Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective

JBR-09358; No of Pages 9 Journal of Business Research xxx (2017) xxx–xxx

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Journal of Business Research

Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective Miao Li a,b, Xu Zheng b, Guijun Zhuang a,⁎ a b

Xi'an Jiaotong University, School of Management, Xianning West Road, PO, Box 710049, Xi'an, People's Republic of China City University of Hong Kong, Department of Marketing, Tat Chee Avenue, Kowloon, Hong Kong

a r t i c l e

i n f o

Article history: Received 1 October 2015 Received in revised form 1 September 2016 Accepted 1 December 2016 Available online xxxx Keywords: IT-enabled interactions Network embeddedness Mutual monitoring Supplier-buyer cooperation

a b s t r a c t This study, which integrates the literature on IT (information technology)-enabled interactions and governance mechanisms in interfirm relationships, proposes facilitated access to a network of partners as a key feature that distinguishes IT-enabled interactions from those that occur offline. Specifically, IT-enabled interactions improve interfirm network embeddedness, which in turn improves firms' perceived ability to mutually monitor each other. In contrast to the unilateral monitoring prevalent in prior research, the network-induced mutual monitoring, which reduces information asymmetry and improves power equity, improves cooperation performance without the backfiring “reactance” effect. Moreover, this study offers conceptual distinctions between formal and informal IT-enabled interactions and their different roles in supplier-buyer cooperation. A sample of 240 manufacturing firms in China contributes to this research, and the results strongly support the hypotheses. Overall, this study provides a better understanding of the role of IT-enabled interactions in supplier-buyer cooperation. © 2017 Elsevier Inc. All rights reserved.

1. Introduction With the rapid development and widespread adoption of information technology (IT), interactions between suppliers and buyers have increasingly shifted from offline to IT platforms (Smock, Rudzki, & Rogers, 2007). With the aid of IT-mediated tools such as e-mail, enterprise resource planning (ERP), and videoconferences, firms can conduct contract-based activities through IT-mediated platforms more efficiently and effectively than they can offline (Kim, Umanath, & Kim, 2005). In addition to these formal IT-enabled interactions, firms also socialize informally with their partners on social media platforms, such as Facebook, Twitter, and WeChat, to reinforce social bonds (Fischer & Reuber, 2014). Prior research has highlighted the critical role of IT-enabled interactions in improving cooperation performance. For instance, using IT in supply chain management could improve cooperation efficiency via enhanced information processing capabilities and knowledge sharing (Bensaou, 1997; Chen, Preston, & Xia, 2013), reduce transactional costs by mitigating opportunism (Wang & Wei, 2007) and cultivating trust (Sinkovies, Jean, & Cavusgil, 2011). Despite the important insights previous research provides, three limitations exist regarding how IT-enabled interactions affect supplierbuyer cooperation performance. First, most prior studies have focused ⁎ Corresponding author. E-mail addresses: [email protected] (M. Li), [email protected] (X. Zheng), [email protected] (G. Zhuang).

on the role of IT-enabled formal interaction in improving cooperation outcomes while largely ignoring the role of IT-enabled informal interaction (e.g., Bensaou, 1997; Chen et al., 2013; Wang & Wei, 2007). In practice, firms not only conduct transactions via business-related software and operation systems but also socialize with their partners via social media. Whether an IT-enabled interaction is formal or informal depends on the interaction's purpose. Formal interactions are business-oriented and use business-related IT platforms, such as groupwares and ERP. Their purpose is to complete business-related tasks, such as negotiating contracts or monitoring the contract completion process, and the involved parties are mostly bonded by current contracts. IT-enabled informal interaction, on the other hand, is relationship-oriented and often facilitated by social media. Informal interactions transmit socializing signals and reinforce the social bond and commitment, but common interests or profits do not necessarily bond the involved parties. The functions of IT-enabled interactions in supplier-buyer cooperation may depend on the type of interaction in question. Second, unlike offline interactions, IT-enabled interactions occur through computer-mediated platforms open to third-party participation, such as other buyers or suppliers of a focal supplier-buyer dyad, third-party logistics in ERP, or informal interactions on social media. IT-enabled interactions may enhance a firm's connections with its partner's partners, thus making it possible for them to build contractual relationships and hence enlarge the network in which the focal partners are embedded. Prior research has proved that network embeddedness, or the extent to which the focal relationship between dyadic partners is

http://dx.doi.org/10.1016/j.jbusres.2016.12.022 0148-2963/© 2017 Elsevier Inc. All rights reserved.

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

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M. Li et al. / Journal of Business Research xxx (2017) xxx–xxx

enabled interactions affect supplier-buyer cooperation performance from a dyadic perspective to a network perspective. Third, by integrating prior literature on interfirm governance mechanisms, this study introduces into literature this new concept of mutual monitoring, which serves as a new governance mechanism induced by dense embedded network.

embedded in a network of mutual contracts (Uzzi, 1997), may influence the focal partners' economic activities (Granovetter, 1985) and generate governance implications pertaining to safeguarding and coordinating these exchanges (Grewal, Lilien, & Mallapragada, 2006; Jones, Hesterly, & Borgatti, 1997). Most prior literature on IT-enabled interactions, however, has largely overlooked the role of network embeddedness in IT-enabled interactions to focus mostly on dyadiclevel factors, including relational factors such as trust and information sharing (Chen et al., 2013; Sinkovies et al., 2011) and structural factors such as process integration and visualization (Wang & Wei, 2007). How IT-enabled interactions could regulate the focal dyadic relationship through network embeddedness has not received commensurate attention. Third, a dense embedded network makes both parties' information more transparent not only to each other, but also to the third parties who are bonded within the same business network. Thus, the network may help regulate the focal dyadic relationship through a mutual monitoring mechanism, which refers to the perception that both parties are equally able to measure or “meter” the other party's contractual performance (Heide, Wathne, & Rokkan, 2007). Either party's engagement in opportunism, for example, will be quickly detected by the other via information transmission within the network, and punished collectively via short-term ostracism or sabotage (Jones et al., 1997). Concern for their own reputations and the danger of collective sanctions make firms less likely to engage in opportunism. In contrast to traditional unilateral monitoring, mutual monitoring functions as a shared norm in which partners perceive less information asymmetry and monitoring power inequity between them, and thus this may induce a satisfactory cooperation without backfiring effects. Previous research has paid scant attention to this new form of bilateral and equal monitoring mechanism. To fill the gaps, this study integrates the literature on IT-enabled interactions and interfirm governance mechanisms and develops a conceptual model (See Fig. 1). The model hypothesizes that IT-enabled interactions improve network embeddedness of focal firms which in turn lead to an increase in their perceived ability to mutually monitor each other via third parties, and such mutual monitoring contributes to the success of their cooperation. Using data from senior managers at 240 different manufacturing firms in China, this study finds strong support for the hypotheses and contributes to the extant research on the role of IT in governing supplier-buyer relationships in three ways. First, the current study distinguishes the different types of IT-enabled interactions (i.e., formal and informal interactions) and investigates their underlying mechanisms for enhancing supplier-buyer cooperation performance. Second, by focusing on the role of the IT-induced network embeddedness, this study enhances prior research about how IT-

IT-enabled Formal Interaction

2. Conceptual framework and research hypothesis 2.1. Conceptual framework Interaction with business partners is an essential part of all business activities, it refers to “mutual or reciprocal actions where two or more parties have an effect upon one another” (Grönroos, 2011, p. 244). Connectivity is inherent to interaction, which means that the parties are in contact with each other in one way or another (Wagner, 1994). As IT has increasingly become vital to contemporary supply chain systems, supplier-buyer interactions have shifted toward IT platforms (Kim et al., 2005), resulting in changes in how business transactions are conducted. The present study defines IT-enabled interactions as mutual actions conducted by suppliers and buyers to exert influence upon one another through the use of IT (Grönroos, 2011), which may be further categorized into formal and informal types (Styles & Ambler, 2003), depending on the objectives of their interactions. IT-enabled formal interaction refers to partners' IT-aided, contractbased bilateral activities aimed at negotiating and safeguarding contracts. It is business-oriented and includes all contract-related activities, such as negotiating orders or agreements over groupwares, signing contracts electronically, integrating business processes, and monitoring the extent to which the partner fulfills contracts via ERP and groupwares. In contrast, IT-enabled informal interaction refers to the partners' bilateral socialization tactics beyond official trade settings via various social platforms, such as following each other and chatting about private interests on WeChat (Fischer & Reuber, 2014). Instead of focusing on exchanging contract-related information, partners in informal interactions mainly transmit socializing signals to reinforce social bonds and commitments, thus establishing a foundation to coordinate and deliver a promise for future exchanges (Kraimer, 1998). Both formal and informal IT-enabled interactions enhance a firm's connections with a network of partners, including both focal partners and third parties. Depending on which kind of interaction, the third parties represent connections of different types. Third parties connected through formal interactions are usually the existing partners of focal parties, such as a supplier's other buyers, a buyer's other suppliers, or a logistics company. Those connected through informal interactions, however, can be anyone connected to either party via social media,

H1a H3

H2 Network Embeddedness

H1c

IT-enabled Informal Interaction

Mutual Monitoring

Cooperation Performance

H1b Control variables Contract Complexity Offline Interaction Buyer's Dependence Relationship Length Firm Type

Fig. 1. Conceptual model.

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

M. Li et al. / Journal of Business Research xxx (2017) xxx–xxx

including some government officials, business partners, or personal friends. Either formal or informal ties might help firms seek new business opportunities by connecting with new third parties, thus enhance the possibility for focal partners to develop transactional relationships with third parties in common. Thus, IT-enabled interactions not only increase the frequency of dyadic interactions but also help increase network embeddedness (i.e., the extent to which the focal relationship between dyadic partners is embedded in a network of mutual contracts; Uzzi, 1997). Dense embedded networks increase firms' perceived ability to monitor each other. The notion of mutual monitoring developed from agency theory (Fama & Jensen, 1983), which describes a control system designed by the principal (i.e., the manager) that enables multiple agents to supervise one another on the principal's behalf. This individual-level concept of mutual monitoring has been studied extensively in accounting and organizational behavior literature and has proved effective in suppressing individual members' opportunistic behavior (Kandel & Lazear, 1992; Welbourne, Balkin, & Gomez-Mejia, 1995). Although their definitions of mutual monitoring differ, researchers have concurred that parties in a mutual monitoring system perceive that they are equally capable of observing each other's activities and measuring each other's performance through third parties (Fama & Jensen, 1983; Kandel & Lazear, 1992; Welbourne et al., 1995). In this paper, we define mutual monitoring from a network perspective as the degree to which the supplier and the buyer believe that they are equally able to measure or “meter” each other's performance via third parties (Heide et al., 2007; Wang, Gu, & Dong, 2013).

2.2. IT-enabled interactions and network embeddedness Both types of IT-enabled interactions may enhance the degree of network embeddedness, albeit to different extents. The embedded network consists of mutual contracts among both focal partners and third parties. The level of network embeddedness indicates how closely a firm and its partners' partners are connected, thus accounting for firms' relationships both with one another and with shared third parties (Wuyts & Geyskens, 2005). In a highly embedded network, the focal firms work intensively and collaboratively with common third parties who are empowered to regulate the network's focal relationship. In contrast, in a less embedded network in which the focal firms have fewer connections with common third parties, third parties as a whole would possess less influence on the focal exchange. IT-enabled formal interactions induce a higher degree of network embeddedness by enhancing a firm's connections with the other party's current partners. A higher degree of IT-enabled formal interactions between supplier and buyer indicates a higher degree of using operating systems and business applications to conduct business transactions, which facilitates information sharing and enhances each firm's ability to track the ongoing business transactions of the other party. The improved knowledge about each other's capabilities and weaknesses facilitates supply chain specialization, that is to say, partners might outsource some business activities to third partners, such as the logistic company, online banking, and IT outsourcing company, to improve cooperation efficiency (Liautaud & Hammond, 2000). Besides, as firms increasingly notice the importance of integrating supply chain information, the IT system initiator might open the system to both the focal partner and related third parties, which may include complementary service providers, end customers, focal buyer's other suppliers, and focal supplier's other distributors or wholesalers (Smock et al., 2007), all of whom could become common partners. Prior research has indicated that firms tend to conduct business with their partners' partners because of transferred trust and reduced transactional risks (Gulati, 1995). Thus, when a firm detects potential future partners, it may reach and connect directly with them. Therefore,

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H1a. IT-enabled formal interaction positively influences network embeddedness. However, IT-enabled informal interactions, which are often facilitated by social platforms, expose firms to broader social networks. These social media connections represent a wide variety of relationships, such as connections with the boundary spanners of existing business partners, partners' partners, business friends, and government officials. Informal IT-enabled interactions between firms may increase network embeddedness for three reasons. First, compared with formal interactions, informal interactions expose the focal firm to a broader set of relationships by making a firm's business ties and interactions with other firms visible. The more partners socialize online, the more they are likely to detect who conducts business with whom. This would help a firm to form new business partnerships with existing partners' partners, thus increasing the chances of embedding the focal relationship in a network. Second, besides the partner's current partners, informal interactions also expose the focal partners to some strangers on social media who could be common partners. For instance, by tracing supplier-buyer interactions online, a beverage formula seller could find out who provides raw materials to a manufacture, and chose the proper supplier-manufacture coupling with a fit production line to produce its beverage. Third, unlike formal IT-enabled interactions, the online informal interactions may indicate the business partners' preferences and interests in non-business-related areas. This information may help firms better develop a trusting relationship with a potential business partner (Nooteboom, Berger, & Noorderhaven, 1997), which, in turn, increase the chances that the two will form a new business relationship. Thus, H1b. IT-enabled informal interaction positively influences network embeddedness. Comparing these two different types of interactions, this paper further posits that IT-enabled informal interaction is more likely than ITenabled formal interaction to lead to a greater degree of network embeddedness. First, unlike formal IT-enabled interactions, which are facilitated by relatively closed platforms, informal IT-enabled interactions are facilitated by open-ended and not necessarily business-driven social platforms (Fischer & Reuber, 2014). The connections informal interactions create may involve all kinds of relationships, such as personal friendships or relationships with government officials or business partners. Thus, compared with IT-enabled formal interaction, firms in IT-enabled informal interactions are more likely to connect with their partners' partners because of their wider reach. Second, whether the firm can connect with the partners' partners in formal interactions depends largely on the other partner's permission. If the firm's partner conceals its indirect ties out of self-interest (e.g., competing relationships might exist between the focal supplier and its material suppliers who provide similar products to the buyer; one party has higher bargaining power and pushes the other to transport products by itself to save costs), the firm gets restricted access to those indirect ties. However, through IT-enabled informal interactions, firms can contact the third partners directly online. Third, informal interactions via social media indicate business partners' preferences and interests that may not be apparent in formal business interactions. Such information may help firms cultivate trust and thus may be more likely to generate new business relationships with a firm's partners' partners. Therefore, H1c. The positive impact of IT-enabled informal interaction on network embeddedness is greater than that of IT-enabled formal interaction. 2.3. Network embeddedness and mutual monitoring Unlike traditional unilateral monitoring mechanisms, mutual monitoring indicates the degree to which the supplier and the buyer believe that they are equally able to measure or “meter” each other's performance via third parties (Heide et al., 2007; Wang, Gu, et al., 2013;

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

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Wang, Li, Ross and Craighead, 2013). A greater degree of network embeddedness may lead to an increase in a firm's perception of mutual monitoring because it increases both the third parties' motivation and ability to do so. First, because members of the embedded network have mutual contracts with one another, either firm's losses resulting from a partner's opportunism could, in turn, damage its cooperative efficiency with third parties. Hence, common partners have a strong incentive to monitor each party's opportunistic behaviors (Kandel & Lazear, 1992). Second, the cost for third parties to monitor the opportunist is reduced with a denser network where information spreads more quickly. Third, third parties are better able to sanction the norm breaker with the help of the whole network because the close network members share a macro culture which specifies tacit behavior rules, and they should punish the norm breaker collectively (Jones et al., 1997). For instance, if a food supplier sells ingredients of low quality to a foreign manufacture, that manufacture's other partners in their country will quickly get the news and unite to punish the supplier via reputational loss, short-term ostracism and even sabotage. In this sense, the formation of such a network would largely reduce information asymmetry, which usually accompanies traditional, unilateral monitoring. Therefore, the closer the network in which the focal relationship is embedded, the more the supplier and the buyer perceive that both they and their partner can monitor each other equally. H2. Network embeddedness positively affects mutual monitoring.

2.4. The impact of mutual monitoring on cooperation performance Lastly, firms' perceived abilities to mutually monitor each other increases cooperation performance, which refers to the firm's satisfaction with the outcome of the cooperation (Saxton, 1997). Transaction cost economics (TCE) research has argued that monitoring can effectively suppress opportunism and improve cooperation efficiency (Williamson, 2002). However, information asymmetry (i.e., a state in which the monitored party does not possess the equivalent ability to monitor the other) may cause unilateral monitoring to backfire as it may lead the monitored party to rebel and resist (Bello & Gilliland, 1997; Grewal, Kumar, Mallapragada, & Saini, 2013). Such information asymmetry also decreases interfirm trust because the party that holds relatively less information would feel passive and weak when bargaining and negotiating contracts with the other party (Kumar & Van Dissel, 1996), which in turn enhances contracting costs and reduces the firm's satisfaction with the cooperation. Unlike unilateral monitoring, network-induced mutual monitoring avoids this drawback and contributes to the success of cooperation by mitigating such information asymmetry. For one thing, both the supplier and the buyer could acquire rich information about each other to clarify misunderstandings and prevent nefarious activities (Ouchi, 1979). For another, decreasing the monitoring power imbalance makes the monitored party less likely to rebel or resist, generates mutual trust, and thus induces a satisfactory cooperation. H3. Mutual monitoring has a positive impact on cooperation performance.

3. Method 3.1. Data collection and sample Data concerning manufacturers' relationships with raw material suppliers represent a suitable empirical setting for the current study because the manufactures generally conduct business with multiple supply chain members, all of whom could be potential common partners, and thus offers a suitable context to capture the network's role in regulating dyadic relationships. The authors acquired the list of sample firms

from the Chinese People's Political Consultative Conference (CPPCC), selecting the capitals of 10 provinces and a random sample of 100 listed manufacturers from each province (1000 firms in total). Firms on electronic markets such as Alibaba were excluded because the third-party platform itself holds legal power and responsibility in governing the focal firm. The selected 10 provinces include Beijing, Jilin, and Guangdong where the manufacturing firms are relatively concentrated and well-developed. The random sample in each province covers a wide range of industries (e.g., textiles, chemicals, electronics, petrochemical, food, furniture), which ensures that the inter-organizational information systems they have adopted are diverse. The authors collect data from these manufacturers' senior managers from January–April 2015, following the procedure suggested by Li, Poppo, and Zhou (2008). Three to eight CPPCC section commanders in each province included in the sample helped collect the questionnaires after data collection training, with a promised reward that would increase proportionally to the amount of the valid questionnaires they collected. The project also promised all respondents a cell phone card worth 20 RMB as the reward. The authors randomly picked five questionnaires every week and called them to check several answers and to find out how the group leaders collected the questionnaires. Of the 600 senior managers who agreed to participate in the interview, 300 individuals completed the online questionnaire, which focused on their business relationships with major suppliers. The preliminary data screening results in deletion of 17 questionnaires with excessive missing information and 43 questionnaires that were finished too quickly (i.e. in b10 min). As a result, 240 valid responses remained, resulting in a net response rate of 40%. All the respondents were familiar with the relationship development with the suppliers and averaged a work history of 9.44 years in the manufacturing industry and 7.68 years at their respective companies, indicating that they are knowledgeable informants. Results from a t-test analysis reveal that no significant difference existed in terms of key firm characteristics such as industry type (t = −0.37), firm size (t = −1.20) and the respondents' work history at their respective companies (t = 0.19) between the early 25% and late 25% of respondents (Armstrong & Overton, 1977), indicating that nonresponse bias is not a concern in this study. 3.2. Measurement The authors develop the construct measures according to standard survey and psychometric scale development procedures (Churchill, 1979), first generating or adapting the scales based on the conceptual definitions, a literature review, and in-depth interviews with manufacturers. Next, the authors develop the questionnaire in English, translated it into Chinese, and had five research assistants translate it back into English to ensure conceptual exactness and equivalence (Hoskisson, Eden, Lau, & Wright, 2000). Finally, a few items are modified accordingly to a pilot study. Appendix A lists the final measures, and Table 1 provides the means, standard deviations, and correlations among the focal constructs. 3.2.1. IT-enabled interaction The measure of IT-enabled formal interaction, adapted from Kim et al. (2005), captures partners' key IT-aided, contract-based bilateral activities, such as transferring business materials via groupwares, negotiating agreements via IT, and integrating business processes through information systems. The present study adds an item to measure the frequency with which the partners hold videoconferences. The scale of IT-enabled informal interaction captures the extent of partners' bilateral socialization tactics beyond official trade settings via various social platforms. This study develops a scale to measure this new construct based on the psychometric scale development procedures suggested by Churchill (1979). First, the authors designed a three-item scale to measure IT-enabled informal interaction on the basis of the extant literature on offline socialization (Poppo & Zhou, 2014; Wang, Gu, et al., 2013;

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

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Table 1 Descriptive statistics and bivariate correlation mix. Variable

1

2

3

4

5

6

7

8

9

10

1 IT-enabled formal Interaction 2 IT-enabled informal Interaction 3 Network embeddedness 4 Mutual monitoring 5 Cooperation performance 6 Contract complexity 7 Offline interaction 8 Buyer's dependence 9 Relationship length 10 Firm type 11 MV marker Mean Std. deviation

0.79 0.23⁎⁎ 0.36⁎⁎ 0.40⁎⁎ 0.31⁎⁎ 0.24⁎⁎ 0.30⁎⁎

0.22⁎⁎ 0.75 0.41⁎⁎ 0.23⁎⁎ 0.18⁎⁎ 0.05 0.39⁎⁎

0.39⁎⁎ 0.22⁎⁎ 0.39⁎⁎ 0.74 0.39⁎⁎ 0.19⁎⁎ 0.30⁎⁎

0.29⁎⁎ 0.38⁎⁎ 0.39⁎⁎ 0.29⁎⁎ 0.41⁎⁎ 0.15⁎ 0.77 0.21⁎⁎

0.07 0.01 0.12⁎ 0.06 0.19⁎⁎ 0.09 0.20⁎⁎

0.02 -0.04 -0.08 -0.18⁎⁎ 3.20 0.92

0.30⁎⁎ 0.17⁎⁎ 0.37⁎⁎ 0.38⁎⁎ 0.75 0.17⁎⁎ 0.42⁎⁎ 0.20⁎⁎

0.23⁎⁎ 0.04 0.14⁎ 0.18⁎⁎ 0.16⁎⁎ 0.88 0.16⁎

0.08 0.03 0.13⁎ 0.17⁎ 3.64 0.99

0.35⁎⁎ 0.40⁎⁎ 0.79 0.40⁎⁎ 0.38⁎⁎ 0.15⁎ 0.40⁎⁎ 0.13⁎

0.02 -0.05 0.04 0 0.01 0.05 0.06 -0.02 – 0.03 0.42⁎⁎ 8.95 7.39

0.12† -0.09 -0.13† 0.02 -0.02 0.20⁎⁎ -0.04 0 0.02 – 0.21⁎⁎ 2.14 1.11

0.05 -0.12† -0.02 3.49 0.78

0.07 0.01 0.03 0.01 3.32 0.82

0.02 -0.01 -0.06 3.68 0.65

0.10 0.06 0.21⁎⁎ 0.30⁎⁎ 3.96 0.74

0.07 -0.03 -0.13⁎ 3.20 0.80

0.76 -0.01 0.01 0.06 2.86 0.91

Note: N = 240. Diagonal elements (bold) are the square root of the variance shared between the constructs and their measures (AVE). † p b 0.1 (two-tailed). ⁎ p b 0.05 (two-tailed). ⁎⁎ p b 0.01(two-tailed).

Wang, Li, et al., 2013) and social media (Ellison, Steinfield, & Lampe, 2011). The three items reflect online social activities across the two firms (see Appendix A). The results from the exploratory factor analysis (EFA) with a pilot study of 52 senior managers from different manufacturing firms reveal that all three items converged under a single dimension, with all factor loadings above 0.70 (0.78, 0.83, and 0.84). 3.2.2. Mutual monitoring Mutual monitoring refers to the degree to which the supplier and the buyer believe that they are equally able to measure or meter each other's performance via network members. Because this is a new construct, this study follows Churchill's (1979) instructions for measure development. On the basis of prior literature in unilateral monitoring (Grewal et al., 2013; Heide et al., 2007), mutual monitoring at the individual level (Kandel & Lazear, 1992; Welbourne et al., 1995), and feedback from in-depth interviews, the study develops a four-item scale, which reflects the manufacturer's perception that both parties are equally able to detect each other's opportunistic behaviors within the network. All four items converge in one dimension in EFA analysis based on the pilot study data (52 manufacturing firms). The factor loading of each item is above 0.7 (0.73, 0.74, 0.79, and 0.85). 3.2.3. Network embeddedness and cooperation performance This study adapts the measure of network embeddedness from Wuyts and Geyskens (2005) to assess a firm's closeness to its partner's partners and measures it with four items that reflect how intensively and collaboratively the firm works with the common partners and how long they maintain the relationship. Following Li, Xie, Teo, and Peng (2010), this study measures cooperation performance with the firm's satisfaction with the exchange outcomes. The three-item scale reflects the degree to which the partner has contributed to the firm's market position, fulfilled the cooperation goals, and satisfied the firm's overall attitude toward the cooperation (Saxton, 1997). 3.2.4. Control variables Prior literature (e.g., Rowley, Behrens, & Krackhardt, 2000; Bond, Houston, & Tang, 2008) has indicated that exchange characteristics such as relationship length, interfirm dependence and firm type may influence the embedded network's effects. Thus, this study controls for the length of the manufacturer's relationship with the selected supplier and measures it by the number of years they had worked together (Poppo & Zhou, 2014). This study controls the manufacturer's dependence on the supplier, and measures it with a three-item scale adapted

from Skinner, Gassenheimer, and Kelley (1992). This study distinguishes firms between different types, including self-owned, joint venture, state-owned, and foreign-owned (Bond et al., 2008). Because contract complexity (which indicates the degree to which parties spell out each other's obligations and roles and specify rules to resolve disputes through formal contracts), may influence the power of network governance by claiming who should be responsible to deal with uncertainty (Williamson, 2002), this study employs the three-item scale developed by Poppo and Zhou (2014) to control for it. In addition, because offline interactions may drive interfirm IT-enabled bilateral activities (Matzat, 2010), this study controls for offline interaction, and measures it with three items that indicate the frequency and pattern of offline socializing between partners (Wang, Gu, et al., 2013; Wang, Li, et al., 2013), 3.3. Reliability and validity Using data from 240 manufacturers in China, this research conducts a confirmatory factor analysis (CFA) to assess the reflective measure's reliability and validity, and filters out three items with factor loadings below 0.40 (see Appendix A). The composite reliability (CR) scores for all factors are N00.80, and all the average variance extracted (AVE) values exceed the benchmark of 0.50, thus proving a good convergent validity. The discriminant validity is satisfactory, with the square root of the AVE exceeding the squared correlation between any pair of constructs (Fornell & Larcker, 1981). Further, to address potential concerns over trait bandwidth, this study runs two measurement models, one with formal and informal interactions as separate constructs and one with these two variables combined into one construct. The eight-factor measurement model fits the data more satisfactorily (χ2/df = 1.50, p b 0.001; confirmatory fit index [CFI] = 0.96; incremental fit index [IFI] = 0.96; Tacker-Lewis index [TLI] = 0.95; root mean square error of approximation [RMSEA] = 0.046) than the seven-factor measurement model (χ2/df = 2.46, p b 0.001; CFI = 0.87; IFI = 0.87; TLI = 0.84; RMSEA =0.078). The chi-square difference between the models is highly significant (Δχ2 = 252.74, 7df, p b 0.001), suggesting that retaining the model is appropriate. 3.4. Common method variance Besides procedural remedies during data collection, this study employs three empirical tests to control for common method variance. First, Harmon”s one-factor test (Podsakoff, MacKenzie, Lee, &

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

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Table 2 Structural model results.

Endogenous variables Independent variables IT-enabled formal interaction IT-enabled informal interaction Network embeddedness Mutual monitoring Controls Contract complexity Offline interaction Dependence Firm nature Relationship length

Network embeddedness

Mutual monitoring

Cooperation performance

0.25⁎⁎(3.41) 0.31⁎⁎(3.64) 0.35⁎⁎(4.07) 0.33⁎⁎(3.90)

0.14⁎ (2.00) 0.11† (1.63) 0.14† (1.72) 0.34⁎⁎(4.07) 0.02 ( 0.30) 0.14† (1.87) 0.03 ( 0.47) −0.03 (−0.44) −0.02 −0.00 (−0.03) (−0.35) R2 0.35 0.21 0.33 Goodness-of-fit:χ2/df = 1.66, p b 0.001; CFI = 0.93; IFI = 0.93; TLI = 0.92; RMSEA =0.053 0.23⁎⁎(2.76)

Note: N = 240. CR = composite reliability, AVE = average variance extracted, CFI = comparative fit index, IFI = incremental fit index, TLI = Tacker-Lewis index, and RMSEA = root mean square error of approximation. † p b 0.1 (two-tailed). ⁎ p b 0.05 (two-tailed). ⁎⁎ p b 0.01(two-tailed).

Podsakoff, 2003) is employed to examine the existence of the general factor. The EFA results reveal that eight factors with eigenvalues N1 account for 74.71% of the total variance. The first factor only explains 10.99% of the total variance. Second, the marker variable (MV) approach provided by Lindell and Whitney (2001) is conducted, and the authors use firm size, which has been proven unrelated to at least one other scale (Audia & Greve, 2006), as the MV and measure it with the number of employees. The lowest positive zero-order correlation between firm size and the other variables is 0.01. After partialling out this coefficient to adjust the correlations, this study finds no significant change among the important constructs (see Table 2). Third, this study employs a single-factor approach using structural equation modeling (SEM) analysis by setting all items to load on a single factor. The model is unacceptable (χ2/df = 7.21, p b 0.001; CFI = 0.39; IFI = 0.39; TLI = 0.33; RMSEA =0.16). All three tests suggest that common method variance is not a concern. 4. Analyses and results 4.1. Results This study tests the hypotheses using the SEM analysis to simulate the multiple regressions of the entire system, including latent variables

and indicators in the research model (Byrne, 1994). Because some degree of association may exist among IT-enabled formal and informal interactions and offline interaction, this study models their covariances in the structural model to test their unique effects on the dependent variables (Kline, 1998). The model is satisfactory (χ2/df = 1.66, p b 0.001; CFI = 0.93; IFI = 0.93; TLI = 0.92; RMSEA = 0.053), and the results provide strong support for the hypotheses (see Table 2). First, the results confirm the positive effects of both IT-enabled formal interaction (β = 0.25, p b 0.01) and IT-enabled informal interaction (β = 0.31, p b 0.01) on network embeddedness, which support H1a and H1b. Next, to test H1c, this study performs a beta difference test. As the results indicate (see Table 3), the difference in the coefficients of the variables for IT-enabled formal and informal interactions is significant (t = 1.68, p b 0.1), which supports H1c. With regard to H2, the coefficient between network embeddedness and mutual monitoring is significant and positive (β = 0.35, p b 0.01), which provides strong support for H2. Finally, in accordance with H3, mutual monitoring has a positive and significant effect on cooperation performance (β = 0.33, p b 0.01).

4.2. Post hoc analysis This study employs two additional tests to compare the roles of IT-enabled interactions and offline interaction in supplier-buyer cooperation. First, the results from beta coefficient equality tests suggest that the positive effect of offline interaction on network embeddedness (β = 0.23, p b 0.01) is weaker than that of both IT-enabled formal interaction (β = 0.25, p b 0.01; t = 3.86, p b 0.001) and IT-enabled informal interaction (β = 0.31, p b 0.01; t = 4.65, p b 0.001). Thus, offline interaction has the weakest positive effect on network embeddedness. Second, in addition to the direct effect of different types of interactions on network embeddedness, this study also tests the mediating paths of network embeddedness between interfirm interactions and mutual monitoring with bootstrap analysis (Preacher & Hayes, 2008). After adding the links between IT-enabled formal and informal interactions and mutual monitoring in the SEM model, this study gets a satisfactory model (χ2/df = 1.61, p b 0.001; CFI = 0.93; IFI = 0.93; TLI = 0.92; RMSEA = 0.050), which is significantly better than the original model (Δχ2 = 20.44, 2df, p b 0.01). A bootstrap analysis (5000 resamples) yields the estimates of the confidence intervals (CI) for direct and indirect effects, which reveal that network embeddedness mediates the effects of IT-enabled formal interaction (β = 0.052, S.E. = 0.033, 95% CI = 0.004, 0.136), IT-enabled informal interaction (β = 0.068, S.E. = 0.041, 95% CI = 0.007, 0.170), and offline interaction (β = 0.052, S.E. = 0.033, 95% CI = 0.005, 0.145) on mutual monitoring. The significant direct effect of IT-enabled formal interaction on mutual monitoring (β = 0.354, S.E. = 0.093, 95% CI = 0.169, 0.529) suggests partial mediation, while the insignificant direct effects of IT-enabled informal interaction (β = 0.020, S.E. = 0.106, 95% CI = −0.196, 0.229) and offline interaction (β = 0.079, S.E. = 0.093, 95% CI = − 0.102, 0.267) on mutual monitoring suggest full mediation.

Table 3 Results of beta difference tests. Statistic

IT-enabled formal interaction

IT-enabled informal interaction

β b s.d. (bi) Covariance (b1b2) Difference between coefficients t

0.245 3.408 0.086 −0.001 0.232 1.680†

0.308 3.640 0.095

1

Note: t = (bi −bj)/ s(bi −bj) for i≠j, where s(bi −bj) = ½s2 ðbi Þ þ s2 ðb j Þ−2covðbi ; b j Þ2 . † p b 0.1.

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

M. Li et al. / Journal of Business Research xxx (2017) xxx–xxx

5. Discussion This study aims to unveil the underlying paths through which ITenabled formal and informal interactions affect supplier-buyer cooperation performance from a network perspective. The authors suggest that both IT-enabled formal and informal supplier-buyer interactions increase network embeddedness, which, in turn, enhances both firms' perceived ability to monitor each other. Such mutual monitoring reduces information asymmetry and the perceived monitoring power inequity that often accompanies traditional, unilateral monitoring, thus improving cooperation performance. This study carries the following theoretical and managerial implications. 5.1. Theoretical implications First, this study contributes to the IT literature by distinguishing IT-enabled informal interactions from IT-enabled formal interactions in supplier-buyer cooperation. Specifically, the objectives of firms' interactions determine the types of the established connections as well as the IT platforms used, which, in turn, differentiate the two types of interactions. For formal interactions, the objectives are to complete business transactions. As a result, connected firms make up existing business partners, and facilitating IT platforms are business-related software or platforms. However, the objective of informal interactions is to transmit socializing commitment and form social ties. Thus, the network formed is openended and may consist of all kinds of relationships, including existing business partners, partners' partners, business friends, and government officials and is usually facilitated by social media. Because the connections created by formal and informal interactions differ, the power of these two types of interactions in leveraging the network-based structure to regulate dyadic relations also differs. Compared with the formal interactions, informal interactions enable either party to connect directly with a larger set of third parties, not necessarily with the permission of the focal partner, thus have a greater influence on network embeddedness. In the case of offline interactions, the power of IT-enabled informal interaction in improving network embeddedness is greater than that of both IT-enabled formal interaction and offline interaction. Second, this study highlights the role of network embeddedness in governing the dyadic supplier-buyer relationship. In particular, this study finds that increased network embeddedness is an important result of the high frequency of IT-enabled interactions and can regulate focal relationship through mutual monitoring, which, in turn, increases cooperation performance. When discussing how IT-enabled interactions affect interfirm relational outcomes, most prior research has focused on dyadic factors, such as trust, information sharing, and process integration (Chen et al., 2013; Wang & Wei, 2007); however, the role of the network has seldom been examined. This research extends the dyadic view of extant literature by highlighting the role of network embeddedness in helping firms improve cooperation performance. Third, this study contributes to the literature on interfirm governance mechanisms by identifying mutual monitoring, a distinct, network-induced governance mechanism that previous research has rarely examined. In contrast to the unilateral monitoring identified in the TCE framework, which is usually characterized by information asymmetry favoring one party (Grewal et al., 2013), mutual monitoring indicates that exchange parties in this system are equally able to measure or meter each other's performance via the network members. Prior research has documented that although a unilateral monitoring mechanism can help firms acquire needed information and deter opportunism, it may also backfire and harm the dyadic relationship because of the “reactance effect” (i.e., the monitored party might interpret the monitoring party's attitudes and behaviors as defensive; Bello & Gilliland, 1997). Further,

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information asymmetry makes the monitored party feel the monitoring system is unfair and trust the other party less. This study argues that mutual monitoring overcomes the reactance effect because both parties perceive themselves as equally able to monitor the other with the aid of common partners. Specifically, both parties are able to track ongoing business transactions and suppress opportunism through fine-grained information sharing from the network while punishing norm breakers via collective sanctions (Jones et al., 1997; Phelps, 2010). Identifying this new network-induced monitoring system extends prior research on firms' choices of monitoring mechanisms. 5.2. Managerial implications This research also assists supply chain managers in better using IT to manage their relationships with business partners. First, to improve cooperation performance, in addition to formal types of interactions facilitated by business-related software and operating systems, firms should also consider building relationships through informal interactions over social media. As the results show, both types of interactions play significant roles in enlarging the embedded network, which regulates the focal relationship and improves cooperation performance. In particular, firms should pay more attention to informal interactions via social media, which has more power to improve their access to other network members. Second, whatever the form of interaction, firms should develop relationships with additional shared third parties, because the current research has found that network embeddedness is a key factor that leads to firms' perceptions of being monitored equally, which, in turn, enhances cooperation performance. Third, this research has found that mutual monitoring is an effective mechanism for governing dyadic relationships because unilateral monitoring creates varying amounts of information asymmetry; therefore, this new form of monitoring deserves more attention from firms. 5.3. Limitations and future research This study has several limitations that suggest directions for future research. First, the authors only collected data from the buyer's side. More research with dyadic data on key variables such as interfirm interactions and mutual monitoring is needed to cross-validate the results. Second, the partial mediation effect between IT-enabled formal interaction and mutual monitoring suggests that there may be different paths to explain the effect of formal interactions. Future research may explore alternative mediating mechanisms, such as enhancing each other”s network centrality. Thus, more third parties, even if unrelated to the norm breaker or the victim, would like to monitor the other party for the firm to gain benefits. Third, this study collected data from a single key respondent to measure the concept of IT-enabled informal interaction. Although the authors carefully selected the respondents who were wellinformed on the questions (Phillips, 1981; Gaski, 1984) and asked them to respond as representatives of their firms, biases cannot be ruled out. Future researchers could use multi-informants to collect data and retest the hypotheses related to IT-enabled informal interactions. Finally, the role of IT-enabled interactions may vary as the interfirm relationship develops. Thus, a longitudinal study would provide a better understanding of the dynamic role IT-enabled interactions play in interfirm relationship management and explore the complementary or substitutive relationships between IT-enabled interactions and offline interactions within different relationship stages. Acknowledgements We would like to thank the editors and the reviewers for their insightful comments on the earlier version of this manuscript. This work was supported by the National Natural Science Foundation of China [No. 71132005].

Please cite this article as: Li, M., et al., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.12.022

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M. Li et al. / Journal of Business Research xxx (2017) xxx–xxx

Appendix A. Measure

Variables IT-enabled formal interactions 1. We often transfer business materials with each other through information technologies (e.g., e-mail). 2. We often transmit purchase orders with each other through information technologies (e.g., ERP, Sametime). 3. We often access each other's inventory (or production) levels through information technologies. 4. We often coordinate inventory (or production) schedules with each other through information technologies. 5. We hold video conferences on a regular basis. IT-enabled informal interactions 1. We follow one another on social media (e.g., QQ, WeChat, Twitter). 2. We often chat about family or interests over the Internet with one another in our spare time. 3. We often interact on social media, such as posting or forwarding interesting links to one another, making comments, and “opting in.” Network embeddedness 1. Our firm worked very intensively with one or more partners of this supplier. 2. Our firm has a very close relationship with one or more partners of this supplier. 3. Our firm's relationship with the partners of this supplier was kept at arm's length, restricted purely to executing transactions (r). 4. Our firm has a very collaborative relationship with one or more partners of this supplier, like a real team. Mutual monitoring 1. We feel like that both of us conduct business in an open platform. 2. We and the supplier can both be aware of each other's activities through a third party. 3. Either we or the supplier perform opportunistic behaviors; a third party will know that soon. 4. We can guarantee each other's good faith through the third party. Cooperation performance 1. Our cooperation with this supplier has contributed to our market position. 2. This cooperation has always realized the goals we set out to achieve. 3. Overall, we are satisfied with the performance of this cooperation. Contract complexity 1. We have specific, well-detailed agreements with this supplier. 2. We have customized agreements that detail the obligations of both parties. 3. We have detailed contractual agreements specifically designed with this supplier. Offline interaction 1. Our firm and this supplier always visit each other. 2. We have personal visit outside of work. 3. We always engage in social activities to improve our relationships. Buyer's dependence on the supplier 1. It would be difficult for this supplier to find some other manufacturer to replace our company. 2. It would be a great loss for this supplier to replace our firm with another manufacturer. 3. It would be difficult for this supplier to find another manufacturer like us that offers it so much in the way of benefits and profits. Relationship length How many years has your company been doing business with this particular supplier? Firm type Your company is: 1 = Self-owned; 2 = Joint Venture; 3 = State-owned; 4 = Foreign-owned Overall Model Fit (χ2/df = 1.56, p b 0.001; CFI = 0.95; IFI = 0.95; TLI = 0.93; RMSEA = 0.048)

α

AVE

CR

Factor loading

0.83

0.63

0.84

.40a 0.61 0.86 0.89 .38a

0.80

0.57

0.80 0.65 0.80 0.80

0.82

0.63

0.84 0.82 0.92 .27a 0.62

0.83

0.55

0.83 0.63 0.78 0.76 0.79

0.80

0.57

0.80 0.72 0.78 0.77

0.91

0.78

0.91 0.87 0.96 0.81

0.80

0.60

0.82 0.59 0.87 0.84

0.80

0.58

0.80 0.72 0.79 0.77

Notes: CR = composite reliability, AVE = average variance extracted, CFI = comparative fit index, IFI = incremental fit index, TLI = Tacker-Lewis index, and RMSEA = root mean square error of approximation. Unless otherwise specified, all items were scored on five-point Likert scales (1 = “strongly disagree” and 5 = “strongly agree”). a Items deleted from further analysis because of low factor loading.

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