Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective

Int. J. Production Economics 148 (2014) 122–132

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

Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective Ing-Long Wu a,n, Cheng-Hung Chuang b, Chien-Hua Hsu a a b

Department of Information Management, National Chung Cheng University, 168 University Road, Ming-Hsiung, Chia-Yi, Taiwan Department of Information Management, Chia Nan University of Pharmacy and Science, Taiwan

art ic l e i nf o

a b s t r a c t

Article history: Received 10 July 2012 Accepted 18 September 2013 Available online 4 October 2013

In modern business, competition is no longer between organizations, but among supply chains. Supply chain is complex in nature, involving various work flows across trading partners. Two major concerns arise in enabling supply chain performance, information sharing and collaborative effort. However, it is necessary to further identify the fundamentals for their implementation in terms of partners' exchange beliefs. Social exchange theory guides interactional behaviors for the expectation of a reward from partners. This study considers four key social exchange issues, trust, commitment, reciprocity, and power and to be antecedents of information sharing and collaboration. This study thus proposes a novel research model to examine the relationships among SET-based variables, information sharing and collaboration, and supply chain performance. Empirical findings show that SET-based issues are important to determine information sharing and collaboration and both information sharing and collaboration indicate partial mediation effect on supply chain performance. & 2013 Elsevier B.V. All rights reserved.

Keywords: Supply chain management Social exchange theory Information sharing Collaboration Supply chain performance

1. Introduction In the contemporary business environment, competition is no longer between organizations, but between supply chains. Organizations are increasingly thinking that they must compete, as part of a supply chain, against other supply chains, to rapidly reflect market changes (Cigolini et al., 2004). The supply chain, in essence, is complex and dynamic across a large number of partners (Vijayasarathy, 2010). To respond to these challenges, supply chain management (SCM) is an important concept to effectively help a focal firm to manage its partners so that they can further build long-term partnerships (Fynes et al., 2008; Sambasivan et al., 2013). SCM can be defined as an effective management on the three complementary flows, material, information, and finance, between a focal firm and its partners (Rai et al., 2006; Lin and Lin, 2006). However, a well-defined mechanism for coordinating partners through using online information plays an important role in effectively managing these flows (Sahin and Robinson, 2002). The major concerns for this mechanism are two-fold: information sharing and collaborative effort (Smith et al., 2007; Yang et al., 2008). First, information is often inconsistent between upstream and downstream of supply chain partners (Prajogo and Olhager, 2012). Supply chain partners may have to forecast their market demands based on incomplete information. All partners thus require

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keeping higher stock for their products or components to immediately respond to market changes. As a result, this would cause the increase of production cost and the reduction of profit margin for partners. This is well known as “bullwhip effect”. Thus, many studies have highlighted that information sharing is one of critical factors for an effective supply chain practice (Narasimhan and Nair, 2005; Jeong and Leon, 2012). Second, a well-defined supply chain is founded on the essence of collaborative behaviors on which a mutual decision-making process is established toward achieving common goals across participants (Smith et al., 2007; Vachon and Klassen, 2008). In essence, collaborative behaviors allow participants to jointly gain a clear understanding of future demand, develop a realistic plan to satisfy the demand, and coordinate related activities in a systematic manner to finish the task (Barratt, 2004). Thus, it is the driving force of an effective supply chain practice (Horvath, 2001). Further, to guide collaborative undertakings, researchers have suggested the importance of advanced IT in supporting information sharing and thereby facilitating collaboration in the supply chain (Smith et al., 2007; Chan et al., 2012). When information sharing and collaboration are closely related to the success of supply chain alliance, however it is imperative to further identify the fundamentals in contributing to both exchange beliefs of partners (Myhr and Spekman, 2005; Sheu et al., 2006). Social exchange theory (SET) argues that individuals or groups attempt to interact with others for the expectation of a reward (Yang et al., 2008). SET assumes that attitudes and behaviors can be assessed by the rewards of interaction minus the cost of that interaction. In the context of supply chain, a supplier makes a contribution to its manufacturer via their partnership policies and

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an expectation from its manufacturer forms for the return of a contribution at a later time (Narasimhan et al., 2009). Many studies have applied SET to examine inter-firm information sharing or collaborative behaviors in the supply chain. SET in supply chain has been defined differently for the particular focus. For example, one study modeled how justice and power issue in SET influence long-term orientation and relational behaviors toward partners (Griffith et al., 2006). Some studies proposed two key issues in SET, trust and commitment, for maintaining relational stability in supply chain alliance (Kwon and Suh, 2005; Yang et al., 2008). Additional studies focused on mutual adaption between partners for developing strategic alliance based on trust and power issue in SET (Hallen et al., 1991; Molm, 1997). Given the theoretical foundations of SET in the supply chain, we thus define comprehensively the antecedents of information sharing and collaboration: trust, commitment, reciprocity, and power. Finally, supply chain performance is the ultimate goal for partners to implement this practice (Tan et al., 2002). In this study, we examine supply chain performance in terms of a focal firm's performance in managing its supply chain. Historically, studies on organizational performance have focused more on financial measure (Lapide, 2000; Ranganathan et al., 2004), and the inconclusive results of IT productivity may be due to applying inappropriate measuring methods (Devaraj and Kohli, 2003). This study therefore considers both financial and non-financial measures for the supply chain performance in a complementary manner. In addition, many studies on supply chain have suggested a number of organizational characteristics for potential effects on achieving supply chain performance, such as industry type and firm size (Banker et al., 2006; Wu and Chang, 2012). We thus specify industry type and firm size as two control variables. Grounding on SET and the interactional behaviors between partners, this study proposes a research model to explore the relationships among exchange drivers, information sharing and collaboration, and supply chain performance. Empirical data are further used to examine this research model.

2. Literature review Based on the above discussion, Fig. 1 provides a pictorial depiction of this research model. The followings discuss the theoretical foundations and the development of hypotheses. 2.1. Supply chain management Most firms are striving to improve their flexibility and customer responsiveness in the dynamic market. The concept of SCM is an important weapon for them to reach the goal (Tan et al., 2002).

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Moreover, the growth in B2B commerce has spotlighted the role of SCM in the modern digital economy. SCM is in a purpose to integrate key business processes among partners for effectively providing various forms of semi-products and services in the end-product creation and hence adds value to customers and other stakeholders (Lambert et al., 1998). Specifically, the mechanism basically involves a large variety of flow activities across the whole supply chain, including material, subassembly, product, order, delivery, payment, and customer service (Stank et al., 2001; Lin and Lin, 2006). It is extremely difficult to effectively manage these complex flows together. These flows require a huge amount of online communication efforts to support information sharing in establishing collaborative behaviors among participants in the supply chain. Two major concerns arise for the problem from the literature review, the technical issue (information technology integration) and the social issue (information sharing and inter-organizational relationship) (Gunasekaran and Ngai, 2004; Prajogo and Olhager, 2012; Wei et al., 2012). However, adopting e-business technologies is known as a popular means for information technology (IT) integration between members and is well defined for most business organizations in the Internet era. A platform of IT integration is also a prerequisite for defining information sharing. It is the notion of this study that over reliance on the technical issue without a willingness to share information for critical activities creates a problem for partners with meaningless connection, that is, lack of an effective collaboration. A new approach is thus the major effort of this study in terms of viewing inter-organizational relationships and information sharing as the important antecedents of collaborative behaviors.

2.2. SET and supply chain management SET originally focuses on cost-profit view for individuals and corporate groups to form basic motivation for an interaction with others (Emerson, 1976). Further, SET argues that attitudes and behaviors for exchange with others are determined by the rewards of interaction minus the cost of that interaction (Luna-Reyes et al., 2005; Kale and Singh, 2009). In other words, the more often a particular exchange is rewarded, the more likely a participant to the exchange is to perform again. SET basically composes a series of basic principles of psychological and economical reinforcement outlining the system of social exchange in an analysis of their participating behaviors, including trust, commitment, reciprocity, justice, relative dependence, and power (Bock and Kim, 2002). Many studies have utilized SET to examine the development of supply chain relationships (Kwon and Suh, 2005; Wei et al., 2012). They argued that social relationships between supply chain partners are formed and maintained because the partners offer reciprocal benefits to one another over time.

Fig. 1. Research model.

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Specifically, Griffith et al. (2006) considered SET to model the outcomes of procedural and distributive justice in supply chain relationships. They argued that the more powerful members in supply chain relationships build up social credit of indebtedness that allows the members to comply with others in the relationships. Relational attitudes and behaviors between members are therefore stimulated through perceived justice demonstrated by the more powerful members. As a result, there are two prominent factors, power and justice, in determining the exchange behaviors. Narasimhan et al. (2009) also used SET to gain a better understanding of supply chain relationships that is characterized by a situation of lock-in dependence between partners. Given the theoretical foundation, two important issues arise for this research, power and justice. Yang et al. (2008) drew on SET for identifying the antecedents of relational stability in supply chain alliance. They contended that relational commitment brings about mutual respect for buyers and suppliers and concerns the common goal for competition from rivalries. The presence of trust creates a better working environment for partner firms as it can increase the reliability of contracts, provide incentives for cooperation, and reduce risk and uncertainty. This study thus suggests two important precursors, commitment and trust, for relational stability affecting alliance performance. Sambasivan et al. (2013) attempted to integrate various theories to completely explain strategic alliance in a supply chain. SET is one of the important theories used to develop the component of relational governance in the research framework in terms of the issues of commitment and trust. Kwon and Suh (2005) investigated supply chain relationships between the level of trust and several relevant factors based on transaction cost theory and SET, and argued that commitment is a key success factor in achieving the relationships and trust is a root in fostering such commitment. Hallen et al. (1991) discussed inter-firm adaptation process for building business relationships drawn from SET and proposed two key issues for explaining such adaptation, trust and power. Building of trust is a crucial element in social exchange processes and assumes the processes evolving over time as the actors mutually and sequentially demonstrate their trustworthiness. The role of power in social exchange indicates the relative dependence between participants in an exchange relationship. In particular, power derives from owning resources that the others need and from controlling the alternative sources of the resources. In sum, we define four key issues in SET to explore supply chain alliance in this study, trust, commitment, reciprocity, and power, in an integrative manner. The deduction process is as follow. First, SET is basically defined as the issues: trust, commitment, reciprocity, justice, relative dependence, and power. In essence, reciprocity and justice, and power and relative dependence are both defined similarly (Griffith et al., 2006; Narasimhan et al., 2009). These studies argued that a partner receiving a valued contribution in an exchange behavior develops a sense of obligation and reciprocates with appropriate responses in a justice manner as well as the relative dependence between two partners in an exchange behavior determines their relative power (Griffith et al., 2006). Four key issues can be classified: trust, commitment, reciprocity, and power. Next, a summary from SET defined differently in supply chain studies, as discussed above, we can integrate into the same four key issues. Accordingly, both arguments provide solid theoretical evidence to define the four key issues in SET. 2.3. Information sharing and collaboration Important information among suppliers, manufacturers, and channels may not be shared correctly online through the whole supply chain and partners attempt to estimate market demands based on incomplete information (Levy et al., 2003; Prajogo and Olhager, 2012). Partners tend to keep more stock in various forms of

material, components, and finished goods for avoiding a shortage. Thus, they will consume high cost with a bullwhip effect in the supply chain and therefore, reduce the profit margin. Many researchers have argued that information sharing is one of critical factors for an effective supply chain and an important solution for mitigating the bullwhip effect (Holm et al., 1999; Subramani, 2004; Zhang and Chen, 2013). Rai et al. (2006) argued that three types of information sharing occur between members, operational, tactical, and strategic. Operational information sharing concerns managing the flows of materials, components, and finished goods in a way to optimize production-related activities across the supply chain. For example, production and delivery information can be shared to enhance operational efficiencies through improved coordination of allocated resources, activities, and roles across the supply chain (Narasimhan and Nair, 2005). Tactical information sharing allows partners to collaboratively manage the flow of decision-based activities in a way to improve decision quality (Lee et al., 2000; Lee and Whang, 2000). For example, the information for market based activities provided by buyers can be shared by suppliers for improving the decisions of collaborative planning, forecasting, and replenishment (CPFR). The concept of CPFR aims to enhance supply chain integration by supporting and assisting joint practices. This allows for continuous updating of inventory and upcoming requirements, making the endto-end supply chain process more efficient. Information sharing between suppliers and buyers aids in planning and satisfying customer demands through an IT-supported mechanism. Efficiency is thus created through the decrease expenditures for merchandising, inventory, logistics, and transportation across all trading partners (Caridi et al., 2005; Pramatari, 2007). Finally, strategic information sharing occurs when information used by group members is in a strategic form for gaining competitive value and further creates the strategic impact of supply chain partnership as a whole on the industry-wide structure (Gunasekaran and Ngai, 2004). Next, recognizing the importance of coordinating flow activities in the supply chain to achieve maximal efficiency, a systematic approach, advocating collaborative behaviors between inter-firms, has emerged desperately. Specifically, there are a number of important reasons for the formation of inter-firm collaborations. These collaborations are formed for sharing the cost of large investment, pooling and spreading of risk, and being access to complementary resources (Horvath, 2001). Moreover, many studies have discussed various types of collaborative processes across supply chain partners, such as sequential and reciprocal interdependence (Barratt, 2004; Wagner et al., 2010). Barratt (2004) contended that supply chain collaboration is a significant determinant for satisfying customer demand and reducing cost structure. Other studies argued that the central principle in creating flexible supply chain is the essence of collaborative relationships, which can facilitate a mutual decision-making process directed toward achieving common goals across their partners (Smith et al., 2007; Zacharia et al., 2009; Ramanathan and Gunasekaran, 2012). In sum, information sharing and collaboration between partners mainly concern the degrees of communication, trust, and interdependence for their willingness to work together in a joint manner, which can result in more stable transactions and reduction of uncertainty in market (Kumar and van Dissel, 1996). Several studies also pointed out that social exchange beliefs, such as trust, commitment, and interdependence, are critical to successfully determine information sharing and collaboration in the supply chain (Myhr and Spekman, 2005; Sheu et al., 2006). 2.4. Supply chain performance In this study, we examine a focal firm's supply chain performance for the relationships with partners. Traditionally, most studies have assesses organizational performance based largely on financial

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indicators. These indicators are important to assess whether operational changes are improving the financial health of a company, but insufficient to measure supply chain performance. These indicators do not relate to important organizational strategies and non-financial performances, such as product quality and customer satisfacton (Lapide, 2000; Ranganathan et al., 2004). More specifically, several studies have proposed a classification for supply chain strategies with the nature of different products, such as efficient supply chains for functional products and responsive supply chains for innovative products (Fisher, 1997; Lee, 2002). This implies that product-related characteristics are crucial in determining the types of supply chain strategies either more efficient or more responsive, and accordingly, are considered as the potential measures of supply chain performance. Accordingly, many studies have suggested both financial and non-financial indicators to measure an organization's supply chain performance. Beamon (1999) proposed a supply chain measurement system that emphasizes three types of performance measures, resource, output, and flexibility. Resource is defined as efficient resource management in a system to meet system's objectives, such as manufacturing cost, inventory cost, and return on investment. Output is used to measure customer responsiveness, on-time delivery, and product quality. Flexibility can measure a system's ability to accommodate volume and schedule fluctuations from suppliers, manufacturers, and customers. Output and flexibility are non-financial measures while resource is a financial measure. Other studies argued similarly that dependability, flexibility, quality, and efficiency are the key indicators for measuring supply chain performance (Vickery et al., 2003; Angerhofer and Angelides, 2006). Dependability is the ability to meet delivery dates at promised prices. Flexibility refers to the ability to react to market changes, new product developments, and customer requirements. Quality determines how well products/services meet customer needs. Efficiency relates to the improvement of processes, such as lowering inventory levels, reducing manufacturing costs, and increasing production volumes. The first three indicators are more likely to be defined as non-financial indicators while efficiency is a financial indicator. Besides, Wu and Chang (2012) applied the BSC to define supply chain performance, which intends to link the performance framework to four BSC-based perspectives. It includes organization and human capital, supply chain improvement, customer relationship, and profitability and revenue. The first three perspectives are non-financial measures and the last perspective is a financial measure. In summary of the two performance systems, flexibility is commonly recognized as an independent non-financial measure. Next, output, as defined above, seems to cover both dependability and quality attribute as a non-financial measure. Finally, efficiency, as defined above, is similar to resource measure and is considered a financial measure. This study thus defines a focal firm's supply chain performance with finance and non-finance in a complementary manner. Further, supply chain performance was conceptualized as a formative construct with two indicators, financial and non-financial. Both indicators, in essence, seem to capture different aspects of supply chain performance. Supply chain performance can thus be defined as a composite of the two indicators for observing its variance (Jarvis et al., 2003). In contrast, reflective indicators, however, are interchangeable for sharing a common theme. 2.5. Hypotheses development Trust can be defined as a firm's expectation that their partners will perform a particular action to benefit their interests irrespective of their ability to monitor or control their partners (Mayer et al., 1995). Working partners in a high trust relationship are not hesitant to share all information and believe in the information they receive, thereby generating greater willingness to take action

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in information sharing for each other's contribution to the relationship (Beccerra and Gupta, 1999; Kwon and Suh, 2005). Therefore, trust indicates a critical role in determining information sharing in a supply chain (Lee and Whang, 2000). Next, many studies argued that collaborative relationships rely on relational forms of exchange characterized by a high level of trust (Kumar and van Dissel, 1996; Vachon and Klassen, 2008). Specifically, in a buyer–supplier relationship, a high level of trust would create the motivation to open communication and be willing to take risks between partner firms, (Corsten and Kumar, 2005; Kwon and Suh, 2005). Moreover, while there are no boundaries for the future transactions among inter-firm partners, a high level of trust is required for guaranteeing their participation. Accordingly, a good transaction climate with mutual trust between partners may play a critical role in facilitating collaboration in the supply chain (Patterson et al., 2003). Accordingly, two hypotheses are thus suggested between trust, and information sharing and collaboration. H1. Trust positively affects information sharing in the supply chain. H2. Trust positively affects collaboration in the supply chain. The concept of commitment is defined as “a partner's exchange belief that an ongoing relationship with another is so important as to warrant maximal efforts at maintaining it, that is, the committed party believes that the relationship endures indefinitely” (Morgan and Hunt, 1994). Based on the social exchange perspective, commitment entails stability and sacrifice, as it helps to establish social relationships and promote supportive behaviors between partner firms (Yang et al., 2008). Any future transaction among supply chain partners requires commitment by two parties in order to achieve their common supply chain goals (Kwon and Suh, 2005). Specifically, relational commitment can help partners to enhance their willingness in establishing partnership. While information sharing relates to the execution of operational, tactical, and strategic activities with partners, a firm with a commitment to partnership is more likely to be willing to share information with its members (Yang et al., 2008). Next, many studies argued that relational commitment can improve communications between participants and facilitate the coordination of buyer–supplier relationships (Hunt et al., 2002; Narayandas and Rangan, 2004). Moreover, a strong sense of commitment on buyer–supplier relationships ensures the actions of cooperation and collaboration in maintaining long-term relationships (Morgan and Hunt, 1994; Ring and van de Ven, 1994). Thus, two hypotheses are thus proposed between commitment, and information sharing and collaboration. H3. Commitment positively affects information sharing in the supply chain. H4. Commitment positively affects collaboration in the supply chain. SET contends that social relationships are formed and maintained because participants offer reciprocal benefits to one another over time (Lawler et al., 2000; Narasimhan et al., 2009). Specifically, motives of the reciprocity in the supply chain emphasize on the establishments of cooperation and collaboration among partners, that is, partners collaborate to purse common goals (Humphreys et al., 2001). According to Humphreys et al. (2001), reciprocity in the supply chain can facilitate information sharing between their partners. For the upstream side, reciprocity will facilitate building virtual business network and suppliers can access up-to-date information for better managing productionbased activities. Similarly for the downstream side, buyers can acquire better customer services, purchase products more easily,

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and deliver orders rapidly through accessing instant information. Accordingly, two hypotheses are thus proposed between reciprocity, and information sharing and collaboration. H5. Reciprocity positively affects information sharing in the supply chain. H6. Reciprocity positively affects collaboration in the supply chain. Power refers to the relative dependence between exchange members, where power gained by one member can influence the decisions and behaviors of other members (Gaski, 1984). Organizations usually exercise power or control over other organizations while their resources are the contingency of asymmetry in a partnership (Griffith et al., 2006). Thus, power is proposed for the parameter of exchange in the supply chain (Narasimhan et al., 2009). Moreover, when information integration dictates the work of group members in the supply chain, a more power member will influence the types of information shared and the recipients to which it is sent (Smith et al., 2007). When a member has more resource than its partners in the supply chain, it will exercise more power to force partners to share information in the execution of their task (Emerson, 1976). Next, a partner with more power in the supply chain may give other partners more pressure to use interorganizational systems to share different types of information online and as a result, can effectively facilitate the collaboration of supply chain activities (Hart and Saunders, 1997; Sawhney and Parikh, 2001). Thus, two hypotheses are thus defined between power, and information sharing and collaboration. H7. Power positively affects information sharing in the supply chain. H8. Power positively affects collaboration in the supply chain. Much research has attempted to identify prerequisites for collaborative relationships in terms of the need of information sharing and integration in the supply chain (Mentzer et al., 2000; Barratt and Oliveira 2001). Barratt (2004) argued information sharing is one of the critical determinants of collaborative culture as collaborative culture can effectively facilitate allied decisionmaking among organizations. Effective information sharing among partners can be an important driver of collaborative effort and improve supply chain performance (Prajogo and Olhager, 2012). More specifically, collaboration in the supply chain requires individual participants to adopt e-business networks or common IT architecture to share information (Horvath, 2001). Accordingly, one hypothesis is thus proposed. H9. Information sharing positively affects collaboration in the supply chain. Information sharing allows suppliers, manufacturer, and retailers to improve forecasts, synchronize production and delivery, coordinate inventory-related decisions, and develop a shared understanding of their performance impact (Lee and Whang, 2000; Simchi-Levi et al., 2000). Information sharing supports two levels of integration in improving supply chain activities, operational and strategic (Prajogo and Olhager, 2012). Operational level considers the improvements of supply chain activities, including inventory level, production and delivery schedule, utilization of capacity, order status, and sale data. Strategic level exceeds rudimentary supply chain activities, expanding to include the improvements of product, customer, supplier, and competition. Next, many studies contended that collaborations in the supply chain can effectively improve finance-based performance for participants such as cost and revenue, and non-finance-based performance such as customer service and commercializing to

market (Mentzer et al., 2000; Frohlich and Westbrook, 2001). Other studies also discussed inter-firm collaborations with the linkage to both financial and non-financial performance for partners (Kumar and van Dissel, 1996). Financial performance includes cost efficiency and return on investment, and nonfinancial performance may range a wide scope such as reduction in uncertainty through vertical integration, access to complementary resources, and risk avoidance from co-investment with partners. Consistent with these arguments, two hypotheses are thus proposed between information sharing and collaboration, and supply chain performance. H10. Information sharing positively affects supply chain performance. H11. Collaboration positively affects supply chain performance. 2.6. Control variables Firms in high dynamic industries, such as high-tech industries, have shorter product lifecycle where time-to-market is of crucial importance (Koufteros et al., 2007). These firms show higher revenue volatility and customer turnover while compared to those in low dynamic industries. SCM is indicated as an important weapon to improve flexibility, customer responsiveness, and time-to-market in the dynamic market (Tan et al., 2002). Some studies thus suggested that industry type is an important variable to control the achievement of supply chain practice (Banker et al., 2006). Larger firms are more likely to influence the implementation of supply chain than smaller firms because they possess the resources and capabilities necessary to execute complex processes across partners (Subramani, 2004; Wu and Chang, 2012). They should be incorporated in the relationship to control the performance.

3. Research design A survey study was conducted to collect empirical data. The design of the research is describe below. 3.1. Instrumentation The instrument includes a four-part questionnaire. The first part uses a nominal scale and the rest use 7-point Likert scale. 3.1.1. Basic information This part collects basic information about organizational characteristics including industry type, annual revenue, number of employees, and number of suppliers, as well as respondent characteristics including working experience, education level, gender, and position. 3.1.2. Trust, commitment, reciprocity, and power This part measures the four SET constructs. The measurement items for trust were adapted from the instrument developed by Doney and Canon (1997) and Zacharia et al. (2009), including four items. Commitment were adapted from the instrument developed by Morgan and Hunt (1994), containing three items. Reciprocity were adapted from the instrument developed by Griffith et al. (2006), containing four items. Power were adapted from the instrument developed by Nelson and Cooprider (1996), containing three items. 3.1.3. Information sharing and collaboration This part measures the extent to which firms share information and collaborate with trading partners. Information sharing defines

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the extent of operational, tactical, and strategic information shared by a focal firm and its supply chain partners. Measurement items were adapted from the instrument developed by Rai et al. (2006), including five items. Collaboration defines all partners in the supply chain that are actively working together as one toward common objectives. Measurement items were adapted from the instrument developed by Tan et al. (2002), including five items. 3.1.4. Supply chain performance This part uses financial and non-financial measures. Financial measures were adapted from the instrument developed by Vickery et al. (2003) and Li et al. (2006), including five items, return on investment and assets, sale revenue, market share, and cost structure. Non-financial measures encompass two major dimensions: flexibility and output. Their measurement items were adapted from the instrument defined by Beamon (1999), including a total of seven items. 3.1.5. Control variables Industry type was defined to include three types, high-tech manufacturing, traditional manufacturing, and service. Firm size was measured using the total number of employees in the firm. It consists of three firm sizes, large, medium, and small.

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provides information on non-response bias in the sample (Armstrong and Overton, 1977; Subramani, 2004). Accordingly, the early and the late sub-samples were identified as 128 and 49 respondents, respectively. The two groups were compared, using various organizational characteristics, for their correlations with t-test, including annual revenue and number of employees. All their correlations revealed no significant difference at the .05 level (t value ¼ .33, and .39). The results indicate no systematic non-response bias for the survey data. In addition, common method bias results from the fact that respondents provide the measures of explanatory and dependent variables by a common rater (Podsakoff et al., 2003). In this study, subjective measures were used for three sets of variables, SET, mediators, and performance. There is a risk for common method bias. Harman's single factor test is one of the most widely techniques to address the issue of common method variance (Podsakoff et al., 2003). We included all items from all of the constructs for a factor analysis to determine whether the majority of the variance can be accounted for by one general factor. The results reported different factors extracted from the survey data. No single factor accounts for the bulk of covariance, leading to the conclusion of the inexistence of common method bias.

3.2. Sample design This study primarily examines the effects of information sharing and collaboration in the supply chain for the focal firm's performance. The qualified firms require an emphasis on the investment of supply chain technologies and considerable experience in SCM practice. Thus, it is assumed that larger firms would be more likely to have these experiences. A sample frame was assembled from the 2009 list of manufacturing and service firms published by the Taiwan Stock Exchange Corporation, which contains 1000 manufacturing and 500 service firms. Further, 700 manufacturing and 300 service firms were randomly selected as the study sample from this source. The target respondents for these firms would be the top managers, including general managers, vice general managers, or logistics/purchase executives. These managers are more likely to familiarize collaboration issue and its performance impact. The names and addresses for the top managers have been made publicly on their web sites. A survey method was used for this study. This survey was conducted during the period of April–June in 2010. First, the questionnaire with a return envelope was mailed to one of the top managers for each firm, and accordingly, each firm only received one questionnaire. Further, in order to improve survey return, follow-up procedure was performed by mailing reminders for non-respondents after 2– 3 weeks. 3.3. Sample demographics Initially, pretest was examined for the scale. The scale was carefully examined by selected practitioners and academicians in this area, including translation, wording, structure, and content. Content validity of the scale should be acceptable. After the questionnaire was finalized, 1000 questionnaires were successfully sent out for the potential respondents. 201 questionnaires were responded. After invalid responses deleted, this resulted in a sample size of 177 for a response rate of 17.7%. Table 1 depicts the sample demographics. The seemingly low response rate raises concern about non-response bias. We tested the non-response bias for the response sample. Considering the late group of respondents as most likely to be similar to non-respondents, a comparison between the early and the late group of respondents

Table 1 Demographics. Characteristics

Frequency

Percent

Industry type High-tech Manufacturing Traditional Manufacturing Service

74 43 60

41.92 23.85 34.23

Annual revenue o 1000 M 1000  10,000 M 10,000  100,000 M 4 100,000 M

35 60 66 15

20.12 34.14 37.41 8.33

No. of employees o 1000 1000  5000 5000  10,000 4 10,000

102 43 16 16

57.34 24.62 9.02 9.02

No. of suppliers o 100 100 300 300  500 4 500

84 37 32 24

47.50 21.25 17.64 13.61

Working experience o 5 year 5  10 years 10  20 years 4 20 years

12 47 62 54

8.36 25.74 35.17 30.73

Education level High school College Graduate school

7 104 66

3.52 58.91 37.57

Position General Managers Vice General Manager Logistics Executives Financial Executives Others

31 47 45 12 42

17.80 26.65 25.32 6.53 23.70

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3.4. Scale validation

4. Hypotheses testing

PLS is a structural equation modeling technique employing a component-based approach for estimation purpose. PLS allows latent variables to be modeled as formative constructs and places minimal demands on sample size and residual distributions (Chin et al., 2003). Theoretically, the sample size for executing PLS requires 10 times the number of indicators associated with the most complex construct or the largest number of antecedent constructs linking to an endogenous construct. The performance variable was defined as a second-order factor with formative indicators. PLS was used for this analysis. Because PLS does not provide a significance test or interval estimation, a bootstrapping analysis was conducted with 1000 sub-samples to estimate path coefficients, statistical significance, and relevant parameters. The evaluation was performed by two steps. The first step assesses reliability and convergent validity, and the second step is for discriminant validity. First, reliability was assessed by the criterion, Cronbach's α larger than .7 (Chin, 1998). Convergent validity was assessed by three criteria: (1) item loading (λ) larger than .70 and statistical significance, (2) composite construct reliability larger than .80, and (3) average variance extracted (AVE) larger than .50 (Fornell and Larcker, 1981). Next, discriminant validity was assessed by the criterion, the square root of AVE for each construct larger than its correlations with all other constructs (Fornell and Larcker, 1981). As indicated in Table 2, standardized item loadings range from .71 to .86, composite construct reliabilities range from .82 to .92, and average variances extracted (AVE) range from .58 to .67. In Table 3, the square root of AVE for each construct is larger than its correlations with all other constructs. Thus, these results show a highly acceptable level of reliability, convergent and discriminant validity.

PLS was used to analyze this structural model. The evaluation was performed by three steps (Chin, 1998). First, we needed to estimate path coefficient and statistical significance for the influential paths. Next, coefficient of determination (R2) for endogenous variables was computed to assess their predicted power. Finally, it is necessary to examine the relative importance of the first-order indicators for the second-order constructs in terms of indicator weights (Chin, 1998). Fig. 2 presents the results of the structural model. In the information sharing, we found that trust, commitment, reciprocity, and power are reported as important antecedents (p o.01). Their path coefficients are .32, .30, .27, and .29 respectively. Hypothesis 1, 3, 5, and 7 are thus accepted. They jointly explain 41% of the variance in information sharing (R2 ¼ .31). In the collaboration, trust, commitment, and power were significant precursors (p o.05) while reciprocity was not. Their path coefficients are .21, .17, and .19, respectively. Hypothesis 2, 4, and 8 are thus accepted. Hypothesis 6 is not accepted with path coefficient, .10. Next, information sharing had a significant impact on collaboration with path coefficient, .38 (p o.01). Hypothesis 9 is thus accepted. The four antecedents and information sharing jointly explain 44% of the variance in collaboration (R2 ¼.44). Both information sharing and collaboration were important in determining supply chain performance (p o.05 and .01) with path coefficient, .20 and .40. Hypothesis 10 and 11 are thus accepted. They jointly explain 38% of the variance in supply chain performance (R2 ¼.38). While our model originally suggested that information sharing has both direct impact and indirect impact through the mediator of collaboration on supply chain performance, we can assure the argument by testing the original research model against a competing model with removing a path from information sharing to supply chain performance (Baron and Kenny, 1986). First, when we looked at the original research model, path coefficients for both information sharing and collaboration to supply chain performance are significant at different magnitude (p o.05 and .01). Second, the R2 for supply chain performance is .38 in the original research model as compared to .27 in the competing model. A procedure similar to stepwise linear regression was used to examine the difference in R2 between the two models (Rai et al., 2006). The results indicated a significant difference between them. This suggests a partial mediation effect of collaboration on supply chain performance while information sharing also has direct effect on the performance. Finally, for the control variables, industry type is positively correlated with supply chain performance. However, firm size does not report any correlation with the performance. In addition, we further presented the relative importance of the indicators in forming the latent variable. Both financial and non-financial performance are significant in forming supply chain performance (W¼.78 and .83 Weight score).

Table 2 Convergent validity. Construct

Items

Item loadings

Composite reliability

AVE

Cronbach's α

Trust Commitment Reciprocity Power Information sharing Collaboration Financial performance Non-financial performance

4 3 4 3 5 5 5 7

.73–.79 .77–.83 .71–.80 .80–.84 .78–.82 .72–.86 .75–.83 .74–.79

.82 .84 .84 .86 .92 .87 .85 .88

.58 .64 .57 .67 .63 .57 .65 .59

.81 .80 .80 .81 .93 .90 .85 .89

Table 3 Discriminant validity. Construct

TR

CO

TR CO RE PO IS CL FP NP

.76 .22 .20 .13 .40 .33 .08 .10

.80 .18 .13 .39 .28 .12 .10

RE

.76 .15 .41 .19 .11 .05

PO

.82 .38 .25 .08 .15

IS

.79 .43 .23 .26

CL

.76 .38 .45

FP

.80 .31

NP

.77

Diagonal value: Squared root of AVE, Non-diagonal value: Correlation. Trust (TR), Commitment (CO), Reciprocity (RE), Power (PO), Information sharing (IS), Collaboration (CL), Financial performance (FP), Non-financial performance (NP).

5. Findings and discussions The findings show an important link between information sharing and collaboration (β¼ .38, path coefficient) and further, it is much stronger than the link between information sharing and supply chain performance (β¼ .38 vs. 20). Alternatively, the link between information sharing and collaboration further imposes significant impact on supply chain performance (β¼.40). We can argue for a fact that collaboration plays an important mediating role in achieving supply chain performance through the antecedent of information sharing, although information sharing also has positive impact on supply chain performance. Comparatively, the

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Fig. 2. Results of path analysis. n: p o .05;

129

nn

: p o.01

0.20*

Information sharing

0.38**

Collaboration

0.40**

Supply chain performance

Fig. 3. Relationships of three key constructs

path for indicating the mediator of collaboration is the major contribution to supply chain performance. Information sharing by itself may not fully exert its influence on the achievement of supply chain performance in a direct manner. The mediating relationship structure among the three variables, information sharing, collaboration, and supply chain performance, are particularly highlighted in Fig. 3. One possible reason is that information sharing may be treated as a behavioral intention for partners and could have resulted in an actual behavior of collaboration. Information sharing, indeed, may play a key enabler in collaborative effort. In other words, when focal firms attempt to plan their implementation practices for enhancing collaborative behaviors, they should first prepare themselves for reaching consensus in nurturing IT capabilities for further information sharing. These include building basic IT infrastructure for communication purpose and various inter-firm application systems. Next, while information sharing is considered as a high-level concept of collaborative effort, they are both significant in influencing supply chain performance at different magnitudes. Partners in the supply chain need to share various types of information, including inventory, production, order, delivery, and demand forecast, and this will further facilitate collaborative behaviors in the execution of inter-firm process activities, such as market response, product design, and problem solving. As partners are more satisfied with information sharing and collaborative behaviors, they will effectively eliminate wastes (time and material) internally and externally and can focus on their core competencies. As a result, both financial and non-financial benefits are expected from both antecedents. Finally, the SET-based variables are all significant in determining information sharing while a partial set of these variables reports the importance in collaboration (non-significance for reciprocity). There is a pattern to show that these variables are more influential in determining information sharing than collaboration (p o.01 vs. .05). This may be explained by that information sharing may play a critical role in driving the supply-chain value creation process by a sequence of information sharing, collaboration, and supply chain performance, as indicated in Fig. 3. Specifically, among the SET-based antecedents, trust of

partners has stronger influence on information sharing while compared to other antecedents. This situation also holds similarly for collaboration. The reason may be noted below. In maintaining a buyer–supplier relationship, a high level of trust would be the initial belief of participants to be willing to take risk in building partnership relationship. Without building the initial belief for their partners, the other issues in social exchange, such as commitment, reciprocity, and power, and the necessary actions would not be possible. Therefore, a high level of trust is the basic fundamental to enable the building of a long-term collaborative strategy. Moreover, reciprocity has a significant effect on information sharing, but it does not on collaboration. This may be due to that supply chain relationship structures are mostly associated with small-to-medium sizes of the partner firms in Taiwan. When focal firms have built good communication mechanisms to be ready for sharing supply chain activities with their partners, however, these partners may not be capable or necessary for collaborating these important activities, such as development of new market, design of new products, and design of new processes. In addition, a few words about the control variables are in order. Industry type indicates an important correlation with supply chain performance, but firm size does not. Supply chain relationships are basically formed from the consideration of vertical integration across various types of industries. Externally based organizational attributes, such as industry type, are more important in controlling performance achievement than internally based organizational attributes, such as firm size.

6. Conclusions and suggestions Partially consistent with the fundamental propositions of SET in impacting an organization's supply chain performance, such as commitment and trust or reciprocity (justice) and power, the findings of this study are more comprehensive to target a wide range of important SET issues for their effect, such as trust, commitment, reciprocity, and power. Further, the findings are more concrete to provide deep insight for practitioners while considering the importance of the mediators’ role, such as

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information sharing and collaboration, in achieving supply chain performance. Without the mediating role of information sharing and collaboration, supply chain performance would not occur. In particular, collaborative effort plays the major mediating role in achieving supply chain performance through the enabler of information sharing and the initial drivers of SET-based issues. This is an important contribution in the theoretical sense. The findings also have important implications for practitioners. As the supply chain performance enabled process was empirically defined, companies are therefore able to design appropriate collaborative strategies for their supply chains. The strategy development may involve two major concerns, social and technological. The first is mainly related to a building of inter-organizational relationships supply chain partners in terms of being rooted in the SET-based issues. The SET-based issues are important to identify psychological states or beliefs of partners for assisting a building of alliance. Specifically, companies should initially attempt to create a favorable climate for supply chain members to be willing to integrate their resources through voluntary, informal, and reciprocal bonds. A further enhancement of relational commitment will develop a stable relationships for alliance that in turn, motivates partners to participate in collaborative behaviors. Collaborative behaviors need to be fostered over time from positive beliefs and behaviors of social exchange processes. Further, IT infrastructure is necessary for a physical connection between members to make information sharing feasible for various flow activities. The IT deployment needs to be importantly associated with inter-organizational process redesign across partners in order to effectively integrate the three complementary flows, material, information, and finance. IT infrastructure represents a lower-order capability that can be leveraged to develop a higher-order capability in terms of process integration. Managers should be carefully aware of the fact that without the assurance of a well-prepared social and technological concern for partners, supply chains would not be likely to be successfully realized for its performance. Finally, when we looked at the findings of supply chain performance in this study, non-financial performance indicates a stronger weight coefficient than financial performance in forming supply chain performance. Managers are able to be aware of the importance of achieving non-finance based performance, such as market change, product performance, and customer service. They may indicate a long-term healthy organizational environment for maintaining short-term profits in the future. As managers plan to design a performance evaluation system for supply chain partners, they should carefully take non-financial measures into consideration in a more complete and correct manner. Subsequent research could be based on this foundation and includes other important factors, e.g. information technology and environmental uncertainty. Moreover, since the study sample was selected from a combination of various industries, the conclusions are more general and comprehensive. Follow-up research could be targeted toward some particular industries, for instance, the high-tech electronics industry, to understand their differences and similarities. This would provide more insight into SCM implementation in these particular industries. Besides, the objective in this research is to understand the impact of information sharing and collaboration on an organization's supply chain performance. The balanced scorecard is considered an important performance evaluation system in an inclusion of both financial and nonfinancial indicators. Future research could try to use it in examining supply chain performance. Although this research has produced some useful results, a number of limitations may be inherent in it. First, the response rate was lower than desirable, despite the various efforts to

improve it. This may be due to a lack of relevant experiences on information sharing and collaboration in the supply chain. However, the response sample indicated no systematic nonresponse bias and was well representative of the study sample. Next, although top managers are the target respondents, however, approximately 23.7 percent of the respondents are staff members. Since top managers in the larger firms are usually busy, some questionnaires may be completed by their subordinates. In fact, staff members are those people who are physically responsible for the daily work. Additional benefits would be an increase in the diversity of data sources with multiple informants and therefore, an increase in the variances of the variables of interest.

Appendix A. Questionnaire Part 1: Basic information 1. 2. 3. 4. 5. 6. 7. 8.

Industry type Annual revenue (NT$ millions) Number of employees (persons) Number of suppliers Working experience (years) Education level Gender Position

Part 2: Trust, commitment, reciprocity, and power On a scale of 1 (strongly disagree) to 7 (strongly agree), indicate the degree of the following scale items: Trust 1. Supply chain partners keep promise made to us and protect our right. 2. Supply chain partners are always truthful and frank with us. 3. When making decisions, supply chain partners consider our welfare as well as their own. 4. Supply chain partners will help us as we have problems. Commitment 1. Supply chain partners are willing to make sacrifice to help us. 2. Supply chain partners are willing to continue the relationship with us. 3. Supply chain partners are willing to spend a higher amount of time and effort with us. Reciprocity 1. 2. 3. 4.

We have fair policies regarding those partners dealing with. We are equitable in treating those partners dealing with. Supply chain partners positively contribute to this relationship. Supply chain partners generally treat us fairly Power

1. Supply chain partners and we have the same power to influence each other in decision, R&D, sale, production, and distribution. 2. Supply chain partners have power to influence our decision, R&D, sale, production, and distribution. 3. We have power to influence our partners’ decision, R&D, sale, production, and distribution.

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Part 3: Information sharing and collaboration Information Sharing 1. 2. 3. 4. 5.

Inventory data are visible at all partners in the supply chain. Production and delivery data are shared across the supply chain. Actual sale data are visible at all partners in the supply chain. Demanding forecasts are shared across the supply chain. Performance metrics are shared across the supply chain. Collaboration

1. Supply chain partners set up a communication plan for action. 2. Supply chain partners collaborate in developing new market and customer response. 3. Supply chain partners collaborate in designing their processes or products. 4. Supply chain partners collaborate in implementing their operational activities. 5. Supply chain partners have frequent interaction while problems occur.

Part 4: Supply chain performance Finance measure 1. 2. 3. 4. 5.

Supply chain can help us improve return on investment. Supply chain can help us improve return on assets. Supply chain can help us improve sales growth. Supply chain can help us improve market share. Supply chain can help us improve production and inventory cost Non-finance measure

1. 2. 3. 4. 5.

Supply chain can help us react to customer requirements. Supply chain can help us react to market change. Supply chain can help us react to new product development. Supply chain can help us improve product performance. Supply chain can help us improve product conformance to design specifications. 6. Supply chain can help us improve product delivery on time. 7. Supply chain can help us improve customer service for product complaints.

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