Interorganizational information systems visibility and supply chain performance

International Journal of Information Management 34 (2014) 285–295

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Interorganizational information systems visibility and supply chain performance夽 Ho Lee, Moon Sun Kim ∗ , Kyung Kyu Kim ∗∗ Graduate School of Information, Yonsei University, Seoul, Republic of Korea

a r t i c l e

i n f o

Article history: Available online 15 November 2013 Keywords: Interorganizational systems (IOS) visibility Supply chain visibility Resource dependence theory Relational view Supply chain performance

a b s t r a c t Despite growing emphasis on the importance of supply chain visibility, few companies to date have fully benefited from the information resources of their supply chain partners. A review of existing literature about supply chain visibility reveals that there are two essential forces at work, namely (1) collaborative behavior – i.e., firms willing to share information with supply chain partners in order to leverage social capital, and (2) opportunistic behavior – i.e., firms wanting to maintain some degree of information asymmetry in order to manage the behaviors of their supply chain partners. In order to identify the antecedents of IOS visibility, our operational definition of supply chain visibility, the two theories – resource dependence theory (RDT) and relational view (RV) – are used to cobble together a set of variables in a framework to investigate their relationships to IOS visibility. The data used in this study was collected from 124 intermediate component manufacturers in three different manufacturing industries. The results show that IOS visibility positively influences overall supply chain performance, as measured by operational performance. Regarding the antecedents of IOS visibility, factors including asset specificity, interorganizational trust, complementary resources, and joint governance structures are significant, whereas environmental uncertainty and interdependence are not significant. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Information technology-enabled supply chain (SC) cooperation has changed the nature of competition in business from companyto-company to SC-to-SC (Barua, Konana, Whinston, & Yin, 2004; Ketikidis, Koh, Dimitriadis, Gunasekaran, & Kehajova, 2008; Pereira, 2009; Rai, Patnayakuni, & Seth, 2006). In SC-to-SC competition, SC visibility through interorganizational information systems (IOS) is an important determinant of SC competitiveness (Kim, Ryoo, & Jung, 2011). Barratt and Oke (2007) define SC visibility as “the extent to which actors within a supply chain have access to or share information which they consider as key or useful to their operations and which they consider will be of mutual benefit” (p. 1218). Acknowledging the central role of IOS in SC visibility, scholars in both supply chain management (SCM) and information systems (IS) literature have investigated inter-firm information sharing through IOS (Kim, Umanath, & Kim, 2006; Zhu, 2004). Differences in theoretical backgrounds, however, have often resulted

夽 This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2011-327-B00157). ∗ Corresponding author. ∗∗ Corresponding author. Tel.: +82 2 2123 4525. E-mail addresses: [email protected] (H. Lee), [email protected] (M.S. Kim), [email protected] (K.K. Kim). 0268-4012/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijinfomgt.2013.10.003

in conflicting findings and have left us with an incomplete understanding of SC visibility. For example, a case study by Barratt and Oke (2007) finds that “the outcome of information sharing is visibility, which then could lead to an improved operational performance of a supply chain” (p. 1230). Wei and Wang (2010) also assert that SC visibility for sensing has a direct impact on SC strategic performance. Further, other dimensions of SC visibility, such as dimensions for learning, coordinating, and integrating, are important for enhancing SC reconfigurability. On the other hand, a study by Holcomb, Ponomarov, and Manrodt (2011) finds mixed results between SC visibility and firm performance. Specifically, that study asserts that only a few visibility factors significantly affect the market share, return on assets, and competitive position of firms. Also Sezen (2008) does not find any significant relationships between SC information sharing and various dimensions of SC performance, including flexibility, output, and resource performance. A review of previous studies about SC visibility reveals that their theoretical backgrounds are rooted in one of two different theories about interorganizational relationships – namely, resource dependence theory (RDT) and relational view (RV). RV considers interorganizational relationships as a source of competitive advantage for supply chains. Resource complementarities among SC partners facilitate cooperation to obtain mutual benefits for both parties (Klein & Rai, 2009). In order to realize a synergistic combination of complementary resources, however, firms need to cooperate more fully, thus exposing the firms to the risks of

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opportunism (Madhok & Tallman, 1998). According to RDT, any sourcing of inputs from outside a firm makes the firm dependent on other firms for critical resources, thereby increasing the likelihood of a partner’s opportunistic behavior. These two theories about interorganizational relationships suggest that there are seemingly counteracting forces at work simultaneously in business environments. These forces are (1) collaborative behavior – i.e., firms willing to share information with supply chain partners in order to leverage social capital, and (2) opportunistic behavior – i.e., firms wanting to maintain some degree of information asymmetry in order to manage the behaviors of supply chain partners (Kim, Umanath, Kim, Ahrens, & Kim, 2012). Thus the objectives of this paper are to explore the antecedents of SC visibility, suggested by the theories of RDT and RV, along with the consequences of SC visibility. Even though the current literature is increasingly focused on SC visibility, the concept remains ill-defined and poorly understood (Barratt & Oke, 2007). Thus this study adopts IOS visibility as an operational definition of SC visibility, referring to the extent to which the information and knowledge of partner firms regarding SC cooperation is visible to the focal firm through IOS (Kim et al., 2011). We evaluate our research model with a sample of 124 manufacturers in three different industries, including electronics manufacturing, heavy shipbuilding, and automobile manufacturing. Specifically, the sample firms consist of first-tier manufacturers of intermediate components who purchase low-level parts from immediate suppliers and assemble the low-level parts into stable intermediate components. Effective manufacturing requires mutual adjustments and cooperation between the manufacturers, because modular components must be assembled following an integrated design. In this type of environment, both first-tier manufacturers and their SC partners benefit from IOS because of the considerable interdependence between the parties (Chow et al., 2008). Accordingly, SC relationships interconnected through IOS (i.e., the specific relationships comprising our sample) provide a good context within which to study IOS visibility. The following sections briefly discuss the conceptual background for IOS visibility and present our research framework and hypotheses. We subsequently describe the research methodology of our study. The paper concludes by discussing the contributions of this research.

2. Conceptual background 2.1. Theories about interorganizational relationships: relational view and resource dependence theory 2.1.1. Relational view (RV) Representing a theory of social capital at the interorganizational level, RV considers the dyad, or a set of two organizations, as the unit of analysis. RV defines idiosyncratic interorganizational linkages as a source of relational rents and competitive advantage (Dyer & Singh, 1998). This theory explains that “relational rents are possible when alliance partners combine, exchange, or invest in idiosyncratic assets, knowledge, and resources/capabilities, and/or they employ effective governance mechanisms that lower transaction costs or permit the realization of rents through the synergistic combination of assets, knowledge, or capabilities” (Dyer & Singh, 1998, p. 662). According to this view, SC partners are the means by which firms can acquire the complementary resources and capabilities that they may otherwise lack. However, the potential for relational rents can only be realized when the partners have systems and cultures that are sufficiently compatible to facilitate coordinated actions (Buono & Bowditch, 1989).

The concept of IOS refers to a network-based information system that transcends organizational boundaries (Bakos, 1991). IOS is an interorganizational resource that belongs to the relationship because the relationship pair should participate in the IOS by way of investment, knowledge sharing, and synergistic value creation. IOS may contribute to differential interorganizational performances in the following ways. As an interconnected asset between organizations, an IOS provides an electronic channel through which firms can instantly see the information of partner firms, without incurring significant costs for transactions. An IOS-based alliance can achieve relational rents by reducing communication errors, lowering total value chain costs, and fostering greater product differentiation (von Hippel, 1988). For example, a supply chain with superior information-sharing mechanisms among SC partners will be able to outperform competing supply chains with less effective information-sharing mechanisms. Existing RV-based studies identify antecedents for successful interorganizational relationships, including effective governance, complementary resources and capabilities, knowledge-sharing routines, and relation-specific assets (Dyer & Singh, 1998). To meet these criteria of antecedents, this study incorporates a joint governance structure (e.g., Klein & Rai, 2009) for effective governance, asset specificity and interorganizational trust (e.g., Dyer & Chu, 2003) to represent the dimensions of relation-specific assets, and complementary resources for resource complementarities. IOS visibility itself, a principal construct in this study, encompasses the criterion of knowledge-sharing routines. An interfirm knowledge sharing routine refers to “a regular pattern of interfirm interactions that permits the transfer, recombination, or creation of specialized knowledge” (Dyer & Singh, 1998, p. 665). Knowledge sharing is the process of “making knowledge available to others by constructing and providing technical and systematic infrastructure” (Nooshinfard & Nemati-Anarak, 2012, p. 2). Also Dixon (2000) describes knowledge sharing as a flow of knowledge from a party who possesses it to another party who needs it. In the meantime, IOS visibility represents the extent to which the information and knowledge of partner firms regarding SC cooperation is visible to the focal firm through IOS. Thus, IOS visibility is considered to reflect an important facet of interfirm knowledge sharing routines. 2.1.2. Resource dependence theory (RDT) RDT addresses how the external resources of organizations influence the behavior of the organization. Organizations may not be entirely self-reliant when it comes to all of the resources required for effective functioning (Reid, Bussiere, & Greenaway, 2001). Thus organizational survival depends on the ability of a firm to procure critical resources from the external environment, which tends to introduce uncertainty into the decision-making processes of the firm. To reduce uncertainty regarding the flow of required resources, organizations generally try to restructure their dependencies using a variety of tactics. The most prominent of these tactics is “constraint absorption” (Casciaro & Piskorski, 2005), and one way whereby organizations can partially absorb constraints is through formal long-term contracts such as IOS (Pfeffer & Salancik, 1978). Indeed, the mutual benefits of IOS enable SC participants to move toward more collaborative long-term economic relationships (Klein & Rai, 2009). Specifically, SC relationships are important for any organization that outsources portions of component manufacturing. In the automobile industry, for example, a number of complex production activities are outsourced to smaller firms for component manufacturing (Kim et al., 2012). Automobile component manufacturers attempt to manage uncertainty about the ongoing acquisition of requisite parts by implementing IOS with their suppliers. Existing RDT-based studies identify antecedents for successful interorganizational relationships, including the environmental uncertainties

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faced by SC partners (e.g., Wang, Tai, & Wei, 2006), the dependence of firms on partners (e.g., Kumar, Scheer, & Steenkamp, 1995), and joint governance structures among partners. 3. Research model and hypothesis development Antecedents for IOS visibility are identified from relationship characteristics according to the two different theories, RV and RDT. The relationship between IOS visibility and SC performance is further discussed below. 3.1. Asset specificity and IOS visibility Inter-firm asset specificity refers to the extent to which the assets are specialized in conjunction with the assets of an alliance partner (Dyer & Singh, 1998). Asset specialization is “a necessary condition for rent” and “strategic assets by their very nature are specialized” (Amit & Schoemaker, 1993, p. 39). Furthermore, interfirm specific assets are co-specialized in that the value of one’s asset is significantly decreased without the other (Clemons & Row, 1991). Highly specific assets to an inter-firm relationship are the strategic core of the alliance, which justify the existence of the relationship (Reve, 1990). When inter-firm asset specificity is high, participating firms would be cooperative, making their internal information visible to their partners. When a firm has access to the required information from their partners, productivity gains for the relationship can be realized. As a result, the entire value chain achieves competitive advantages over competing value chains. The concept of inter-firm asset specificity refers to the significance of a firm’s alliance partner for the future strategic development of the firm (Lunnan & Haugland, 2008), due to the very nature of co-specialization. Thus, the partners are willing to take adventurous actions such as establishing IOS and sharing strategic information. Other things being equal, the higher the inter-firm asset specificity between two organizations, the higher their incentives to exchange or share their important information resources through IOS. Hence, the following hypothesis: H1.

Asset specificity positively influences IOS visibility.

3.2. Interorganizational trust and IOS visibility Social capital theory asserts that networks of relationships constitute an important asset for the conduct of boundary spanning activities and interorganizational trust is a key facet of relationship capital (Nahapiet & Ghoshal, 1998). Adler (2001) identifies three sources of interorganizational trust, i.e., familiarity through repeated interaction, calculation based on interests, and norms that create predictability and trustworthiness. Interorganizational trust is relation-specific because it represents “the extent of trust placed in the partner organization by the members of a focal organization” (Zaheer, McEvily, & Perrone, 1998, p. 142). It is inherently relational because it resides in interorganizational relationships and has little value outside the relationship. Further, interorganizational trust is a relationship asset because it reduces costs of negotiation and interorganizational conflict, leading to effective performance of an exchange relationship (Zaheer et al., 1998). Interorganizational trust arises from a belief in the good intentions and concerns of the exchange partners (Nahapiet & Ghoshal, 1998) and stimulates perceived fairness in a relationship (Levin & Cross, 2004). When a firm believes in the integrity and benevolence of its partners, the firm is more willing to make efforts at collaborative behavior in the form of information exchange with SC partners. Interorganizational trust also reduces concerns about releasing internal information to trustworthy partners through IOS, and encourages SC partners to implement exchanges of information

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that would otherwise be considered risky (Putnam, 1993). Thus interorganizational trust may increase IOS visibility between SC participants. Hence, we propose the following hypothesis: H2.

Interorganizational trust positively influences IOS visibility.

3.3. Complementary resources and IOS visibility Firms enter into strategic alliances in order to achieve access to the complementary resource endowments of the partner. Resource complementarities represent an enhancement of resource value, and arise when a given resource produces greater returns in the presence of another resource than by itself (Milgrom, Qian, & Roberts, 1991). Mutual gains are possible in a supply chain partnership when the individual strengths of partners complement each other. For instance, manufacturers have expert knowledge about the manufacture of their products, while suppliers have expert knowledge about the characteristics of their raw materials. When SC participants consider the resource bases of partners to be complementary, their common interests may motivate them to exchange important information through IOS to enable mutual gains by way of each other’s resources. Because certain resources, such as strategic information about market demand, are neither tradable nor imitable through external market mechanisms, information sharing through IOS becomes an important source of an alliance’s competitive advantage (Dierickx & Cool, 1989; Kogut & Zander, 1992; Tsai, 2000). With up-to-the-minute access to a partner’s complementary knowledge through IOS, SC partners can realize mutual gains in the form of enhanced cost-effectiveness and adaptability. The greater the mutual gains from combining complementary resources, the higher their incentives to exchange or share their important information resources through IOS. Hence, the following hypothesis: H3.

Complementary resources positively influence IOS visibility.

3.4. Joint governance structures and IOS visibility Both RV and RDT theorists have focused a lot of attention on the structural properties of interorganizational relationships. RDT suggests that firms seek to reduce uncertainties and to manage dependence by structuring their exchange relationships through various governance mechanisms (Reid et al., 2001). Reflecting on the specific nature of IOS, i.e., long-term relation-specific investments in a strategic alliance, we focus on joint governance structures. Joint governance structures refer to the structures, processes and associated arrangements that IOS management must have in place to fully account for the management of systems and the services delivered. An appropriate joint governance structure can regulate opportunistic behavior by SC partners because the structure allows monitoring of any party’s improper behavior. With the appropriate IOS governance structure in place, investments into relationship-specific capital can be safeguarded, resulting in more collaborative behavior in terms of information exchange. Without the appropriate governance structure, an organization’s opportunistic behavior may cause other partners to behave opportunistically as well (Park & Ungson, 2001), thereby restraining the flow of information among SC partners. Therefore, we present the following hypothesis: H4. Joint governance structure positively influences IOS visibility. 3.5. Environmental uncertainty and IOS visibility Environmental uncertainty refers to a lack of information about environmental factors that affect decision making (Kim, Park, Ryoo, & Park, 2010). The primary environmental uncertainties that SC

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participants face are demand volatility and industry clockspeed (Wang et al., 2006). Uncertainties resulting from high demand volatility and fast clockspeed pose problems for SC participants, including too much or too little inventory and cost issues. Also, a SC partner experiences uncertainty when relevant information is missing or when logistical contingencies are too many to be specified (Ryu & Eyuboglu, 2007). When faced with uncertainties, it is difficult for a manufacturer to make informed judgments about the actual status of the environment (Frazier & Antia, 1995). Organizational information processing theory suggests that environmental uncertainties significantly influence organizational processes, and may result in high information processing needs among firms (Tushman & Nadler, 1978). Highly uncertain environments promote conditions that facilitate cooperation between SC partners for the seamless exchange of information. The need for additional information can be fulfilled by readily reachable SC partners, as long as the sources are trustworthy and in possession of relevant information. IOS has the ability to generate and collect ontime information for environmental monitoring that can be shared among SC partners. Thus environmental uncertainty will lead to increases in IOS visibility. H5. Environmental uncertainty positively influences IOS visibility. 3.6. Interdependence and IOS visibility Interdependence between SC partners refers to the extent to which firms are mutually dependent on each other to achieve their respective goals. Thompson (1967) identifies three levels of increasing interdependence: pooled, sequential, and reciprocal. As interdependence among SC partners increases, the partners are more likely to be committed to the partnership and are less likely to behave opportunistically (Gulati & Sytch, 2007; Kumar et al., 1995). High levels of interdependence also signify that each party needs a lot of information from the other party to fulfill its own tasks and not to cause any disruptions in upstream and downstream activities. Thus interdependent SC partners are more likely to maintain a close relationship to reinforce the larger interests at play (Lusch & Brown, 1996). In other words, interdependent SC partners are more likely to develop a norm of information sharing through IOS. Hence, we propose the following hypothesis: H6.

Interdependence positively influences IOS visibility.

3.7. IOS visibility and SC performance There is an extensive body of research on the impact of SC integration on performance (e.g., Devaraj, Krajewski, & Wei, 2007; Flynn, Huo, & Zhao, 2010; Germain & Iyer, 2006). Supply chain integration refers to the extent to which “a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organization processes,” (Flynn et al., 2010, p. 59). Firms can collaborate with their SC partners in many different ways and thus there can be various dimensions of SC integration. Examples of the SC integration dimensions include internal vs. external integration, customer vs. supplier integration, process vs. relationship integration, etc. These various dimensions of SC integration have caused the difficulties for a cross comparison of findings about the relationship between SC integration and performance. Thus, more focused approaches are clearly called for. This study adopts IOS visibility as a dimension of SC integration which represents an external integration between manufacturers and their suppliers. SC partners with highly visible IOS have on-time access to the information required for decision making. When requisite information to cope with environmental changes is readily visible to SC partners, the entire supply chain can adapt effectively to a changing environment.

Conversely, the relative withholding of information by a SC partner may work to the disadvantage of the entire supply chain. For example, the “bullwhip effect”1 is a core problem in supply chain management because it distorts information about demand, which is transmitted upstream in a supply chain (e.g., Lee, Padmanabhan, & Whang, 1997). The bullwhip effect supposedly happens when a supplier’s demand forecast is made based on the order history of its immediate downstream partner (manufacturer), without knowing sales information from the ultimate customers (Kim et al., 2012). One way of mitigating the bullwhip effect is to share sales information with upstream SC partners through IOS. When IOS visibility is high, the relevant information flows seamlessly to upstream partners, and all members of the supply chain can synchronize their operations. This, in turn, allows SC participants to reduce overall SC inventory and costly duplicate practices, including forecasting by multiple participants. Consequently, SC performance is increased. Thus we propose the following hypothesis: H7. IOS visibility positively influences supply chain performance. For our empirical investigation, we propose the following research model (Fig. 1). 4. Research methods 4.1. Sample and data collection The data required for this study was collected from manufacturers in three different industries, including electronics manufacturing, automobile manufacturing, and heavy shipbuilding. In order to identify potential participants, we searched a major financial database of Korean companies, using criteria that included (1) the industry code, (2) size, and (3) the approach to production (e.g., assemble-to-order or make-to-stock). Among the identified list of companies, we contacted the ones with whom we were able to establish connections through various ways (e.g., alumni connections, industry association members, etc.). As a result, we identified 195 manufacturers as potential participants in our sample. A questionnaire was administered to respondents via faceto-face meetings, phone calls, and email correspondence at the convenience of respondents. Follow-up contact via phone calls and email was made five and ten days after initial contact with respondents. Respondents participating via email were asked to send completed questionnaires back to the primary author. Out of 195 buyers contacted, final data was collected from 124 buyers. Table 1 shows a profile of the respondents. To evaluate any systematic differences for non-responses, ANOVAs for comparing mean differences between early and late responses were performed for all the research variables. Also a t-test was performed for annual sales between the respondents and non-respondents. No statistically significant differences were found among the companies at a 0.05 level of significance. 4.2. Measures All of our research variables were measured with multi-item, seven-point Likert scales, using instruments from existing literature. Supply chain performance is operationalized as operational benefits from enhanced IOS visibility using items developed by Subramani (2004). The benefits attributable to participation in the IOS relationship include economic outcomes such as cost efficiencies. Also, the expectation of better profits is considered a major

1 The bullwhip effect occurs when “the variance of orders may be larger than that of sales and the distortion tends to increase as one moves upstream” (Lee et al., 1997, p. 546).

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Fig. 1. The research model.

motive for a long-term relationship such as IOS (Jap & Anderson, 2003). IOS visibility refers to the extent to which the information of SC partners required for effective SC cooperation is visible to the focal firm through IOS (Kim et al., 2011). Considering the boundaryspanning roles of purchasing, a focal firm needs information from its partner in multiple areas, including order completion status, backorder status, production schedules, current production capacity, and demand planning information (Lee & Whang, 2000). In our sample, purchasing managers were asked to respond to the questionnaire. In instances when purchasing managers did not have enough knowledge to answer a question, they were encouraged to contact appropriate experts within the firm. Inter-firm asset specificity refers to relationship-specific assets that are not transferable to alternative uses (Williamson, 1985).

Table 1 Respondent characteristics. Variables

Category

(%)

Primary industry

Automotive Electronics Shipbuilding

33.9 32.3 33.9

Respondent’s years of work experience

1–4 years 5–8 years 9–12 years Over 13 years

46.8 27.4 11.3 14.5

Respondent’s position

Executive level Director/General Manager Manager Sub-manager Other position

2.4 10.5 29.0 37.9 20.2

Firm’s annual sales

Less than US $10 million US $10–50 million US $51–100 million Over US $100 million

23.9 29.5 13.6 33.0

Since highly specific assets are co-specialized with the assets of a SC partner, it would be difficult to find an alternative source of supply and thus switching costs would be high (Clemons & Row, 1991). Asset specificity was measured with three items, i.e., substitutability, total switching costs, costs of losing a partner, proposed by Poppo, Zhou, and Ryu (2008). Interorganizational trust was measured on the basis of two dimensions proposed by Bensaou and Venkatraman (1995). The dimensions are (1) the degree of mutual trust between a pair of firms, and (2) the degree of comfort in sharing sensitive information with suppliers. Five items were adopted from the work of Poppo et al. (2008). A joint governance structure refers to the structures, processes, and associated arrangements that IOS management must have in place to fully account for the use of resources, the management of systems, and the services delivered between partners. For analysis of the joint governance structure, eight items were adopted from Luo (2008). Complementary resources refer to the degree of complementarity between the resources each participant contributes to the SC relationship (Lunnan & Haugland, 2008). Three items were adopted from Lunnan and Haugland (2008) to measure complementary resources. They are differences, similarities, and criticality of the partner resources. Inter-firm dependence refers to the extent to which one party is dependent on the other. Three items were adopted from the work of Morgan, Kaleka, and Gooner (2007) to measure inter-firm dependence. Environmental uncertainty refers to (1) the degree of change that is unpredictable in the external environment (Huber & Daft, 1987) and (2) the lack of information about environmental factors that affect decision making. Four items were adopted from the work of Poppo et al. (2008) to measure environmental uncertainty. 5. Analysis and results Statistical analyses were performed using the statistical software Partial Least Squares (PLS) Graph, version 3.00, for the

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Table 2 Confirmatory factor analysis.

AS 1 AS 2 AS 3 IOT 1 IOT 2 IOT 3 IOT 4 IOT 5 CR 1 CR 2 CR 3 JG 1 JG 2 JG 3 JG 4 JG 5 JG 6 JG 7 JG 8 EU 1 EU 2 EU 3 EU 4 ID 1 ID 2 ID 3 IOSV 1 IOSV 2 IOSV 3 IOSV 4 IOSV 5 SCP 1 SCP 2 SCP 3

Asset specificity

Inter-organizational trust

Complementary resources

Joint governance structure

Environmental uncertainty

Interdependence

0.76 0.87 0.84 0.22 −0.02 −0.13 −0.11 −0.16 0.06 −0.05 0.24 −0.09 −0.15 −0.07 0.06 −0.03 0.14 0.10 0.07 0.05 0.04 0.11 0.12 0.25 0.36 0.19 −0.01 0.14 0.09 0.05 0.15 −0.04 0.09 0.05

−0.14 0.00 0.00 0.69 0.80 0.76 0.74 0.64 0.21 0.35 0.28 0.20 0.06 −0.03 0.00 0.18 0.36 0.44 0.48 −0.05 0.04 −0.25 −0.10 0.07 0.07 0.14 0.17 0.11 0.08 0.18 0.14 0.11 −0.04 0.11

0.09 0.03 0.03 0.26 0.20 0.28 0.22 −0.01 0.81 0.72 0.60 0.13 0.21 0.24 0.18 0.08 −0.09 0.00 −0.01 −0.03 −0.05 −0.04 0.01 −0.03 0.09 0.07 0.01 0.05 0.06 −0.02 −0.02 0.05 −0.05 0.00

−0.09 −0.01 0.04 0.15 0.20 0.09 0.15 0.24 0.17 0.17 0.27 0.80 0.79 0.81 0.84 0.81 0.74 0.66 0.63 −0.02 −0.03 0.04 −0.06 0.14 0.14 0.11 0.27 0.31 0.21 0.24 0.19 0.01 0.10 0.02

0.10 0.07 0.17 0.04 −0.10 −0.12 −0.18 −0.16 −0.08 0.05 −0.08 −0.09 −0.10 −0.05 0.01 0.03 0.03 0.03 0.01 0.84 0.78 0.80 0.73 0.17 −0.01 0.08 −0.02 0.02 −0.02 −0.01 −0.08 −0.03 −0.01 −0.06

0.22 0.17 0.28 −0.02 0.04 0.09 0.12 0.24 0.08 −0.08 0.13 0.21 0.24 0.12 0.12 0.00 0.04 −0.14 −0.10 0.23 0.12 0.07 −0.20 0.81 0.72 0.81 0.19 0.07 −0.03 0.10 −0.02 0.07 0.03 0.08

IOS visibility 0.13 0.12 0.08 0.06 0.18 0.18 0.26 0.00 −0.03 0.12 −0.05 0.20 0.21 0.26 0.08 0.23 0.19 0.30 0.23 −0.01 0.03 −0.05 −0.07 0.06 0.06 0.11 0.82 0.80 0.90 0.87 0.87 0.14 0.16 0.23

Supply chain performance 0.01 0.03 0.06 0.22 −0.03 0.03 −0.11 0.15 0.00 −0.08 0.09 −0.01 0.01 −0.02 −0.08 0.10 0.17 0.07 0.17 0.03 −0.12 −0.01 0.01 −0.01 0.10 0.12 0.12 0.16 0.14 0.08 0.19 0.90 0.87 0.89

The bold values are represented for factor loadings which are greater than 0.50.

following reasons. PLS (1) is the most suitable software during the early stages of theory development and (2) enables the modeling of latent variables, even for small-to-medium-sized samples. First, IOS visibility is a new construct under investigation in SC research. Second, because the unit of analysis occurs at the organizational level, the sample size of 124 firms is not large, despite being sufficient to test our research model. On the other hand, PLS analysis can support both exploratory and confirmatory research, along with being relatively robust against deviations from a multivariate distribution (Gefen, Straub, & Boudreau, 2000).

5.1. Measurement model The measurement model was examined with the criteria of individual item reliability, internal consistency, convergent validity, and discriminant validity. Internal consistency and convergent validity were evaluated by examining item-construct-loading, composite reliability, and average variance extracted (AVE). As shown in Table 2, the results of factor loading are within an acceptable range of above 0.6, and the t-values show that all measures are significant at the 0.01 level. Discriminant validity was evaluated by examining the extent to which each measure loads more highly on their intended construct than on other constructs. The construct level correlation matrix is provided in Table 3. The extent to which the square root of AVE is larger than inter-construct correlations was evaluated for discriminant validity. The results show that the reliability coefficients are all above 0.70 and each AVE is above 0.50 (see Table 3), which are the respective threshold values, as frequently cited. The confirmatory factor analysis (CFA) and the

correlation matrix results provide sufficient evidence for discriminant validity. 5.2. Structural model The structural model was evaluated by examining the explained variances (R2 ) for the research model and the path coefficients of the independent variables. The PLS analysis results are presented in Fig. 2. Our research model accounts for 36% of the variances in IOS visibility and 13% of the variances in SC performance. The results of PLS analyses (Table 4) show that all the hypotheses are supported, except for H5 and H6. As expected, asset specificity (H1: t = 2.36, p = 0.0099), interorganizational trust (H2, t = 2.25, p = 0.0131), complementary resources (H3: t = 2.01, p = 0.0232), and joint governance structure (H4, t = 6.511, p = 0.0000) show significant relationships with IOS visibility. Also, IOS visibility shows a significant positive relationship with supply chain performance (H7, t = 4.28, p = 0.0000). On the other hand, neither environmental uncertainty (H5, t = −0.53, p = 0.2993), nor interdependence (H6, t = 0.02, p = 0.493) shows a significant relationship with IOS visibility. Finally, common method bias was examined with both Harman’s one-factor test (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) and inter-construct correlations (Pavlou & El Sawy, 2006). No single factor accounted for a large proportion of the variance in the factor analysis (eight factors were extracted, and the first factor explained 15.6% of the variance). Further, Table 3 shows that there were not any unusually high correlations (the highest correlation between principal constructs is r = 0.56).

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Table 3 Correlation matrix and average variance extracted (AVE). Constructs

1

2

3

4

5

6

7

8

1. Asset specificity 2. Interorganizational trust 3. Complementary resources 4. Joint governance structure 5. Environmental uncertainty 6. Interdependence 7. IOS visibility 8. Supply chain performance

0.89a −0.02 0.11 0.03 0.21 0.49 0.20 0.12

0.82a 0.56 0.50 −0.27 0.21 0.37 0.17

0.81a 0.43 −0.12 0.19 0.19 0.03

0.82a −0.11 0.27 0.54 0.19

0.76a 0.06 −0.10 −0.07

0.87a 0.24 0.17

0.92a 0.35

0.92a

0.79 0.92 3.90 1.57 3

0.66 0.91 4.94 0.90 5

0.65 0.85 4.78 1.11 3

0.68 0.94 4.05 1.19 8

0.58 0.84 3.61 1.26 4

0.76 0.91 4.28 1.30 3

0.84 0.96 3.51 1.63 5

0.84 0.94 4.42 1.09 3

AVE Composite reliability Mean Std. Dev. Number of measurement items a

Figures along diagonal in bold are values of the squared root of the AVE.

Fig. 2. The results of Partial Least Squares.

Table 4 Results of PLS analysis. Hypothesis Theoretical paths H1 Asset specificity → IOS visibility H2 Interorganizational trust → IOS visibility H3 Complementary resources → IOS visibility H4 Joint governance structure → IOS visibility H5 Environmental uncertainty → IOS visibility H6 Interdependence → IOS visibility H7 IOS visibility → supply chain performance Note: df = 123. * p < 0.05 in one-tailed test. ** p < 0.01 in one-tailed test. *** p < 0.001 in one-tailed test.

Path coefficient

t-Value

p-Value

Outcome

0.214 0.196 0.169 0.505 −0.058 0.002 0.354

2.361** 2.250* 2.012* 6.511*** 0.528 0.019 4.286***

0.010 0.013 0.023 0.000 0.299 0.493 0.000

Supported Supported Supported Supported Not supported Not supported Supported

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6. Discussion and conclusions 6.1. Contributions to theory This study attempts to make several important contributions to existing SCM and IS literature. It has been touted by academic scholars that close cooperation among SC partners can enhance the performance of an entire supply chain. For example, if the ultimate demand information from a retailer is available to all relevant parties in the supply chain (e.g., wholesaler, 3rd party logistics, manufacturer, raw material supplier), the SC participants may adapt their operations accordingly and thereby the efficiency and effectiveness of the entire SC can be enhanced. However, close cooperation such as sharing internal information with SC partners necessarily exposes the focal firm to strategic and operational risks. Thus, firms are cautious about releasing their internal information to others and therefore it is difficult to see a fully collaborative end-to-end SC in current practice (Bowersox, Closs, & Cooper, 2010). This research addresses the issue of counteracting forces at work simultaneously with regard to interorganizational information sharing. When we consider these counteracting forces concurrently in the same research model, the results show that SC partners weigh more on the potential benefits from close cooperation than on the potential risks, although they are cautious about negative consequences resulted from internal information sharing. Further, this study employs a novel construct of IOS visibility as an operational definition of SC visibility. Although SC visibility is garnering an increasing amount of attention in the relevant literature, it still remains an underexplored facet of SCM (Kim et al., 2011). Further, considering that the exchange of up-to-the-minute information through means other than IOS is not feasible to achieve SC visibility in practice, IOS visibility is the appropriate level of specificity for our research. The results of studying the antecedents and consequences of IOS visibility have the following theoretical implications. Our results show that IOS visibility leads to higher SC performance. Advances in information systems technology enable SC partners to work in close coordination and optimize performance across the entire supply chain. Underlying this argument is an assumption that relevant information should be visible to both SC participants. This study confirms that IOS visibility indeed leads to improved SC performance. This study also identifies antecedents of IOS visibility based on two different theories of interorganizational relationships, which include resource dependence theory and relational view. The theory of RV values interorganizational relationships as a source of competitive advantage, while RDT weighs the risks that may arise from interorganizational dependences. Our results show that in an upstream SC relationship, RV variables are important predictors of IOS visibility, while RDT variables, with the exception of joint governance structures, are not. That is, in the context of IOS, firms approach any SC partners with strong intentions to leverage relationship capital. Meanwhile, the concerns of firms about potential risks arising from dependent relationships are partially mitigated when an appropriate joint governance structure is in place. This makes sense considering the strategic nature of IOS in an upstream SC partnership, which works as a channel for the ongoing exchange of information. Contrary to our expectations, environmental uncertainty and interdependence did not turn out to be significant predictors of IOS visibility. Regarding environmental uncertainty, existing literature presents two opposing views about the effects of environmental uncertainty on interorganizational relationships such as IOS (Srinivasan, Mukherjee, & Gaur, 2011). One view posits that SC participants facing high levels of uncertainty will courageously exchange information in an attempt to reduce that uncertainty

(Pfeffer & Salancik, 1978). The other view asserts that, in order to maximize their flexibility in uncertain environments, firms attempt to minimize their reliance on SC partners. One main example is for firms not to actively engage in information exchange through IOS (Heide & Miner, 1992). We suspect that both of these opposing forces surrounding environmental uncertainty are at work, which may be a cause of the insignificant relationship of environmental uncertainty with IOS visibility. Further research is needed to clarify the relationship. Regarding interorganizational dependence, a plausible explanation of the insignificant relationship with IOS visibility is predicated on the substitution relationship between asset specificity and interorganizational dependence. When two variables are in a substitution relationship, the marginal benefit of each variable decreases as the level of the other variables increases (Siggelkow, 2002). Given the high correlation between asset specificity and interorganizational dependence, together with the significantly positive effect of asset specificity on IOS visibility, a substitution effect in which the marginal effect of interdependence decreases when asset specificity is high should, in theory, cause an insignificant effect of interdependence. To verify this inference, we examined a restricted regression model without asset specificity [IOS visibility = f(interorganizational dependence)] and found a significant positive effect (t = 2.721, p = 0.007). While helpful, these post hoc explanations are clearly speculative and should be subject to further empirical investigation. 6.2. Practical implications This study offers some implications for supply chain practitioners. Our results confirm that IOS visibility is positively associated with SC performance. Thus, firms should approach IOS visibility in a positive way to maximize the returns from IOS, while enforcing appropriate mechanisms to control a partner’s potential opportunistic behavior. SC partners can use two different types of control mechanisms to protect against opportunism. They include informal safeguards such as interorganizational trust and formal safeguards such as joint governance structures (e.g., established procedures to resolve conflicts). Further, when informal safeguards are used together with formal safeguards, informal safeguards are the most effective and least costly means of protecting inter-firm investments. Opportunistic behavior by either party may jeopardize a difficult-to-replace relationship. Further, our results show that when considering IOS visibility, firms’ orientation toward cooperative relationships outweighs the concerns about the negative consequences from the dependent relationships. It would be useful to understand the partner’s cooperative orientation when the focal firm negotiates with their partners about the IOS-related issues. For example, there are many important decisions about IOS implementation (e.g., IS compatibilities, network protocols, securities, etc.) that affect both parties in terms of costs and strategies. If both parties are confident about the bottom-line attitude of the partner, then negotiation can be much smoother and chances of negotiation failure would be much less. 6.3. Limitations The limitations of this study should be mentioned before concluding. The first limitation of this research is the measurement of IOS visibility. Our measurement of IOS visibility focuses mainly on the visibility of transactional information, such as inventory status and available production capacity. Supply chain partners exchange information/knowledge for various purposes beyond transactional information. Considering the importance of transactional information in interorganizational relationships, however, this limitation does not pose a serious threat to the validity of our IOS

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visibility measurement. Nonetheless, future research may include the measurement of other types of information/knowledge exchanged between SC partners. The second limitation is related to the selection of research variables included in this study. The research variables are chosen from the two theories, RV and RDT, based on their relevance to the research objectives. The major purpose of this research is to investigate the antecedents of IOS visibility which represents the degree of interorganizational knowledge sharing through IOS. Thus, this study accommodates only the variables directly related to IOS visibility among the wide range of potential variables suggested by the two theories. For example, knowledge sharing routines in RV encompasses both tacit and explicit dimensions of knowledge sharing. However, the scope of this research is limited to the sharing of explicit knowledge, which can be transmitted through IOS. Thus, knowledge sharing routines that facilitate sharing of tacit knowledge such as interfirm employee transfers are not considered in this study. IOS is an important aspect of interorganizational relationships; however, there are other relevant dimensions such as cognitive and social aspects of interorganizational relationships. Future research complementing these aspects may enrich our understanding of interorganizational relationships. The third limitation is related to the characteristics of our sample, which was a convenience sample rather than a random sample. In order to successfully collect data from a sufficient number of companies to test our research model, we used a major database of financial, statistical, and market information about Korean companies to select companies (manufacturers) based on their likelihood of cooperation. Although it is not a random sample, gathering firsthand data from 124 companies required significant effort. In this aspect, regardless, the results of this study should be interpreted with some caution. Further, the data was collected only from three manufacturing industries. Thus, generalizability of the research findings should be enhanced by future research looking into similar issues in other industries. Appendix A. Constructs and measures (seven-point Likert scale, 1–7) Asset specificity (Poppo et al., 2008) 1. It would be difficult for your firm to replace this supplier’s products with another supplier’s product line. 2. The total costs of switching to another comparable supplier would be prohibitive for your firm. 3. It would be costly for your firm to lose this supplier. Interorganizational trust (Poppo et al., 2008) 1. The relationship with this partner firm can be characterized as mutually trusting. 2. This partner firm keeps the promises it makes to your company. 3. Your firm is sure that what this partner firm says is true. 4. This partner firm fulfills its commitments exactly as specified. 5. When making important decisions, this partner firm is concerned about your company’s welfare. Complementary resources (Lunnan & Haugland, 2008) 1. Our partner and we are mutually dependent on each other since we contribute different resources and competencies. 2. Our partner contributes similar resources and competencies as we do. (Reverse) 3. This cooperative venture would not be possible without our partner’s resources and competencies.

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Joint governance structure (Luo, 2008) 1. Both parties always work together on establishing and implementing new policies, rules, and procedures that govern alliance operations. 2. Both parties always work together formulating and executing budget control and investment control. 3. Both parties always work together building and exercising various information control systems (in accounting, sales, production, inventory, etc.). 4. Both parties are always dedicated to establishing a new corporate culture suitable for alliance growth, relinquishing their own corporate culture if necessary. 5. Both parties always work together setting forth alliance goals and objectives and annual plans, and monitoring and appraising middle-level manager performance using some of these measures. 6. Whenever the alliance contract needs alternation or renewal, both parties always work together on all related terms and clauses and jointly monitor contract enforcement thereafter. 7. Contract terms on interparty cooperation, sharing, and exchange are clearly defined and well executed by both parties. 8. Contract terms on directing, monitoring, and governing the alliance’s major activities are clearly defined and well executed by both parties. Environmental uncertainty (Poppo et al., 2008) 1. Availability of the major product in the market is highly uncertain. 2. Uncertainty about production of the major product in the market is a real problem. 3. Supply of the major product is not stable. 4. The price of the major product in the market is volatile. Interdependence (Morgan et al., 2007) The extent to which you agree or disagree with the following statements, as the statement applies to your firm in this category: 1. This partner firm would be very difficult to replace. 2. We are dependent on this partner firm. 3. Losing this partner firm would be costly for us. IOS visibility (Saeed, Malhotra, & Grover, 2005) The extent to which partner firms’ information/knowledge related to SC cooperation is visible to the focal firm through IOS in the following areas: 1. 2. 3. 4. 5.

Inventory status Order status Production schedules Current production capacity Demand forecast information

SC performance (Subramani, 2004) The extent to which you are receiving the following benefits as a result of your relationship with this partner firm: 1. Cost efficiencies from higher sales volumes 2. Improvements to current processes or creation of new processes 3. Increased profitability References Adler, P. S. (2001). Market, hierarchy, and trust: The knowledge economy and the future of capitalism. Organization Science, 12(2), 215–234.

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H. Lee et al. / International Journal of Information Management 34 (2014) 285–295 Ho Lee is a doctoral student of Information Systems at Yonsei University, Korea. He completed a Master of Science in Information System from Pacific States University, USA and received his Bachelor of Science in Computer Science from State University of New York at Sony Brook, USA. He also has various work experience, such as an instructor, executive director, franchise manager, programmer, and quality assurance analyst. His current research interests are in the areas of anonymity, online behavior, knowledge management, and environmental uncertainty. Moon Sun, KIM is graduated and received M.A. from Ewha Womans University, Republic of Korea in 1996 and Ph.D. of Information Systems at the Graduate School of Information, Yonsei University in 2011. She worked as a senior researcher at the Korea Small Business Institute(KOSBI) from 1996 to 2001. After she worked at the intermediary non-profit organization of TIPA(Korea Technology and Information

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Promotion Agency) till 2013 as a manager of survey and research team. Now she is a research fellow at IMRC(Information and Management Research Consortium) since 2013. She is interested in several research fields including e-business, ICT policy and outcome, informatization of SMEs, etc. Kyung Kyu Kim is Professor of Information Systems at Yonsei University, Korea. His current research interests are in the areas of virtual worlds, knowledge management, IT-enabled supply chain management, and behavioral issues in e-business. He has published his research works in Accounting Review, MIS Quarterly, Journal of MIS, Journal of the Association for Information Systems, Omega, Decision Sciences, Information and Management, Database, Journal of Organizational Computing and Electronic Commerce, Journal of Business Research, Electronic Commerce Applications and Research, Journal of Information Science, International Journal of Information Management, and Journal of Information systems.