SCRM practices and ascertaining the conditions under which each of the practices is particularly effectively.

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1. Introduction

The threat of supply chain risks (SCRs) (Juttner et al., 2003; Juttner, 2005; Khan and Burnes, 2007; Kleindorfer and Saad, 2005) and their actual negative impacts on the operational continuity and corporate performance of supply chains and individual firms have attracted significant attention from practitioners and researchers of supply chain management (SCM) (Cousins et al., 2004; Hendricks and Singhal, 2003, 2005a, 2005b; Kleindorfer et al., 2003; Matsuo, 2015). For instance, Hendricks and Singhal (2003, 2005a, 2005b) demon- strate that supply chain disruptions decrease shareholders' value by almost 11% and firms, on average, experience a 40% decline in stock price after a disruption. Recently, a study by Aon Risk Solutions reports that, on average, the percentage of global companies reporting a loss of income due to a supply chain risk increased from 28% in 2011 to 42% in 2013 (Saenz and Revilla, 2014). Indeed, many SCM practitioners consider managing SCRs an indispensable part of their jobs (Zsidisin and Ellram, 2003; Blome and Schoenherr, 2011) and researchers assert that SCRs should be regarded as a corporate-level concern with strategic importance (Narasimhan and Talluri, 2009). To mitigate the

negative impacts of SCRs, researchers have proposed various strategies such as the real option (Cucchiella and Gastaldi, 2006), flexibility (Tang and Tomlin, 2008), and buffer strategies (Chopra and Sodhi, 2004). More specifically, firms can build extra inventory in order to cope with the risk of mismatch between demand and supply. If the extra inventory cannot satisfy the demand, firms can postpone or backlog the orders. Also, firms can improve the flexibility of their facilities or broaden the product line to meet diversified demands. However, rooted in the literature on enterprise risk management, these strate- gies employ a single-firm perspective that they are internal practices with minimal insights on the collaboration between the focal firm and its supply chain partners. Yet SCRs are much more than mere internal risks. SCRs are different from general enterprise risks in that SCRs specifically refer to risks that transmit among supply chain members; and their magnitude and probability can be significantly influenced by supply chain phenomena such as the rippling and network effects (Juttner, 2005). Without incorporating the concepts of SCM into SCR mitigation practices, the mitigation efforts are unlikely to be effective. One fundamental concept of SCM is that the firms of a supply chain have to work jointly to ensure their continuity and profitability (Tang, 2006a). Thus, to mitigate SCRs effectively, firms have to adopt not only internal mitigation practices, but also relevant inter-firm practices (Colicchia and Strozzi, 2012; Juttner, 2005; Kleindorfer and Saad, 2005). In this study we identify the relevant inter-firm mitigation practices and examine their performance consequences.

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Int. J. Production Economics

http://dx.doi.org/10.1016/j.ijpe.2015.02.021 0925-5273/& 2015 Elsevier B.V. All rights reserved.

n Corresponding author. Tel.: þ852 2766 7415. E-mail addresses: [email protected] (G. Li),

[email protected] (H. Fan), [email protected] (P.K.C. Lee), [email protected] (T.C.E. Cheng).

Int. J. Production Economics 164 (2015) 83–94

We identify two relevant joint supply chain risk management (SCRM) practices, namely risk information sharing and risk sharing mechanism. When implementing risk information sharing, a firm and its supply chain members exchange their SCR-related infor- mation in a timely and accurate manner. This joint practice is an essential element leading to supply chain visibility (Christopher and Lee, 2004). A lack of visibility in a supply chain causes consequences such as mistaken decision making, the bullwhip effect, excessive inventories, jeopardizing the profit margin of the whole supply chain (Prajogo and Olhager, 2012). Risk sharing mechanism pertains to the situation in which a firm aligns the incentives and obligations among supply chain members regard- ing how they share the duties to mitigate SCRs and face the consequences of SCRs in their supply chain (Juttner, 2005; Kleindorfer and Saad, 2005). Specifically, this joint practice involves using contracts with terms such as buy-back agreement or revenue sharing to coordinate the relevant SCRM activities (Faisal et al., 2006). The traditional enterprise risk management perspective focuses on the risk mitigation practices that firms can implement internally. As such, it offers limited insights on how firms can work jointly to tackle the risks that they have to confront together (e.g., SCRs). In this study we argue that firms should adopt the perspective of SCM and understand that they need to have “an openness to share risk-related information and the willingness to accept SCRs as joint risks” (Juttner, 2005). We argue that in addition to internal SCRM practices, firms should imple- ment two important joint SCRM practices, namely risk information sharing and risk sharing mechanism. Indeed, the former practice represents a “soft” element that helps achieve an effective SCRM information system, whereas the latter practice represents a “hard” and formal arrangement that aligns different parties' incentives and obligations, facilitating the coordination of differing SCRM activities.

Given that our two joint SCRM practices are concerned with the interface between a focal firm and its supply chain partners, agency problems such as opportunistic behaviors, and differences in goal and risk preference may exist among the firms involved in the collaboration (Eisenhardt, 1989). For instance, because of the threat of opportunism and the entailing confidentiality concern, a firm may not share all of its SCR information with its supply chain partners by withholding information about its internal disrup- tions. For example, Philips was reluctant to disclose timely and complete information to Ericsson after a fire occurred in its semiconductor plant in 2000, which resulted in a loss of 400 million Euros for Ericsson (Latour, 2001). Likewise, because of goal and risk preference differences, a firm and its supply chain partners could have a dispute on the specific methods in sharing the costs of SCR events. Thus, agency theory is an appropriate theoretical lens to examine the implementation of joint SCRM practices (Zsidisin and Ellram, 2003; Zsidisin et al., 2004; Zsidisin and Smith, 2005). Further, to temper the negative impacts from such agency problems, agency theory suggests that a collaborative relationship approach characterized by low levels of goal conflict and a long-term orientation is conducive to behaviour-based management efforts such as risk information sharing and risk sharing mechanism (Eisenhardt, 1989; Anderson and Oliver, 1987; Zsidisin and Ellram, 2003). The collaborative approach is widely used by firms to manage their supply chain relationships because it facilitates mutual decision making and the implementation of joint activities, thereby helping firms to achieve their common goals (Ramanathan and Gunasekaran, 2014; Zacharia et al., 2009; Jap, 1999). In addition, some researchers of SCRM assert that the collaborative approach can help firms implement SCRM activities more effectively (Lavastre et al., 2014). In this study we draw on the literature on collaborative relationships to identify three relevant relationship characteristics, namely relationship length,

supplier trust, and shared SCRM understanding, that can influence the mitigation effectiveness of risk information sharing and risk sharing mechanism.

The objectives of this study are to examine whether or not (1) risk information sharing and risk sharing mechanism are positively associated with financial performance, and (2) the associations between these two joint SCRM practices and financial performance are positively moderated by relationship length, supplier trust, and shared SCRM understanding. Based on data collected from 350 Chinese manufacturers, we empirically test our conceptual model using statistical techniques such as confirma- tory factor analysis and hierarchical regression analysis. This study contributes to the SCRM literature by filling in the knowledge void concerning the relevant joint SCRM practices. It also contributes to practice by offering clear managerial guidelines that firms should recognise the importance of joint SCRM practices such as risk information sharing and risk sharing mechanism, and that certain characteristics (i.e., relationship length, supplier trust, and shared SCRM understanding) of the relationships with their supply chain partners could influence the effectiveness of such joint practices.

2. Theoretical background and research hypotheses

2.1. Theoretical foundation

As firms within supply chains are getting more interdependent of one another (Hallikas et al., 2004; Thun and Hoenig, 2011; Li et al., 2010), SCRs' detrimental effects become more intense through SCM phenomena such as the rippling and network effects (Juttner, 2005). In addition to the perspective of enterprise risk management, firms need to adopt an SCM perspective to identify and implement relevant SCR mitigation practices for addressing their SCRs (Juttner, 2005; Kleindorfer and Saad, 2005; Tang, 2006a). Accordingly, Tang (2006a) defines SCRM as “the management of SCRs through coordination or collaboration among the supply chain partners so as to ensure profit- ability and continuity”. More specifically, Juttner (2005) maintains that the foundation of effective SCRM is “an openness to share risk-related information and the willingness to accept SCRs as joint risks”. Like- wise, Kleindorfer and Saad (2005) identify two key issues of SCRM, namely sharing information among supply chain members to increase the chain's visibility of vulnerabilities and establishing incentives across supply chain members to support relevant SCR decisions and practices. These works imply that, when taking an SCM perspective to view SCRM, there are two critical joint SCRM efforts, namely risk information sharing and risk sharing mechanism (Juttner, 2005; Kleindorfer and Saad, 2005). Risk information sharing is the essence of SCRM information systems that enhance supply chain visibility (Christopher and Lee, 2004), coordination (Sahin and Robinson, 2002), and responsiveness (Speier et al., 2011). Through stipulating partnering firms' obligations and responsibilities in managing SCRs, risk sharing mechanism aligns incentives among supply chain mem- bers (Narayanan and Raman, 2004) and facilitates the coordination of different members' mitigation efforts. As these two SCRM practices are concerned with the joint activities between a firm and its supply chain partners, their effectiveness could be influenced by the principal-agent problems of agency theory and the concepts of collaborative supply chain relationships. Thus, we next use agency theory and the collaborative relationship perspective to further examine risk informa- tion sharing and risk sharing mechanism.

Agency theory applies to problems that arise when one party, the principal, delegates work to another party, the agent (Eisenhardt, 1989). It has been one of the major theories adopted by researchers to examine SCR problems (Zsidisin and Ellram, 2003; Zsidisin et al., 2004; Zsidisin and Smith, 2005). Agency theory is relevant for our joint SCRM practices in that certain

G. Li et al. / Int. J. Production Economics 164 (2015) 83–9484

common principal-agent problems such as opportunism, differ- ences in goals and risks, and information asymmetry may jeopar- dize such practices' effectiveness in SCR mitigation. For instance, the threat of opportunism may cause firms to hold back certain internal information when sharing SCR information to supply chain partners. Differences in goals and risk attitudes make supply chain partners difficult to reach a consensus on methods to share the duties to mitigate SCRs and face the consequences of SCRs. Information asymmetry could aggravate the threat of opportunism among supply chain partners and the difficulty in evaluating risk events' consequences, thereby further impeding the effectiveness of risk information sharing and risk sharing mechanism in mitigating SCRs.

From the perspective of agency theory, Zsidisin and Ellram (2003) suggest that the management of supply risks can be buffer-based or behaviour-based. Buffer-based management efforts reflect the results of the mitigation practices, whereas behaviour-based management efforts focus on the processes involved and reflect the extent to which the firms emphasize “tasks and activities” that lead to risk mitigation (Eisenhardt, 1989; Zsidisin and Smith, 2005). As the two joint SCRM practices of this study are concerned with the processes of sharing SCR information, duties, and consequences, they belong to behaviour- based management efforts. In addition, the relevant literature suggests that when SCRs, which are important sources of uncertainty, become a critical concern, the firms within a supply chain need to adopt behaviour-based management efforts in order to reduce the prob- ability and/or impact of SCRs (Zsidisin and Ellram, 2003; Zsidisin and Smith, 2005). Agency theory argues that behaviour-based manage- ment efforts could be more effective when inter-firm relationship has a low level of goal conflict and a long-term orientation (Eisenhardt, 1989). Therefore, the literature on collaborative relationships offers insights on factors that can weaken the negative impacts from the relevant principal-agent problems.

The importance of collaborative relationships between firms has been well recognized in the SCM literature (Cao and Zhang, 2011; Mentzer et al., 2000; Sheu et al., 2006; Simatupang and Sridharan, 2002; Luo et al., 2015). SCRM researchers have recently started to pay attention to relational aspects such as guanxi (Cheng et al., 2012) and a collaborative mitigation approach (Chen et al., 2013). Cheng et al. (2012) argue that firms can form a guanxi network with its key suppliers to reduce SCRs. Chen et al. (2013) empirically examine the effectiveness of supply chain collabora- tion as a risk mitigation strategy. The case study of Zsidisin and Smith (2005) asserts that firms can manage supply risks through early supplier involvement. Therefore, we argue that collaborative relationships are very relevant for firms to achieve effective SCRM and that certain relevant collaborative relationship characteristics can have supplementary effects on our joint SCRM practices' effectiveness (Fig. 1).

2.2. Risk information sharing and risk sharing mechanism

In this study risk information sharing refers to situations in which supply chain members share critical and proprietary SCR information (Juttner, 2005; Ritchie and Brindley, 2007). Such information sharing activities can improve the coordination between the processes of different supply chain members, leading to improved supply chain integration, delivery accuracy, time-to- market (Jarrell, 1998), customer satisfaction (Spekman, 1988), and partnership quality (Lee and Kim, 1999). Indeed, information sharing is a key ingredient for any SCM systems (Moberg et al., 2002) and one of the most important enablers for SCR mitigation (Chopra and Sodhi, 2004; Faisal et al., 2006; Ritchie and Brindley, 2007; Saldanha et al., 2013; Spekman and Davis, 2004; Tse and Tan, 2012). Christopher and Lee (2004) suggest that one key element in any strategy designed to mitigate SCRs is improving

“end-to-end” visibility. Juttner et al. (2003) assert that firms' joint efforts in sharing risk-related information are one of the most important SCR mitigation strategies. Specifically, SCR information helps reduce SCRs in two ways. First, before SCRs become actual risk events, it helps firms identify possible vulnerabilities of the supply chain and develop the corresponding contingency plans, thereby making firms responsive when certain SCRs become reality. Second, when actual SCR events occur, it provides firms with timely and accurate information on the status of the events and the results of their mitigation efforts. By adapting their mitigation efforts according to such information, firms can reduce the events' impacts more effectively. Thus we propose the follow- ing hypothesis:

H1. Risk information sharing is positively related to financial performance.

Risk sharing mechanism refers to situations in which supply chain members use more formal policies and arrangements (e.g., agreements, contracts etc.) to share the obligations and responsi- bilities in activities and/or resources relating to SCRM. Supply chain phenomena such as the rippling and network effects suggest that SCRs would not affect a single firm, but all the firms within a supply chain. Yet the blurring boundaries between companies in many integrated supply chains of today may confuse the lines of responsibility and the resultant lack of ownership is recognized as one of the network-related risks (Juttner et al., 2003). To be more effective in reducing SCRs, collaboratively sharing the risk man- agement obligations and responsibilities between supply chain members is required. Researchers have suggested that risk could be shared, both by contractual mechanisms (e.g., Cachon, 2002 and Tsay et al., 1999) and by improved collaboration (e.g., Norrman and Jansson, 2004; Chen et al., 2013). The relevant sharing mechanisms such as contracts (e.g., wholesale price contracts, buy back contracts, revenue sharing contracts, quantity-base contracts etc.) and collaborative initiatives (e.g., vendor managed inventory, collaborative planning, forecasting, replenishment etc.) are deemed to facilitate efficient coordination between supply chain members. Such mechanisms also align the incentives among firms (Narayanan and Raman, 2004) and reduce the vulner- ability of the supply chain against different operational problems and disruptions (Faisal et al., 2006; Juttner, 2005; Norrman and Jansson, 2004; Tang and Nurmaya Musa, 2011). Indeed, Faisal et al. (2006) identify risk sharing as one of the critical SCRM enablers. Juttner (2005) advocates that firms' sharing risk management responsibility is fundamental to effective SCRM. In addition, risk sharing mechan- ism could not only reduce SCRs, but also contribute positively to long-term oriented cooperation (Cooper et al., 1997; Ellram and Cooper, 1990) and competitive advantage (Cooper and Ellram, 1993). Thus we postulate the following hypothesis:

H2. Risk sharing mechanism is positively related to financial performance.

2.3. Moderating effects of collaborative relationship characteristics

The literature on collaborative relationships suggests that, through such inter-firm relationships, firms can more effectively manage their SCRM activities (Lavastre et al., 2014). Specifically, collaborative relationships help supply chain members access complementary resources (Park et al., 2004), share risks (Kogut, 1988), reduce costs of opportunism and monitoring (Croom, 2001), lower transaction costs (Kalwani and Narayandas, 1995), and enhance profit performance and competitive advantage (Mentzer et al., 2000). By using the perspective of the collaboration relation- ship approach, we identify three relevant factors that could influence the effectiveness of risk information sharing and risk

G. Li et al. / Int. J. Production Economics 164 (2015) 83–94 85

sharing mechanism, namely relationship length (Spekman, 1988), supplier trust (MacDuffie and Helper, 2007; Wu et al., 2014), and shared SCRM understanding (Chopra and Sodhi, 2004; Faisal et al., 2006; Juttner, 2005).

2.3.1. The moderating effect of relationship length Relationship length represents the average lifetime of the

relationships between a firm and its major suppliers for the firm's major product line (Tang and Rai, 2012). When supply chain members have a long-term business relationship, organizational routines can be established among them inadvertently (Nelson and Winter, 1982). The joint SCRM practices of this study, i.e., risk information sharing and risk sharing mechanism, are likely rele- vant organizational routines that firms could develop through a long-term business relationship. Agency theory offers similar insights by suggesting that a long-term business relationship is conducive to behaviour-based practices between an agent and the principal (Eisenhardt, 1989). When a firm implements risk infor- mation sharing with its long-term supply chain partners, they exchange SCR information frequently. Because of the extra insights concerning the operations and problems of each other, firms involved can not only mitigate their SCRs more effectively, but also reduce the information asymmetry and the threat of oppor- tunism between them, thereby further enhancing the effectiveness of risk information sharing adoption. As for risk sharing mechan- ism, the relevant literature suggests that firms in a long-term relationship tend to use explicit contractual governance methods to manage their joint activities (Lee and Johnson, 2010), implying that a long-term business relationship provides a favorable envir- onment for supply chain members to use formal risk sharing mechanisms to manage their activities and resources in SCRM. Thus we propose the following two hypotheses:

H3. Relationship length positively moderates the relationship between risk information sharing and financial performance.

H4. Relationship length positively moderates the relationship between risk sharing mechanism and financial performance.

2.3.2. The moderating effect of supplier trust Supplier trust refers to the extent to which a firm trusts its most

important supplier. Trust plays a key role in any organizational relationship (Morgan and Hunt, 1994; Ring and Van de Ven, 1992) and is widely recognized as an essential element of collaborative buyer–supplier relationships (Anderson and Narus, 1990). A high level of trust creates motivation to open communication and willingness to take risks between partner firms (Corsten and

Kumar, 2005; Kwon and Suh, 2005). Trust is an expectation that partners will not act in an opportunistic manner even if short- term incentives exist (Chiles and McMackin, 1996). Trust also contributes significantly to cooperation (Achrol, 1991) and long- term stability (Spekman, 1988). Through reduced opportunistic behaviors and improved cooperation and relationship stability, trust encourages supply chain members to exchange SCR informa- tion more thoroughly and frequently, thereby improving the effectiveness of risk information sharing. Furthermore, trust helps align the incentives among business partners more effectively (Narayanan and Raman, 2004) and is considered a predictor of risk sharing behaviors between supply chain members (Juttner, 2005; Mentzer et al., 2001). Trusted relationships between buyers and suppliers also help reduce cost and develop problem-solving capabilities (Stuart et al., 1998). Hence, as a result of the better aligned incentives, and improved cost efficiency and capabili- ties, trust supports supply chain members to adopt risk sharing mechanism more effectively. Thus we suggest the following two hypotheses:

H5. Supplier trust positively moderates the relationship between risk information sharing and financial performance.

H6. Supplier trust positively moderates the relationship between risk sharing mechanism and financial performance.

2.3.3. The moderating effect of shared SCRM understanding Shared SCRM understanding between supply chain members is

another relevant factor that may moderate the effectiveness of joint SCRM efforts (Chopra and Sodhi, 2004; Faisal et al., 2006; Juttner, 2005). According to Hinds and Weisband (2003), shared SCRM understanding refers to the extents of cognitive overlap and commonality in beliefs, expectations, and perceptions about SCRM. The relevant literature suggests that when exchange partners share a similar vision and goal, they are more likely to make relationship-specific investments in the relationship (Henke and Zhang, 2010; Teece, 1992). As both risk information sharing and risk sharing mechanism are relationship-specific practices, shared SCRM understanding may influence the firms involved to invest more resources in the adoption of such practices. Indeed, there has been evidence indicating that both knowledge transfer- ence (Wathne et al., 1996) and shared understanding (Coleman and Coleman, 1994) exist in firms with repeated interactions. It can be inferred that through better shared understanding of SCRM, firms can know what knowledge (i.e., SCR information) they have to share with their supply chain partners in order to enhance their efforts of SCR mitigation. Furthermore, shared understanding

Fig. 1. The conceptual model.

G. Li et al. / Int. J. Production Economics 164 (2015) 83–9486

between firms helps aligns the firms' joint management efforts by reducing the potential implementation problems, errors, frustra- tion, and conflicts (Hinds and Weisband, 2003). This implies that shared SCRM understanding should help supply chain partners to align and share their responsibilities in SCRs through reduced problems, errors, and conflicts, leading to more effective risk shar- ing mechanism adoption. Thus we postulate the following two hypotheses:

H7. Shared SCRM understanding positively moderates the relation- ship between risk information sharing and financial performance.

H8. Shared SCRM understanding positively moderates the rela- tionship between risk sharing mechanism and financial perfo- rmance.

3. Research methodology

3.1. Data collection

To test the hypotheses of this study, we surveyed a cross-sectional sample of 510 manufacturers in China. As China is a very large country whose economic development varies across different regions, Zhao et al. (2006) categorize all provinces in China into seven regions for research purposes. We strategically selected five of these regions to provide geographic and economic diversity. Northwest China and Central China represent relatively undeveloped areas with low levels of marketization. The Bohai Sea Economic Area has moderate levels of development and marketization. The Yangtze River Delta and Pearl River Delta are developed areas with high levels of marketization. We then randomly selected companies from the yellow pages of China Telecom for major cities in these five regions. After that, we collected data from the sample firms by means of email and field study. We followed the key informant approach (Kumar et al., 1993; Phillips, 1981) to collect data from one informant who is highly knowledgeable about SCRM of the sample firms. The typical job titles of the key informants are supply chain managers, procurement managers, opera- tions managers, vice presidents (sales and marketing), and CEO/ presidents. To encourage informants' participation, we emphasized that we would strictly protect their privacy and offer them a summary of the results. The field study and email generated 388 responses and 350 of them were usable, indicating an effective response rate of 68.63%. We conducted the t-test to examine the non-response bias. The test results suggest that no difference was found between the early and later questionnaires in terms of ownership, industry, and sales revenue (Armstrong and Overton, 1977).

Table 1 shows the sample firms' profiles. The data collection yielded a heterogeneous sample covering a broad range of regions, industry sectors, and ownerships. From the distribution of the sample firms' employee numbers, assets, and annual sales, it can be inferred that a big portion of them are large firms, which have the resources and capability to conduct advanced SCRM practices. Table 2 shows the informants' profiles. Their job titles (i.e., CEO, president, chairman, vice president, and supply chain/OM/risk control/purchas- ing manager) suggest that they should be knowledgeable about the SCRM practices and the overall operations of their firms.

3.2. Measures

Based on an extensive review of the SCM literature, we identify some existing items and insights for the constructs of this study. We modify the existing items to fit them to the specific context of this study (i.e., SCRM between supply chain members). Indeed, the measurement of the joint SCRM practices pertains to using new constructs. To develop these new constructs, we adapt Menor and Roth's (2007) two-stage approach to review the relevant literature and

interview managers. Except for relationship length, we use multiple- items as indirect, reflective indicators for all of the constructs. All the items are presented on seven-point Likert scales (anchored at 1¼“strongly disagree” and 7¼“strongly agree”).

One major challenge in the development of our survey instru- ment is that while our constructs are based on the pertinent literature in English, our informants are Chinese. The translation of the draft English survey instrument into a Chinese-version was conducted by a native Chinese operations management (OM) professor. This version was then back-translated into English. This translated English-version was checked against the original English-version by two OM PhD students. To further refine the survey instrument, we pilot tested the survey with two other OM PhD students and 20 senior managers of Chinese manufacturers. Based on the feedback gathered from the pilot test, we modified and improved the survey instrument. Except for the constructs relating to the hypotheses of this study (see Appendix A), we identified five control variables, namely firm age, firm size, region, industry, and ownership, in order to remove certain extraneous effect in the hypothesis test.

3.3. Common method bias

To minimize the potential threat of common method bias, we took steps to address this concern. At the questionnaire design stage, we (a) developed concise and clear items based on a literature review, manager interviews, and a pilot test (Tourangeau et al., 2000), (b) reversely stated some of the items (Boyer and Pagell, 2000), (c) presented the independent and dependent variables in different sections (Podsakoff et al., 2003), (d) assured the informants the anonymity of the data (Podsakoff et al., 2003), and offered them the result summary to motivate their participation (Swink and Song, 2007). At the data collection stage, we employed two approaches (Podsakoff et al., 2003), i.e., email and field study, to collect data. At the data analysis stage, we employed two approaches to test the common method variance. First, a confirmatory factor analysis (CFA) was conducted to test Harman's single-factor model (Cao and Zhang, 2011; Flynn, et al., 2010). The single model's fit indices (χ2=df ¼ 8:15, NNFI¼0.88, CFI¼0.89, SRMR¼0.11, and RMSEA¼0.16) were unaccep- table and significantly worse than those of the measurement model, indicating that a single factor does not reflect our data and common method bias is unlikely significant. Second, a measurement model only including the traits and another model including a method factor and the traits were compared (Cao and Zhang, 2011; Flynn, et al., 2010; Podsakoff et al., 2003). The results indicate that method factor, just accounting 3.21% of the total explained variance, marginally improved the model fit (CFI by 0.01, RMSEA by �0.01, SRMR by �0.013). The item loadings for their factors are still significant, despite the inclusion of a method factor (Paulraj et al., 2008). Based on these results, we conclude that common method variance does not pose a significant threat to the data of this study.

3.4. Measurement assessment

First, CFA was used to assess unidimensionality of our con- structs. Four items were dropped as they did not load well on their constructs. The results of measurement model fit indices, such as chi-squared statistic ðχ2=df ¼ 3:1Þ, NNFI (0.97), CFI (0.97), RMSEA (0.079), and SRMR (0.057) are acceptable (Hu and Bentler, 1999), suggesting all the constructs are unidimensional. Second, follow- ing the recommendation of Garver and Mentzer (1999), we used Cronbach's Alpha, Average Variance Extracted (AVE), and SEM construct reliability to test the reliability of our constructs. The values of Cronbach's Alpha, AVE, and SEM construct reliability of all constructs are higher than their respective threshold values of 0.7, 0.5, and 0.7, indicating adequate construct reliability in the

G. Li et al. / Int. J. Production Economics 164 (2015) 83–94 87

data (Garver and Mentzer, 1999; O’Leary-Kelly and Vokurka, 1998 (see Appendix A). Third, CFA was further used to test convergent validity and discriminant validity (Bagozzi et al., 1991; Flynn et al., 2010; O’Leary-Kelly and Vokurka, 1998). We built a CFA model to test convergent validity by linking each item to its corresponding construct and letting the covariance among the constructs be freely estimated. The results of the fit indices (χ2=df ¼ 3:1, NNFI¼0.97, CFI¼0.97, RMSEA¼0.079, and SRMR¼0.057) suggest that the model is acceptable (Hu and Bentler, 1999), indicating adequate convergent validity (O’Leary-Kelly and Vokurka, 1998). The results also indicate that all factor loadings are greater than 0.5 and are significant at the po0.001 level (see Appendix A),

suggesting adequate convergent validity of the constructs (Hair et al., 2006). Finally, we tested the discriminant validity by conduct- ing a series of χ2 difference tests between unconstrained CFA models and constrained CFA models (Bagozzi et al., 1991; Flynn et al., 2010; O’Leary-Kelly and Vokurka, 1998). The constrained CFA model was formed by any possible pair of latent constructs with the correlations between the paired constructs fixed to 1.0., whereas the unconstrained CFA model comprising the same pair of constructs with the correlation between them unfixed. Table 3 lists the result of these χ2 difference tests. All the differences are significant at po0.001, indicating strong discriminant validity (Bagozzi et al., 1991; Fornell and Larcker, 1981). Furthermore, the value of the average variance extracted (AVE) of each construct is greater than the squared correlation between that construct and the other constructs (Fornell and Larcker, 1981), further supporting adequate discriminant validity of data.

4. Analysis and results

We employ hierarchical regression analysis to test our hypoth- eses. We standardize all the independent variables and moderate variables, and then compute the interaction terms. To eliminate undesirable sources of variance, we include five control variables in our models, which include firm age, firm size, region, industry, and ownership.

By using the general procedure of hierarchical regression analysis, we first enter the control variables (Control model), then the independent variables (Main model), and the moderate vari- ables (Moderator model). After that, we test the interaction effect

Table 1 Company profile (N¼350).

Company profiles Count Percent Company profiles Count Percent (%) Region: Industry type:

Yangtze River Delta 102 29.14% F&B, alcohol, &Cigar 17 4.86 Pearl River Delta 85 24.29% Textiles & Apparel 17 4.86 Bohai Sea Economic Area 39 11.14% Wood & Furniturea 5 1.43 Central China 30 8.57% Chem & Petrochem 18 5.14 Northwest China 40 11.43% Building Materiala 7 2.00 Others 54 15.43% Pharm & Medicala 9 2.57

Ownership: Transportation equipmenta 8 2.29 State-owned 44 12.57% Mech. & elect. equipment 25 7.14 Collective 6 1.71% Rubber & Plastics 16 4.57 State-owned stock 25 7.14% Precision instrumentationa 2 0.57 Private 185 52.86% Toysa 1 0.29 Sino-foreign 17 4.86% Tele., Elect. & Electrical 70 20.00 Foreign 71 20.29% Metal 20 5.71 Blank 2 0.57% Publishing & Printinga 10 2.86

Companies' employees: Arts & Craftsa 1 0.29 o ¼49 31 8.86% Mechanical 64 18.29 50–99 38 10.86% Cleaning & Cosmeticsa 2 0.57 100–299 63 18.00% Non-metallic mineralsa 5 1.43 300–999 82 23.43% Service 24 6.86 1000–1999 39 11.14% Othersa 29 8.29 2000–4999 44 12.57% 4 ¼5000 49 14.00% Blank 4 1.14%

Companies' sales (Million): Companies' assets (Million): o5 20 5.71% o5 24 6.86 5–10 23 6.57% 5–10 19 5.43 10–20 25 7.14% 10–20 27 7.71 20–50 39 11.14% 20–50 42 12.00 50–100 48 13.71% 50–100 56 16.00 100–500 60 17.14% 100–500 71 20.29 500–1000 34 9.71% 500–1000 22 6.29 41000 92 26.29% 41000 78 22.29 Blank 9 2.57% Blank 11 3.14

a We categorize these industries to one dummy variable in the regression, as they just contain few samples.

Table 2 Respondents profile (N¼350).

Respondents profile Amount Percentage (%)

Positions of respondents: Chairman/president/CEO 49 14.00 Vice president 52 14.86 Supply chain/OM/risk control/purchasing manager 54 15.43 Marketing/sales manager 32 9.14 Other middle manager 87 24.86 Others 18 5.14 Blank 58 16.57

Years in the position: r1 31 8.86 2–4 132 37.71 5–8 81 23.14 8–11 26 7.43 Z12 23 6.57 Blank 57 16.29

G. Li et al. / Int. J. Production Economics 164 (2015) 83–9488

of the two independent variables and three moderate variables, respectively. Note that the interaction of risk information sharing and relationship length (Model 1), risk sharing mechanism and relationship length (Model 2), risk information sharing and sup- plier trust (Model 3), risk sharing mechanism and supplier trust (Model 4), risk information sharing and shared SCRM understand- ing (Model 5), and risk sharing mechanism and shared SCRM understanding (Model 6) are tested in different models separately. Each regression model's residuals approximate the normal dis- tribution. The values of the variance inflation factors (VIF) are way below the threshold value (the highest VIF¼3.16), suggesting that multicollinearity poses not a significant threat to the data (Hair et al., 1998; Tabachnick and Fidell, 1996) (see Table 4 for these results).

The Base Model in Table 4 includes the five control variables in the regression. While firm age and firm size (employee number) are reflected by numerical values, region, industry, and ownership involve the use of dummy variables in the analyses. The Main Model in Table 4 adds the independent variables to the regression. The significant coefficient for risk information sharing ðβ ¼ 0:34; po0:01Þ indicates support for H1 – risk information sharing is positively related to financial performance. The significant coefficient for risk sharing mechanism ðβ ¼ 0:17; po0:01Þ offers support for H2 – risk sharing mechanism is positively related to financial performance. The Moder- ate Model in Table 4 adds the moderate variables, which is the base model for Models 1 to 6.

Models 1 to 6 test the effect of the three moderate variables. In Model 1, the interaction of risk information sharing and relation- ship length is added. The significant interaction coefficient ðβ ¼ 0:10; po0:05Þ indicates support for H3 – relationship length positively moderates the effect of risk information sharing on financial performance. Model 2 adds the interaction of risk sha- ring mechanism and relationship length. The interaction coeffi- cient ðβ ¼ 0:07; p40:1Þ is not significant, suggesting no support for H4. In Model 3, the interaction of risk information sharing and supplier trust is added. The interaction coefficient ðβ ¼ 0:11; po0:05Þ is significant, indicating support for H5 – supplier trust positively moderates the effect of risk information sharing on financial performance. Model 4 adds the interaction of risk sharing mechanism and supplier trust. The insignificant interaction coeffi- cient ðβ ¼ 0:07; p40:1Þ offers no support for H6. In Model 5, the interaction of risk information sharing and shared SCRM under- standing is added. The interaction coefficient ðβ ¼ 0:07; p40:1Þ is not significant, offering no support for H7. Model 8 adds the interaction of risk sharing mechanism and shared SCRM under- standing. The significant interaction coefficient ðβ ¼ 0:08; po0:1Þ indicates support for H8 – shared SCRM understanding positively

moderates the effect of risk sharing mechanism on financial performance. The results of hypothesis testing are presented in Fig. 2 and Table 5.

5. Discussion and conclusion

Drawing on the literature on SCM, SCRM, agency theory, and collaborative relationships, this study investigates the financial consequence of two joint SCRM efforts, namely risk information sharing and risk sharing mechanism, under three collaborative relationship characteristics, namely relationship length, supplier trust, and shared SCRM understanding. Using a survey dataset comprising 350 Chinese manufacturers from various industrial sectors, we find that both risk information sharing and risk sharing mechanism are associated with financial performance. Our results also indicate that while relationship length and supplier trust can strengthen the effectiveness of risk information sharing, shared SCRM understanding enhances the effectiveness of risk sharing mechanism. We next discuss the theoretical and practical implica- tions of these findings.

5.1. Contributions to the literature

Examining a conceptual model pertinent to the performance consequences of two important joint SCRM practices, this study's contributions to theory are three-folds. First, we offer specific new insights on two joint SCRM efforts, i.e., risk information sharing and risk sharing mechanism. SCRM researchers have realized that the management of SCRs needs an SCM perspective (Juttner, 2005; Kleindorfer and Saad, 2005). Relevant prior studies highlight the importance of coordination and collaboration among supply chain partners to successful SCRM (Tang, 2006a). Juttner (2005) advocates a SCRM philosophy concerning “an openness to share risk-related information and the willingness to accept SCRs as joint risks”. Like- wise, Kleindorfer and Saad (2005) address two key SCRM issues: (1) sharing information among supply chain members in order to increase the supply chain's visibility of vulnerabilities, and (2) establish- ing incentives across supply chain members different implement differing SCRM activities. Building on such prior studies, we identify risk information sharing and risk sharing mechanism as two critical joint SCRM efforts. Indeed, risk information sharing represents a “soft” practice that is associated with closer relationships between supply chain members and can facilitate the implementation of those internal or inter-firm SCRM practices that rely on risk information. On the other hand, risk sharing mechanism represents a “hard” practice concerning formal institutional arrangements for SCRM activities between supply chain members. This aligns supply chain partners' incentives and coordinates their activities in differing joint SCRM efforts. Given the lack of systematic efforts in examining joint SCRM practices in the literature, this study contributes to the literature by identifying and discussing two highly relevant joint SCRM practices.

Second, although numerous strategies or practices of SCRM have been available in the literature (Braunscheidel and Suresh, 2009; Christopher and Lee, 2004; Faisal et al., 2006; Juttner, 2005; Tang, 2006b; Tang and Tomlin, 2008), the validity and effectiveness of such strategies and practices have rarely been validated by empirical evidence (Colicchia and Strozzi, 2012; Hendricks et al., 2009; Kleindorfer and Saad, 2005). Researchers have suggested that, to support and facilitate supply chain managers in decision making, empirical research focusing on the effectiveness of risk reduction strategies and practices is eagerly needed (Juttner et al., 2003; Khan and Burnes, 2007; Sodhi et al., 2012; Tang, 2006b). This study addresses this call by empirically examining the financial consequence of risk information sharing and risk sharing mechanism, advancing knowledge in the empirical SCRM literature.

Table 3 The assessment of constructs' discriminant validity (N¼350).

Construct pairs Unconstrained Constrained Δχ2

χ2 d.f. χ2 d.f.

Risk sharing mechanism Risk information sharing 419.98 53 450.78 54 30.8 Supplier trust 261.05 34 343.35 35 82.3 Shared SCRM understanding 173.65 26 214.22 27 40.57 Financial performance 277.22 34 323.47 35 46.25 Risk information sharing Supplier trust 328.21 53 447.44 54 119.23 Shared SCRM understanding 260.32 43 335.56 44 75.24 Financial performance 384.49 53 461.44 54 76.95 Supplier trust Shared SCRM understanding 152.17 26 278.90 27 126.73 Financial performance 279.90 34 385.40 35 105.5 Shared SCRM understanding Financial performance 187.09 26 261.49 27 74.4

G. Li et al. / Int. J. Production Economics 164 (2015) 83–94 89

Third, integrating the literature of agency theory and colla- borative relationships, we propose that the effectiveness of our joint SCRM practices could be negatively affected by certain agency problems, and that as these joint SCRM practices are behavioral-based management efforts, a collaborative approach could be used to reduce the negative impacts of the relevant agency problems. Thus, we identify three collaborative relation- ship characteristics, namely relationship length, supplier trust, and shared SCRM understanding, as factors that could have a positive influence on the effectiveness of our joint SCRM practices. By empirically testing the effects of these relationship characteristics, this study contributes to theory by demonstrating that when firms exert joint SCRM efforts, there are certain circumstances under which their efforts would be particularly effective. These findings

offer new and precise insights to the literature on SCRM and collaborative relationships.

However, our findings reveal that the moderating effects of the three identified collaborative relationship characteristics vary between the two joint SCRM practices. Specifically, relationship length and supplier trust can positively moderate the effectiveness of risk information sharing but not risk sharing mechanism, whereas shared SCRM understanding can positively moderate the effectiveness of risk sharing mechanism but not the risk information sharing. One plausible explanation could be related to the nature of these characteristics. A long-term orientation (i.e., relationship length) and supplier trust are relational attributes that entail relational outcomes such as informal communication and personal ties (Coleman and Coleman, 1994; Cousins et al.,

Table 4 Regression results.

Controls Main Moderate 1 2 3 4 5 6 DV Financial performance

Independent variables Constant 4.04nn 4.09nn 4.30nn 4.32nn 4.30nn 4.23nn 4.28nn 4.29nn 4.27nn

H1: Risk information sharing (RIS) 0.34nn 0.20nn 0.18nn 0.20nn 0.19nn 0.19nn 0.18nn 0.18nn

H2: Risk sharing mechanism (RSM) 0.17nn 0.04 0.05 0.03 0.03 0.03 0.04 0.04 Moderating variables

Relationship length (RL) 0.02

0.02 0.02 0.02 0.02 0.02 0.02

Supplier trust (ST) 0.05

0.05 0.04 0.04 0.04 0.04 0.04

Shared SCRM understanding (SU) 0.31nn

0.31 0.32nn 0.31nn 0.32nn 0.31nn 0.32nn

Interaction effects H3: RIS � RL 0.10n H4: RSM � RL 0.07 H5: RIS � ST 0.11n H6: RSM � ST 0.07 H7: RIS � SU 0.07 H8: RSM � SU 0.08†

Control variables Firm agea �0.10† �0.14nn �0.16nn �0.16nn �0.16nn �0.15nn �0.15nn �0.16nn �0.15nn Firm size (employee number)b 0.23nn 0.21nn 0.19nn 0.18nn 0.19nn 0.18nn 0.18nn 0.18nn 0.18nn

Regionc Yangtze River Delta 0.21nn 0.21nn 0.21nn 0.21nn 0.21nn 0.21nn 0.21nn 0.21nn 0.21nn

Pearl River Delta 0.26nn 0.27nn 0.23nn 0.23nn 0.23nn 0.23nn 0.23nn 0.23nn 0.23nn

Bohai Sea Economic Area 0.02 0.10† 0.11† 0.09 0.10† 0.11† 0.11† 0.10† 0.10†

Central China �0.01 0.02 0.02 0.02 0.02 0.03 0.03 0.02 0.02 Northwest China 0.10 0.15nn 0.13n 0.14n 0.13n 0.13n 0.13n 0.13n 0.13n

Industryd F&B, alcohol, &Cigar 0.07 0.06 0.05 0.05 0.05 0.06 0.05 0.05 0.05 Textiles & Apparel �0.05 �0.04 �0.02 �0.03 �0.03 �0.02 �0.02 �0.03 �0.02 Chem & Petrochem �0.03 �0.02 �0.03 �0.03 �0.03 �0.04 �0.04 �0.03 �0.04 Mech. & elect. equipment �0.10n �0.06 �0.05 �0.05 �0.05 �0.05 �0.05 �0.05 �0.05 Rubber & Plastics �0.04 �0.03 �0.03 �0.04 �0.04 �0.04 �0.04 �0.04 �0.04 Tele., Elect. & Electrical �0.18nn �0.13n �0.13n �0.13n �0.13n �0.13n �0.13n �0.13n �0.14n Metal �0.08 �0.07 �0.06 �0.05 �0.05 �0.05 �0.05 �0.05 �0.05 Mechanical 0.05 0.02 0.00 0.01 0.00 0.00 0.00 0.00 0.00 Service 0.03 �0.03 �0.02 �0.01 �0.02 �0.03 �0.02 �0.03 �0.03

Ownershipe Collective �0.03 0.00 �0.01 �0.01 �0.01 �0.02 �0.01 �0.01 �0.01 State-owned stock 0.00 0.07 0.06 0.06 0.06 0.07 0.07 0.06 0.06 Private 0.15 0.19n 0.16n 0.15n 0.15n 0.17n 0.17n 0.16n 0.17n

Sino-foreign 0.07 0.07 0.06 0.05 0.06 0.07 0.07 0.07 0.07 Foreign 0.16n 0.16n 0.10 0.10 0.10 0.11 0.10 0.10 0.10 R2 0.15 0.35 0.39 0.40 0.40 0.40 0.40 0.40 0.40 ΔR2 0.15nn 0.20nn 0.04nn 0.01n 0.01 0.01n 0.01 0.01 0.01†

F-value for ΔR2 2.78 48.41 8.08 4.47 2.62 5.62 2.26 2.24 2.94

a Natural logarithm. b Employees is measured in seven categories (1–49, 50–99, 100–299, 300–999, 1000–1999, 2000–4999, and 45000). c Other area as base dummy. d (1) Other industry as base dummy, (2) As there are too many industries in our sample, the industries not shown in this table are categorized into Other industry. e Stage-owned as base dummy. † po0.10. n po0.05. nn po0.01.

G. Li et al. / Int. J. Production Economics 164 (2015) 83–9490

2006). When a firm works with a long-term and trusting supply chain partner, it is likely to share SCR information to this partner in a more timely and thorough manner, and the relational outcomes entailed by the closer relationship (e.g., informal communication and personal ties) further support the undertaking of the neces- sary interactions and communication during the sharing process, explaining the positive influence from relationship length and supplier trust on risk information sharing. On the other hand, shared SCRM understanding is not about relationship closeness but focuses specifically on what supply chain partners know about one another regarding SCRM beliefs and practices. Similarly, risk sharing mechanism is not much related to relationship closeness but concerned with supply chain members' contractual agree- ments on SCR duties and obligations. When a firm and its supply chain partners adopt risk sharing mechanism and know the SCRM practices of each other very well, they could spend less time on negotiation, reach a consensus on the shared duties and obligation more quickly, and work together more effectively when resolving SCR problems, explaining the positive influence from shared SCRM understanding on risk sharing mechanism.

5.2. Managerial implications

First, managers should employ an SCM perspective to mana- ging SCRs – they should pay attention to collaborating with

supply chain partners to work jointly to mitigate SCRs. We specifically advocate two joint SCRM practices, namely risk information sharing and risk sharing mechanism. The former is an essential element supporting SCRM information systems that manage the critical and propriety risk-related information in the supply chain. Without such systems, the implemen- tation of many modern SCRM practices would not be possible (Christopher and Lee, 2004; Norrman and Jansson, 2004; Sheffi and Rice, 2005). The latter is concerned with formal arrange- ments (e.g., contracts) for supply chain partners' shared obliga- tions and responsibilities towards SCRM. This practice reduces SCR-related disputes between supply chain members and pro- vide them with specific guidelines on how they should work together to mitigate SCRs.

Second, we offer guidelines indicating that these two joint SCRM practices are particularly effective when certain relationship characteristics exist. Specifically, risk information sharing is parti- cularly effective when there is a high level of long-term orienta- tion or supplier trust, whereas risk sharing mechanism is particularly effective when a high level of shared SCRM under- standing exists between the firms. Such precise guidelines help firms decide if their supply chain relationships are favorable for them to implement these joint SCRM practices.

5.3. Limitations and future research directions

Several limitations exist in the present study, which warrant further future investigation. First, although we examine two critical joint SCRM practices in this study, other relevant joint efforts could be related to risk identification, forecasting, and warning. Future research can examine the effectiveness of such practices. Second, in addition to relationship length, supplier trust, and shared SCRM understanding, there could be other collabora- tive relationship characteristics (e.g., relationship-specific invest- ment, interdependence) that could moderate the effectiveness of joint SCRM practices. Future research could identify and examine other moderating factors. Third, this study employs a cross- sectional date-set. Future research could collect longitudinal data in order to test the causal relationships in the hypotheses more rigorously. Finally, future work could employ other relevant theories (e.g., transaction cost economies, resource-based view etc.) as the perspective to identify different joint SCM practices and their corresponding moderating factors.

Table 5 Summary of major results.

Hypotheses Findings

H1: Risk information sharing is positively related with financial performance

Supported

H2: Risk sharing mechanism is positively related with financial performance

Supported

H3: Relationship length positively moderates the effect of risk information sharing on financial performance

Supported

H4: Relationship length positively moderates the effect of risk sharing mechanism on financial performance

Not supported

H5: Supplier trust positively moderates the effect of risk information sharing on financial performance

Supported

H6: Supplier trust positively moderates the effect of risk sharing mechanism on financial performance

Not supported

H7: Shared SCRM understanding positively moderates the effect of risk information sharing on financial performance

Not supported

H8: Shared SCRM understanding positively moderates the effect of risk sharing mechanism on financial performance

Supported

Fig. 2. The hypotheses test result.

G. Li et al. / Int. J. Production Economics 164 (2015) 83–94 91

Acknowledgments

This research was partially supported by The Hong Kong Polytechnic University under grant number G-YL37, the Natural Science Foundation of China under grant number 61174171, and the Fundamental Research Funds for the Central Universities.

Appendix A

See Table A1.

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Table A1 Construct measures.

Measures Factor loading

Risk information sharing ( Cronbach's¼0.92, AVE¼0.62, SEM construct reliability¼0.93) Our partners share proprietary information with us (Li and Lin, 2006). 0.77 We share accurate risk related information with our supply chain members (Faisal et al., 2006). 0.83 We are willing to share real time information on demands with our suppliers (Faisal et al., 2006). 0.78 Information is actively shared between functional teams in our firm (Schoenherr and Swink, 2012). 0.78 It is expected that members in the supply chain keep each other informed about events or changes that may affect the other party (Li et al., 2005). 0.75 Our partners keep us fully informed about issues that affect our business (Li, et al., 2005). 0.80 We have closely integrated information systems with key suppliers and logistic providers (Chen and Paulraj, 2004). 0.74

Risk sharing mechanism ( Cronbach's¼0.90, AVE¼0.64, SEM construct reliability¼0.90) Our firm utilizes a strategy of sharing supply chain risk with our supply chain partners (e.g., buy back agreements, cost/revenue sharing, etc.) (Norrman, 2008)

0.84

There are risk management policies defining responsibilities for each party of the supply chain member (Waters 2011). 0.82 There are clear risk and revenue sharing rules between the members of the supply chain (Faisal et al., 2006; Mentzer et al., 2001) 0.81 We have formal mechanism (e.g., buy back agreement) and informal mechanism (e.g., verbal commitment) to share risk with supply chain partner (Norrman, 2008).

0.70

There are wildly acknowledged and accepted risk/revenue sharing mechanism in our supply chain (Cavinato, 2004). 0.83 Relationship length

How many years we do business with this supplier? Supplier trust ( Cronbach's¼0.86, AVE¼0.55, SEM construct reliability¼0.86)

This supplier is trustworthy (Hill et al., 2009). 0.62 We believe the information that this supplier provide us (Hill et al., 2009). 0.71 This supplier is genuinely concerned that our business succeeds (Hill et al., 2009) 0.74 When making important decisions, this supplier considers our welfare as well as its own (Hill et al., 2009). 0.78 We trust that this supplier keeps us best interest in mind (Hill et al., 2009) 0.83

Shared SCRM understanding ( Cronbach's¼0.89, AVE¼0.68, SEM construct reliability¼0.82) There are strong beliefs among our supply chain members that we should management SCR cooperatively (Douglas and Fredendall, 2004). 0.65 Supplier chain partners participate the supply chain risk related training offered by our firm (Douglas and Fredendall, 2004) 0.82 Suppliers are encouraged to join us to manage supply chain risks (Chen et al., 2013). 0.91 Supply chain partners work with us together to cooperatively manage supply chain risk (e.g., regular consultations) (Chen et al., 2013). 0.89

Financial performance ( Cronbach's¼0.90, AVE¼0.64, SEM construct reliability¼0.90) We have better return on investment (ROI) than our competitors (Lusch and Brown, 1996). 0.83 We have better return on sales (ROS) than our competitors (Lusch and Brown, 1996). 0.84 We have better growth in sales than our competitors (Lusch and Brown, 1996). 0.76 We have better growth in profit than our competitors (Lusch and Brown, 1996). 0.88 We have better growth in market share than our competitors (Lusch and Brown, 1996). 0.67

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