University of Groningen

Exploring supplier-supplier innovations within the supply network Potter, Antony; Wilhelm, Miriam

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DOI: 10.1002/joom.1124

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Citation for published version (APA): Potter, A., & Wilhelm, M. (2020). Exploring supplier-supplier innovations within the Toyota supply network: A supply network perspective. Journal of Operations Management, 66(7-8), 797-819. https://doi.org/10.1002/joom.1124

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Download date: 29-09-2021 Received: 16 October 2020 Revised: 5 September 2020 Accepted: 10 September 2020 DOI: 10.1002/joom.1124

RESEARCH ARTICLE

Exploring supplier–supplier innovations within the Toyota supply network: A supply network perspective

Antony Potter1 | Miriam Wilhelm2

1Management Science, Alliance Manchester Business School, University of Abstract Manchester, Manchester, UK This article investigates the development of supplier–supplier innovations 2Faculty of Economics and Business, that occur when two firms that are part of the same supply network University of Groningen, Groningen, The co-patent a new product. This study unravels how the structure of the Netherlands supply network influences each firm's ability to form supplier–supplier Correspondence innovations with other network members. Specifically, we investigate how Antony Potter, Management Science, supplier degree centrality influences the generation of supplier–supplier innova- Alliance Manchester Business School, University of Manchester, Room 3.102, tions, and the extent to which this relationship is moderated by the structural Booth Street West, Manchester, UK, M15 embeddedness of firms in the supply network. Using data from the Toyota supply 6PB. – Email: [email protected] network, the results reveal that a firm's ability to co-develop supplier supplier innovations with other network members dependsonitsnumberoftiesandtheir Handling Editor: Subodha Kumar, directionwithinthesupplynetwork.Although betweenness centrality has no sig- Sriram Narayanan, Fabrizio Salvador nificant moderating effect, closeness centrality, and embeddedness in small world clusters negatively moderate the relationship between supplier degree centrality and supplier–supplier innovations. Additionally, the number of manufacturing plants a firm operates in strengthens the positive effect supplier degree cen- trality has on the development of supplier–supplier innovations.

KEYWORDS betweenness centrality, closeness centrality, degree centrality, small world clusters, supplier– supplier innovation, Toyota Motor Corporation

1 | INTRODUCTION to unravel how the structural characteristics of supply net- works can influence the generation of innovations As internally generated innovations are not sufficient to (Narasimhan & Narayanan, 2013). The majority of studies compete in dynamic markets, firms are seeking new ways in this field have focused on the relationship between sup- to leverage their supply networks to co-develop innova- plier degree centrality and the number of innovations cre- tions (Gao, Xie, & Zhou, 2015; Hong & Hartley, 2011; ated by firms (Chae, Yan, & Yang, 2020; Gao et al., 2015; Narasimhan & Narayanan, 2013). The integration of sup- Narasimhan & Narayanan, 2013). Yet another route also is pliers and customers into internal product development pertinent, and arises when firms in the same supply net- efforts is well documented (Clark & Fujimoto, 1991; Law- work co-develop supplier–supplier innovations, an effort son, Krause, & Potter, 2015; Rost, 2011; Zhou, Zhang, that may particularly depend on a firm's embeddedness in Sheng, Xie, & Bao, 2014), and researchers also have begun the supply network (Hong & Hartley, 2011; Kamath &

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Journal of Operations Management published by Wiley Periodicals LLC. on behalf of the Association for Supply Chain Management, Inc.

J Oper Manag. 2020;66:797–819. wileyonlinelibrary.com/journal/joom 797 798 POTTER AND WILHELM

Liker, 1994; Kim, 2014). With this study, we therefore seek tend to be more capable at generating supplier–supplier to contribute to extant literature on supply network- innovations, but in contrast, firms with fewer down- enabled innovation by asking how the structural character- stream ties to key customers seem to enjoy more success istics of a supply network influence the development of in co-developing supplier–supplier innovations in a col- supplier–supplier innovations. laborative manner. Research using social network theory suggests that Moreover, we unravel how the structural position of the network ties between firms within a supply network firms within the supply network, in terms of their can play an important role during the innovation process betweenness centrality, closeness centrality, and (Bellamy, Ghosh, & Hora, 2014; Gao et al., 2015; Schil- embeddedness in a small world cluster, influences the ling & Phelps, 2007). These network ties can be defined relationship between supplier degree centrality and the as the ties that connect a firm to its upstream suppliers co-development of supplier–supplier innovations. We and downstream customers within a supply network investigate whether betweenness centrality strengthens (Kim, Choi, Yan, & Dooley, 2011). Anecdotal evidence the positive effect of supplier degree centrality on sup- shows that network ties can lead to the diffusion of valu- plier–supplier innovations due to the unique influence of able knowledge and operational practices (Choi, Wu, network brokers that have a large degree of power and Ellram, & Koka, 2002; Dyer & Nobeoka, 2000; control within a supply network. Even if firms in broker Wilhelm, 2011) and thus to enhanced product develop- positions (i.e., with a high betweenness centrality) have ment performance (Hong & Hartley, 2011; Kamath & an important role in managing the flow of materials Liker, 1994). In particular, supplier degree centrality, across supply networks, we do not find evidence that they which measures the number of network ties a firm has enjoy any advantages in terms of using their network ties with other suppliers and customers in the supply net- to generate supplier–supplier innovations. While we work, is often associated with a greater degree of innova- expected that firms with high closeness centrality can use tion by firms (Bellamy et al., 2014; Gao et al., 2015). their network ties to rapidly absorb the latest knowledge, Building upon this literature we anticipate that supplier our results show that the positive relationship between degree centrality has a positive effect on the co- supplier degree centrality and supplier–supplier innova- development of supplier–supplier innovations within a tions is stronger among firms in more remote positions, supply network setting. on the periphery of the supply network (i.e., with a low Furthermore, previous studies of supply network- closeness centrality). enabled innovations also indicate that the structural In line with our expectations, we find that small embeddedness of firms within a supply network can influ- world clusters weaken, rather than strengthen, the rela- ence the way they develop new products (Kim, 2014; tionship between supplier degree centrality and supplier– Narasimhan & Narayanan, 2013). In particular, researchers supplier innovations. This is because firms embedded in often measure the structural position of firms in a supply small world clusters can foster supplier–supplier innova- network according to their betweenness centrality tions with fewer network ties, but firms outside of these (Carnovale & Yeniyurt, 2015; Kim et al., 2011), closeness clusters need a larger number of network ties to absorb centrality (Borgatti & Li, 2009; Carnovale, Yeniyurt, & new knowledge and co-develop innovations within sup- Rogers, 2017), and their embeddedness in small world clus- pliers. Finally, we unravel the geographical complexity of ters (Kito, Brintup, New, & Tsochas, 2014; Sharma, Kumar, the supply network by studying how the location of firms' Yan, Borah, & Adhikary, 2019). Expanding this research, manufacturing plants influences these relationships. we propose that the relationship between supplier degree Thus, we go beyond traditional supply network analysis centrality and the formation of supplier–supplier innova- by adding a geographical dimension that captures the tions can be influenced by the structural position of firms number of manufacturing plants each firm operates within the supply network. within Japan. In our study setting, the number of Within this article, we explore these relationships manufacturing plants a firm operates in Japan helps to using data from firms within Toyota's supply network strengthen the effect supplier degree centrality has on related to their supplier–supplier innovations over a 4- supplier–supplier innovations. year period from 2015 to 2018. We offer further empirical With this evidence, our study contributes to research evidence for the important contribution supplier degree into supply network-enabled innovation in three main centrality makes to the co-development of supplier– ways. First, we build on research by Bellamy et al. (2014) supplier innovations. More specifically, we show that the and Gao et al. (2015) that has explored the relationship number and direction of ties can have differential effects between supplier degree centrality and firm-level innova- on the occurrence of supplier–supplier innovations tions, to determine how supplier degree centrality might between firms across the supply network. Firms that affect the co-development of supplier–supplier innova- manage a large portfolio of upstream ties with suppliers tions. The findings reveal that upstream ties and POTTER AND WILHELM 799 downstream ties are not equally important for creating as an approach in which “…the buyer expects first-tier supplier–supplier innovations across inter-firm bound- suppliers to coordinate their activities, communicate aries. Instead, upstream ties with suppliers generate more directly with each other and make mutual adjustments in opportunities for firms to jointly innovate with other net- component designs without the buyer's direct involve- work members, but downstream ties with customers ment” (p. 45). The result may be more innovations, with have negative effects. Second, we find that a firm's net- fewer delays and higher product quality, because the sup- work position in terms of betweenness centrality, close- pliers share new knowledge that can improve the inter- ness centrality, and embeddedness in a small world face between components and reduce production defects cluster seems to weaken the positive effect supplier (Choi et al., 2002; Choi & Krause, 2006). However, degree centrality has on the generation of supplier– supplier–supplier innovations seldom occur in isolation supplier innovations. Instead, upstream ties with sup- and instead require considerations of the firm's structural pliers are more effective at developing supplier–supplier embeddedness in the overall supply network (Choi & innovations when firms are in remote positions in the Kim, 2008; Kim, 2014; Wilhelm, 2011). Structural periphery of the supply network and outside small world embeddedness refers to how “…a supplier's performance clusters. This finding suggests that supplier–supplier depends on how it environs itself with other companies innovations may evolve in a decentralized manner, (i.e., its suppliers and customers)” (Choi & Kim, 2008, resulting from horizontal network ties that connect p. 5). However, this element of network theory has not remote firms in the periphery of the supply network, been applied systematically to study how a firm's challenging the assumption that innovations among sup- embeddedness in a supply network might influence its pliers are being top-down or centrally coordinated. Third, ability to develop innovations with other suppliers. beyond the structural characteristics of supply networks, firms with more manufacturing plants in Japan appear to have network ties that are more capable of generating 2.2 | Network theory supplier–supplier innovations. By accounting for the den- sity of manufacturing activity in Japan, we introduce a Network theory, originating from sociology literature, geographical component to considerations of how struc- focuses on the ties of different nodes within a network tural characteristics influence supply network-enabled (Burt, 2004; Granovetter, 1973; Uzzi, 1996). Borgatti and innovations. Finally, our post hoc analysis reveals that Halgin (2011) summarize the main characteristics of net- these findings are more pronounced among firms that work theory as follows: First, the position of nodes, their are developing eco-innovations for new green vehicle ties, and the structure of the network explain organiza- architectures in the . tional outcomes, rather than the attributes of the nodes. Second, ties between nodes provide vital conduits for the transfer and diffusion of knowledge within a network. 2 | THEORETICAL BACKGROUND Nodes with more ties and that are centrally positioned in a network receive more knowledge flows than nodes on 2.1 | Supplier–supplier innovations the periphery of the network with only a few connec- tions. Third, a network is both a sociological construct Asanuma (1985) described how Japanese automakers and a mathematical object, such that its structural char- convinced competing suppliers to collaborate on product acteristics can influence how its nodes interact. development and obtain supplier–supplier innovations. Researchers increasingly have applied network theory In a continuation of this tradition, many Japanese auto- to the study of supply networks, often using concepts and makers adopt a two-vendor policy and encourage both measures from social network analysis to depict a supply suppliers to collaborate to design and create new prod- network as the number of firms (nodes), connected ucts, while also offering protections of each party's status together through ties to other members of the supply net- (Kamath & Liker, 1994). Choi et al. (2002) report that work (Borgatti & Li, 2009; Galaskiewicz, 2011; Daimler– also encourages suppliers of instru- Kim, 2014; Leenders & Dolfsma, 2016). These ties are ment panels and seats to jointly undertake NPD activi- based on the flow of materials, components, and modules ties, as part of its buyer-initiated Tech Teams program. between firms within the supply network (Basole, 2016; Supplier–supplier innovations occur when a firm Bellamy et al., 2014; Kim et al., 2011). For this study, we co-develops a patented new product with another organi- focus only on firms that are suppliers to the focal auto- zation within an automaker's supply network. Their prac- maker (Toyota), through its supplier association, because tical relevance is growing, yet research on this topic lags such firms play important roles for the co-development their real-world existence. In a notable exception, Hong of innovations (Aoki & Lennerfors, 2013; Dyer & and Hartley (2011) define supplier–supplier connections Nobeoka, 2000). 800 POTTER AND WILHELM

Research in supply network-enabled innovations has ties with suppliers and downstream ties with customers contributed to enhancing our understanding of how the that the firm has within a supply network (Kim structure of the supply network influences the generation et al., 2011). Building upon previous studies, we expect that of innovations in different industries (Narasimhan & the number of upstream ties (Bellamy et al., 2014; Clark & Narayanan, 2013). However, research has yet to explore Fujimoto, 1991; Cusumano & Takeshi, 1991) and down- how supplier degree centrality affects the development of stream ties (Prahalad & Ramaswamy, 2004; Urban & Von supplier–supplier innovations, and how this relationship is Hippel, 1988) a firm manages within a supply network will influenced by the structural embeddedness of firms within influence the way in which it co-develops supplier– the supply network (Choi & Kim, 2008; Kim, 2014). Con- supplier innovations. In particular, the integration of mul- sequently, we seek to unravel how the relationship tiple components requires large amounts of collaboration between supplier degree centrality and supplier–supplier in product development across the supplier–supplier inter- innovations is moderated by the supplier's betweenness face to ensure the smooth functioning of the new product centrality (Carnovale & Yeniyurt, 2015; Kim et al., 2011), (Hong & Hartley, 2011; Takeishi & Fujimoto, 2001). closeness centrality (Borgatti & Li, 2009; Carnovale Previous studies have shown that when firms have et al., 2017), and embeddedness in small world clusters many upstream ties they will often involve and integrate (Kito et al., 2014; Sharma et al., 2019), as well as the num- their suppliers into the NPD process to benefit from ber of manufacturing plants in Japan. Figure 1 provides an shorter development times, lower development costs, and overview of this conceptual framework and each of our higher product quality (Clark & Fujimoto, 1991; Hand- research hypotheses. field, Ragatz, Petersen, & Monczka, 1999; Lawson et al., 2015). For example, firms that act as system inte- grators typically maintain ties with different suppliers to 3 | HYPOTHESES DEVELOPMENT access new knowledge and foster inter-firm problem solv- ing during the innovation process (Hobday, Davies, & 3.1 | Supplier degree centrality as an Prencipe, 2005; Kim et al., 2011). In a similar vein, firms antecedent of supplier–supplier with multiple downstream ties with customers frequently innovations participate in NPD activities with their key clients, so they have more opportunities to jointly develop new Using network theory, several studies indicate that firms products in a collaborative manner (Prahalad & with more network ties to other organizations within a Ramaswamy, 2004; Urban & Von Hippel, 1988). Building supply network tend to be more innovative (Bellamy on these studies, we predict that supplier degree central- et al., 2014; Gao et al., 2015; Schilling & Phelps, 2007). ity is equally beneficial for developing inter-firm innova- Degree centrality captures the number of network ties a tions with various members of a supply network. firm has with other members in the supply network, and it Upstream and downstream ties that span a supply net- is often regarded as an important antecedent of firm-level work may act as important channels for the formation of innovation outcomes (Ahuja, 2000; Leenders & inter-firm NPD projects and the development of Dolfsma, 2016; Tsai, 2001). For our research context, sup- supplier–supplier innovations. We therefore propose the plier degree centrality refers to the number of upstream following research hypothesis:

Supplier Supplier Betweenness Closeness Centrality Centrality

H2 + H3 +

Supplier H1+ Supplier-Supplier Degree Centrality Innovations

H4 - H5 +

Small World Number of plants Clusters located in Japan FIGURE 1 Conceptual framework POTTER AND WILHELM 801

Hypothesis 1 Supplier degree centrality is positively asso- 3.3 | Moderating effect of supplier ciated with more supplier–supplier innovations. closeness centrality

In the supply network literature, closeness centrality refers to 3.2 | Moderating effect of supplier how close a firm is to all other organizations in a supply net- betweenness centrality work beyond the ones it has direct connects to (Freeman, 1979; Kim et al., 2011). It is therefore often used to The ability to leverage such network ties for the purpose identify the degree to which a firm has the independence and of developing supplier–supplier innovations also depends freedom from the influence of other members of the supply on the firm's structural embeddedness in the supply net- network. Firms with high closeness centrality are often reg- work (Borgatti & Li, 2009; Choi & Hong, 2002; arded as navigators that can quickly reach other organiza- Kim, 2014). A frequently studied measure of a firm's net- tions within the supply network, including those with which work position is its betweenness centrality, which is often they are not directly connected, using relatively few path defined as the number of times a firm acts as a link along lengths within the supply network (Kim et al., 2011; the shortest path of all combinations of pairs of nodes Marsden, 2002). These centrally positioned firms often have within the supply network (Freeman, 1979; Kim the freedom to efficiently absorb knowledge from hard-to- et al., 2011; Marsden, 2002). Notably, firms with high reach parts of the network (Costenbader & Valente, 2003; Sal- betweenness centrality are often characterized as brokers man & Saives, 2005), which they then can use for their own that control flows of materials and products among dif- innovation activities (Leenders & Dolfsma, 2016). ferent organizations throughout a supply network By extension, we argue that the positive effect of sup- (Carnovale & Yeniyurt, 2015; Choi & Wu, 2009). Prior plier degree centrality on the co-development of supplier– research suggests that brokers are often characterized by supplier innovations will be stronger among navigator firms their high betweenness centrality that enables them to that have a high closeness centrality. In particular, due to control knowledge flows between different organizations their central position in the supply network, these navigator within the supply network (Owen-Smith & Powell, 2004). firms can use their upstream and downstream ties more In an innovation setting, we suggest that betweenness effectively for the joint development of supplier–supplier centrality strengthens the positive effect of supplier degree innovations. With their fewer path lengths and shorter sup- centrality on the co-development of supplier–supplier ply chains, they are in a better network position to use their innovations due to the unique role played by brokers. Due network ties to rapidly absorb the latest knowledge, with lit- to their power and prominence within the supply network, tle risk of information distortions (Kim et al., 2011). Naviga- such brokers will have network ties that are more effective tors also have access to knowledge sources outside their at absorbing new knowledge and generating supplier– network ties with their suppliers or customers, which may supplier innovations with a range of stakeholders be particularly beneficial if the new product being designed (Leenders & Dolfsma, 2016). Broker firms with higher relies on other materials, components, and modules in the betweenness centrality accordingly might be able to make supply network (Takeishi & Fujimoto, 2001). Firms in more more effective use of their network ties as they will be able remote, peripheral positions in the supply network instead to use their power and control within the supply network may face greater difficulties using their network ties to to encourage suppliers and customers with complemen- effectively generate supplier–supplier innovations, due to tary technologies to co-develop innovations with them. their limited and restricted access to knowledge from differ- For example, Corporation, an important ent network partners (Carnovale, Rogers, & Yeniyurt, 2016; broker in the Toyota supply network has successfully Schilling & Phelps, 2007). Building upon these arguments, undertaken multiple inter-firm innovation projects with we put forward the below research hypothesis: their suppliers and customers that rely on complementary technologies. In contrast, firms that are not brokers likely Hypothesis 3 Supplier closeness centrality positively do not have reputations as important network partners moderates the relationship between supplier degree though, so they might not be able to exert the necessary centrality and supplier–supplier innovations. control to leverage their network ties to foster supplier– supplier innovations (Carnovale & Yeniyurt, 2015). We therefore propose the following hypothesis: 3.4 | Moderating effect of supplier embeddedness in small world clusters Hypothesis 2 Supplier betweenness centrality positively moderates the relationship between supplier degree Rather than clearly defined supplier tiers within a pyra- centrality and supplier–supplier innovations. mid structure, the Toyota supply network contains 802 POTTER AND WILHELM multiple, small world clusters (Kito et al., 2014), such locations. Thus, beyond the traditional structural char- that the firms in these clusters have more links con- acteristics of the supply network, we also add a - necting them to one another than to other firms in the graphical dimension that captures the density of network (Schilling & Phelps, 2007). That is, firms within manufacturing plants that each firm operates within small world clusters have multiple ties among themselves Japan. Although globalization has encouraged many but few ties with organizations outside the cluster that firms to establish manufacturing plants in low-cost still constitute the wider supply network (Sharma locations and emerging markets, Japanese automotive et al., 2019; Watts & Strogatz, 1998). The ties of a firm component manufacturers also have retained and with other members of a small world cluster help facili- expanded their manufacturing plants within Japan tate efficient knowledge sharing, but ties to more periph- (Aoki & Lennerfors, 2013; Dyer & Nobeoka, 2000; Pot- eral members in the supply network grant access to new ter & Graham, 2018). In fact, the majority of first-tier knowledge from outside the cluster (Galaskiewicz, 2011; suppliers (78%) and second-tier suppliers (65%) in the Schilling & Phelps, 2007; Sharma et al., 2019). For Toyota supply network are located in Japan (Kito instance, the average clustering coefficient measure indi- et al., 2014), and many of these firms manage large cates the extent to which a firm clusters with neighboring domestic networks of manufacturing plants. In partic- firms, into a tightly knit group that contains multiple ties ular, maintaining a large number of manufacturing (Basole, 2016; Sharma et al., 2019). plants within Japan helps to foster greater synergies However, a firm's embeddedness in a small world clus- with local suppliers related to alternative fuel technol- ter actually might weaken, rather than strengthen, the rela- ogies (Belderbos, Cassiman, Faems, Leten, & van tionship between supplier degree centrality and supplier– Looy, 2014; Potter & Graham, 2018). Thus, local supplier innovations. Firms within small world clusters manufacturing plants may have a more strategic role, already benefit from intensified knowledge flows, so they reflecting the typically tight integration of manufactur- might require only a few network ties to absorb new knowl- ing and research capabilities through local network edge and co-develop supplier–supplier innovations (Dyer & ties with the automaker (Aoki & Lennerfors, 2013; Nobeoka, 2000; Sharma et al., 2019). In contrast, firms that Morgan & Liker, 2006). have not joined small world clusters need a lot of network We contend that the number of manufacturing plants ties to absorb all the new knowledge they need to jointly a firm operates in Japan will help to strengthen the posi- create supplier–supplier innovations. Yet these firms also tive effect supplier degree centrality has on the formation might achieve greater freedom to leverage their upstream of supplier–supplier innovations. In particular, firms and downstream ties to foster innovations with different with a large Japanese manufacturing base are able to use organizations from other parts of the supply network. In their extensive manufacturing capabilities to help this sense, firms enjoy the independence and autonomy to develop the engineering and research capabilities of local choose the most creative supplier or innovative customer suppliers as well (Dyer & Nobeoka, 2000; Lawson with which to design new products, without being confined et al., 2015). Large automotive component manufacturers to the few members of the small world cluster. Therefore, even operate so-called mother plants (i.e., lead factories) we developed the following research hypothesis. and showcase facilities in Japan that maintain a reposi- tory of the firm's manufacturing and research knowhow, Hypothesis 4 Supplier embeddedness in a small world which is often used to help upgrade local suppliers’ R&D cluster negatively moderates the relationship between capabilities (Cheng, Farooq, & Johansen, 2015; Dyer & supplier degree centrality and supplier–supplier Nobeoka, 2000; Ferdows et al., 2016). Firms in the Japa- innovations. nese automotive industry with multiple domestic manufacturing plants also tend to engage in personnel exchanges with suppliers and customers, to share new 3.5 | Moderating effect of the number of knowledge, train workers, experiment with prototypes, manufacturing plants in Japan and jointly invest in R&D, which helps to ensure that their network ties are used to develop supplier–supplier Network centrality metrics often treat the firm as a sin- innovations (Aoki & Lennerfors, 2013; Dyer & gle node, thereby oversimplify the real-life complexity Nobeoka, 2000; Todo, Matous, & Inoue, 2016). Accord- of supply networks. The literature on international ingly, we anticipate that firms with a large manufactur- manufacturing networks (Ferdows, Vereecke, & ing presence in Japan possess local network ties that are Meyer, 2016; Rudberg & Olhager, 2003) has pointed more effective at knowledge sharing and co-developing out that a firm is usually comprised of multiple facili- supplier–supplier innovations. This leads us to the fol- ties that are spread over different geographical lowing research hypothesis. POTTER AND WILHELM 803

Hypothesis 5 The number of manufacturing plants in the global automotive industry. It contains information located in Japan positively moderates the relation- about 40,000 firms, making it one of the most detailed ship between supplier degree centrality and supplier– databases of automakers and their suppliers. Kito supplier innovations. et al. (2014) also used the Marklines database to establish the detailed structure of the Toyota supply network. We also cross-referenced the data on network ties with other 4 | RESEARCH METHODOLOGY secondary data sources such as the S&P Capital IQ data- base of supplier and customer ties in the automotive 4.1 | Research context: Toyota supply industry. Finally, we gather data from company websites network to record the number of plants in Japan owned by each firm in the Toyota supply network. Increasing trends toward collaboration between Japanese automakers and suppliers during innovation processes have led to a substantial rise in the number of co-patents 4.2 | Measures within supply networks (Borgstedt, Neyer, & Schewe, 2017; Konno, 2007; Potter & Graham, 2018). For 4.2.1 | Dependent variable: Supplier– this study, we consider 219 firms within the Toyota sup- supplier innovations ply network that manufacture the materials, components, and modules that go into Toyota's vehicles. Based on our Patents are a widely used measure of the innovation analysis, all these firms are first-tier suppliers that supply performance of different organizations (Artz, Norman, parts to Toyota and are members of its supplier associa- Hatfield, & Cardinal, 2010; Liu, Yeung, Lo, & tion (Kyohokai). The Toyota supplier association aims to Cheng, 2014). To capture the degree of inter-firm inno- enhance members’ cooperation and knowledge sharing vation, an alternative measure focuses on the total num- with Toyota and one another, through regular meetings ber of co-patents a firm generates during a specific and workshops (Toyota, 2016). Members of Kyohokai are period (Kim & Song, 2007; Potter & Graham, 2018). Toyota's most important material suppliers, accounting Within this study, we use patent data from the Japan for more than 90% of Toyota's purchasing (Sako, 1996). Platform for Patent Information (JPPI) database that is We therefore focus our analysis on the product innova- managed by the Japan Patent Office. Specifically, we use tions developed by material suppliers within the this database to collect patent data on the number of co- Kyohokai part of the supplier association. However, due patents registered by different firms within the Toyota to data limitations, we are unable to incorporate firms in supply network. We began by measuring how many co- the lower tiers of the supply network as this is beyond patents each firm in the Toyota supply network had reg- the scope of our study (Kito et al., 2014). istered that also included one other Toyota supplier as a

We use secondary data sources to measure the vari- co-assignee from 2015 (t0) to 2018 (t+4). We only focus ables. The Japan Patent Office (JPO) is one of the main on co-patents with each firm and another Toyota sup- sources of data we use in our study and we use its patent plier as registered co-assignees. In other words, we mea- data to collect information on the number of co-patents sure supplier–supplier innovations by focusing exclusively recorded by each firm and other Toyota suppliers within on the co-patents that are registered by two suppliers the supply network over a 4-year period from 2015 to within the Toyota supply network. Therefore, we do not

2018 (i.e., t0 to t+4). By studying this period, we can evalu- include co-patents that are registered with the automaker ate the direction of causality and investigate whether sup- as this is beyond the scope of our study. Then we cross- plier degree centrality at t0 is an antecedent of later referenced these co-patents with other secondary data supplier–supplier innovations. In addition, this time sources, checking that they appeared in the Derwent Inno- period corresponds to the 4 years following the launch of vation Index and Google patents database. We recorded the fourth-generation Toyota Prius and the total, such that our measure of supplier–supplier inno- the vehicle that is a hydrogen fuel cell vehi- vation reflects the total number of other Toyota suppliers cle that began mass production in 2015. The measures of each firm in the Toyota supply network had co-patented supplier degree centrality, betweenness centrality, close- with during the 4-year study window. Overall, we find that ness centrality, small world clusters, number of plants in firms often develop supplier–supplier innovations with

Japan, and control variables all refer to 2015 (t0), so that their immediate suppliers as they account for 20% of all we can make inferences about causality. To measure the supplier–supplier innovations in the supply network, centrality and small world cluster variables, we use the whereas immediate customers only constitute 13%. In Marklines database, which records transactions by firms total, therefore, approximately 33% of all supplier–supplier 804 POTTER AND WILHELM innovations involve the firm collaborating with its immedi- paths between other nodes in the network (Carnovale & ate suppliers or customers to jointly design and patent an Yeniyurt, 2015). Following Basole (2016), betweenness innovative new product. This also indicates that the major- centrality is measured as follows: ity of supplier–supplier innovations represent collabora- X tions between firms and the other members of the Toyota BC = P i q,j≠i q,i,j supply network with whom they do not have network ties.

where pq,i,j is the proportion of shortest paths that occur 4.2.2 | Explanatory variables: Supplier between q and j that occur through i in the supply net- indegree and outdegree centrality work. In a similar manner to Basole (2016) we use our data on network ties together with Gephi network soft- Similar to Kito et al. (2014), we identify the company ware1 to measure the betweenness centrality of each firm name of each firm in the Marklines database, then within the Toyota supply network. record the total number of upstream ties and down- stream ties it has in the Toyota supply network. With this information, we developed a supplier indegree cen- 4.2.4 | Moderator: Closeness centrality trality variable that measures the total number of sup- pliers from which each firm sources materials, Closeness centrality is often defined as the sum of dis- components, parts, and modules, within the Toyota tances from all other firms within the network, in which supply network. For example, supplier indegree cen- distance from one firm to another is regarded as the trality is the number of direct ties that flow to a firm length (in links) of the shortest path (Carnovale (node) from its suppliers in the supply network. et al., 2017). According to Basole (2016) and Carnovale Adapting the approach used by Kim et al. (2011) and et al. (2017), closeness centrality can be expressed as: Basole (2016), this can be defined as: X n X CC = dp,p i i =1 i j DCi = nij j

In the above equation, d(pi,pj) represents the number where nij is equal to 1 if there is a direct tie that flows to of ties in the shortest path that connect pi and pj.Aswe ni from nj, and equal to 0 otherwise (Freeman, 1979; Kim are studying a supply network with directional ties et al., 2011). between firms, we use the harmonic version of closeness Then we created a separate supplier outdegree cen- centrality. Specifically, harmonic closeness centrality cap- trality variable that indicates the number of customers tures the sum of the inverted distances and is expressed in the Toyota supply network to which each firm sells in the following equation: its products (Basole, 2016; Kim et al., 2011). With this X detailed data, our empirical analysis spans two levels, 1 HCCi = and we test our hypotheses using data about supplier ≠ i j dpi,pj indegree centrality and supplier outdegree centrality separately. Therefore, we can evaluate whether our results only apply to specific interactions between a Consequently, firms with a high harmonic closeness firm and its Toyota suppliers (i.e., supplier indegree centrality will be centrally positioned, whereas firms with centrality) or its customers within the Toyota supply a low harmonic closeness centrality will be in remote network (i.e., supplier outdegree centrality). As we are positions in the periphery of the supply network interested in the network ties among the firms in the (Carnovale et al., 2016). Toyotasupplynetwork,wedonotincludedataonthe ties between each firm and the focal automaker (Toyota) for the network measures used in this study. 4.2.5 | Moderator: Supplier embeddedness in a small world cluster

4.2.3 | Moderator: Betweenness Small world clusters were originally proposed by Watts centrality and Strogatz (1998) and can be identified when a firm has a high average clustering coefficient within a supply Previous studies have measured betweenness centrality network (Sharma et al., 2019). Specifically, the average by focusing on how often a firm appears on the shortest clustering coefficient can be used to measure the extent POTTER AND WILHELM 805 to which a firm's network partners form a cluster within suppliers and the products they manufacture for the the supply network (Basole, 2016; Sharma et al., 2019). automaker to identify whether each firm manufactures a For example, Kito et al. (2014) use average clustering modular system, sub-system, assembly, or sub-assembly coefficients to help analyze small world clusters within (Toyota, 2016). In a database of all components provided the Toyota supply network. Similar to Basole (2016), our by each firm to Toyota, we recorded a value of 1 if the measure of the average clustering coefficient is expressed description of a component included a keyword related in the following manner: to a module system, such as module, sub-assembly, assembly, sub-system, or system. We also cross- np referenced these data with the Marklines database and ACCi = ðÞ− = ni ni 1 2 company websites to confirm that the firm supplied mod- ule systems. Second, using data from Toyota's (2016) list

In the above equation np represents the number of of suppliers and components, we determined if each firm ties that occur among all ni direct partners p of each firm provided parts, components, or products for Toyota's i (Basole, 2016). In particular, a high average clustering engine system (1 = yes). coefficient indicates that a firm is closely tied to its adja- Automakers and suppliers have invested in green cent firms within a small world cluster in comparison to technologies, so we also checked whether the results a situation where all the ties are randomly distributed might be more pronounced among green technology sup- (Sharma et al., 2019). pliers. Similar to Borgstedt et al. (2017) and Potter and Graham (2018), we used data from the JPO and searched patent titles, abstracts, and codes registered by different 4.2.6 | Moderator: Number of plants in firms to identify if they had developed any eco- Japan innovations used in vehicles that were powered by hybrid, electric, or hydrogen fuel cell powertrains during We used data gathered from company websites to iden- a 4-year period. This dichotomous variable, Green Tech- tify the addresses of the individual manufacturing plants nology Supplier (GTS), measures whether a firm had pat- that each firm operates in 2015 (t0). We then determined ented an eco-innovation in green technology (1 = yes). the number of plants in Japan owned by each firm in the To capture the effects of distinct keiretsu networks of Toyota supply network. We cross-referenced these data interlocking ownership in Japan, we created another with secondary data sources such as Marklines, S&P Cap- dichotomous variable, Toyota investment, which mea- ital IQ, and company annual reports, to verify that the sures whether a firm had received financial investment firm owned and operated each manufacturing plant. from the automaker. To address supplier–supplier gover- nance, we also used a dichotomous variable called sup- plier investment that captures whether another Toyota 4.2.7 | Control variables supplier had invested financially in each firm in the sup- ply network (coded 1). Both these measures reflect data We controlled for organizational factors that might influ- from the S&P Capital IQ database, Toyota's financial ence the frequency of co-patenting (Artz et al., 2010). Firm accounts, the suppliers’ annual reports, and company size refers to the total number of employees working for websites. Long-term relationships influence the develop- each organization, obtained from the S&P Capital IQ data- ment of innovations in inter-firm collaborations, so we base and company websites (log). Firm age is the number used data from Sako (1996) that identify whether a firm of years since the firm was established (log). For interna- was a member of the Toyota supply network in 1980, tional governance, we capture whether the firm is interna- then construct a dichotomous variable to represent its tionally listed with headquarters outside Japan (1 = yes). long-term relationship with Toyota (coded 1 for yes). Overall, the majority of firms (93.62%) are Japanese owned Finally, the degree of competition varies across compo- with a domestic headquarters, and only a small number of nent markets in the automotive industry. Therefore, we Toyota's suppliers are internationally listed. Previous stud- measured market competition as the number of major ies indicate that R&D centers influence innovation pro- competitors that produced the products manufactured by cesses, so we measured the number of research centers, the firm, using data from automotivenews.com. institutes, and laboratories owned by the firm (log). Two other variables capture different product charac- teristics. First, Hong and Hartley (2011) and Salvador and 4.3 | Empirical analysis Villena (2013) demonstrate that module systems have a significant effect on how firms develop innovations. We The systematic investigation of the data relied on multi- used detailed data from a list, published by Toyota, of variate ordinary least square (OLS) regression techniques 806 POTTER AND WILHELM

(Woolridge, 2016). As mentioned, the empirical analysis The interaction plot affirms the positive relationship proceeds at two levels in the Toyota supply network, so between supplier indegree centrality and supplier– we used a two-stage approach, by testing the hypotheses supplier innovations only occur when there is a low with data about supplier indegree centrality, then repeat- degree of closeness centrality (Figure 2). ing the analysis with information gathered about supplier Regarding the moderating effect of small world clus- outdegree centrality. The Variance Inflation Factors ters, in line with our expectations, we find that they exert (VIF) statistics indicate that multicollinearity is not a a negative moderating effect by weakening the relation- concern; we provide a correlation matrix in Table 1. Fol- ship between supplier indegree centrality and supplier– lowing guidelines by Ketokivi and McIntosh (2017) and supplier innovations (B = −0.787; p < .004). The results Woolridge (2016), we check for endogeneity using two- from the moderation analysis identify that supplier inde- stage least squares (2SLS) regression with instrumental gree centrality has no statistically significant effect on variables. In addition, we also test whether our results supplier–supplier innovations when firms are located are more pronounced among firms developing eco- within small world clusters (B = −2.207; p < .113). In innovations for new green vehicle architectures contrast, when firms are positioned outside of small (Appendix).2 world clusters a significant moderating effect occurs that strengthens the relationship between supplier indegree centrality and the co-development of innovations across 5 | EMPIRICAL RESULTS supplier–supplier boundaries (B = 3.461; p < .001). This moderating effect is further evident in the interaction 5.1 | Supplier indegree centrality: Main plot in Figure 3 where the positive relationship between and moderator effects supplier indegree centrality and supplier–supplier inno- vations emerges only if the level of small world clustering In Table 2, we present the empirical findings in sequen- is low. tial order: Models 1–4 reveal the effects of supplier inde- Finally, the results in Model 4 of Table 2 demonstrate gree centrality, whereas Models 5–7 pertain to the that the number of manufacturing plants in Japan has a contributions of supplier outdegree centrality. Model positive moderating effect on the ability of supplier inde- 2 offers support for the positive effect of supplier indegree gree centrality to generate supplier–supplier innovations centrality on supplier–supplier innovations (B = 0.470; (B = 0.412; p < .000). In the moderation analysis, firms p < .000), and Model 4 indicates that betweenness cen- with many manufacturing plants in Japan experience a trality has no significant moderating effect on this rela- positive and significant relationship between supplier tionship (B = −0.219; p = .132). Contrary to our indegree centrality and supplier–supplier innovations expectations, closeness centrality had a significant nega- (B = 1.421; p < .048), but no significant relationship tive moderating effect on the relationship between sup- exists for firms with a small number of manufacturing plier indegree centrality and supplier–supplier plants in Japan (B = −0.167; p = .809). To illustrate this innovations (B = −0.571; p < .017). During our initial finding, the interaction plot in Figure 4 reveals a positive moderation analysis, we began by creating equations for relationship only when firms have many manufacturing each relationship at −1 standard deviation (low) and +1 plants in Japan. standard deviation (high) values of the explanatory and moderator variables to investigate the moderating effects using simple slope statistics. Next, we used conditional 5.2 | Supplier outdegree centrality: Main interaction plots to undertake a more detailed examina- and moderator effects tion of the relationships at the 10th percentile (low) and 90th percentile (high) values of the explanatory variable, Supplier outdegree centrality is not significantly associ- together with the moderator values at the 25th percentile ated with supplier–supplier innovation in Model 5 (low) and 75th percentile (high) values. This approach (B = −0.105; p = .109), though it seemingly generates a helps us to illustrate the nature of the relationships significant negative effect in Model 6 (B = −0.212; within the interaction plots without the potential influ- p < .001). Whereas supplier indegree centrality appears ence of outlier firms. When firms have high closeness to be a positive antecedent of supplier–supplier innova- centrality, we find no significant relationship between tions, our results suggest supplier outdegree centrality supplier indegree centrality and supplier–supplier inno- has a negative effect. We next check for moderating vations (B = −0.358; p = .610), but if their closeness cen- effects (Model 7, Table 2) but do not find any evidence of trality is low, a significant positive relationship emerges, significant moderation by betweenness centrality albeit at a 90% significance level (B = 1.612; p < .071). (B = 0.077; p = .522), closeness centrality (B = 0.218; POTTER

TABLE 1 Correlation matrix

Standard AND

Variables Min Max Mean deviation 1 23456789101112131415161718 WILHELM

1. Firm size 21.000 320, 13,814.292 36,558.931 1.000 725.000 2. Firm age 4.000 162.000 75.411 26.158 0.041 1.000 (0.544) 3. International 0.000 1.000 0.064 0.245 −0.017 0.016 1.000 governance (0.809) (0.811) 4. R&D centers 0.000 15.000 0.950 2.405 0.306*** 0.022 0.249*** 1.000 (0.000) (0.745) (0.000) 5. Module system 0.000 1.000 0.073 0.261 0.202** 0.029 0.070 0.243*** 1.000 (0.003) (0.674) (0.302) (0.000) † 6. Engine system 0.000 1.000 0.146 0.354 −0.056 0.137* −0.002 −0.064 −0.116 1.000 (0.414) (0.042) (0.972) (0.343) (0.086) † 7. Green 0.000 1.000 0.648 0.479 0.357*** −0.054 0.114 0.328*** 0.133* −0.074 1.000 technology (0.000) (0.431) (0.092) (0.000) (0.049) (0.273) supplier † † † 8. Toyota 0.000 1.000 0.192 0.395 0.208** −0.082 −0.127 −0.063 0.130 −0.037 0.116 1.000 investment (0.002) (0.228) (0.060) (0.354) (0.054) (0.583) (0.087) 9. Supplier 0.000 1.000 0.292 0.456 0.178** −0.102 0.037 −0.021 0.090 −0.038 0.137* 0.274*** 1.000 investment (0.008) (0.134) (0.583) (0.758) (0.186) (0.572) (0.043) (0.000) † † 10. Long-term 0.000 1.000 0.233 0.424 0.131 −0.042 0.033 −0.003 −0.030 −0.075 0.225** 0.116 0.074 1.000 relationship (0.054) (0.536) (0.631) (0.962) (0.657) (0.269) (0.001) (0.087) (0.279) 11. Market 1.00 12.000 2.269 2.168 0.167* 0.110 0.189** 0.226** 0.200** 0.068 0.218** 0.245*** 0.105 0.046 1.000 competition (0.013) (0.104) (0.005) (0.001) (0.004) (0.316) (0.001) (0.000) (0.122) (0.499) 12. Supplier 0.000 139.000 6.132 15.127 0.288*** −0.034 0.055 0.246*** 0.135* 0.032 0.209** 0.280*** 0.049 −0.014 0.241*** 1.000 Indegree (0.000) (0.618) (0.421) (0.000) (0.046) (0.643) (0.002) (0.000) (0.471) (0.835) (0.000) centrality † † † 13. Supplier 0.000 26.000 2.986 4.119 −0.172* −0.025 −0.126 −0.183** 0.091 0.005 −0.226** 0.112 0.031 −0.030 −0.130 −0.134* 1.000 Outdegree (0.011) (0.711) (0.062) (0.006) (0.182) (0.947) (0.001) (0.099) (0.643) (0.662) (0.055) (0.048) centrality † 14. Betweenness 0.000 6,457.616 241.489 719.844 0.163* −0.736 −0.004 0.224** 0.149* 0.003 0.091 0.200** 0.054 0.048 0.149* 0.729*** 0.126 1.000 centrality (0.016) (0.278) (0.952) (0.001) (0.028) (0.964) (0.182) (0.003) (0.429) (0.478) (0.028) (0.000) (0.064) 15. Closeness 0.000 0.848 0.357 0.184 0.106 −0.021 0.022 0.091 0.179** 0.071 0.055 0.280*** 0.086 0.024 0.178** 0.457*** 0.426*** 0.390*** 1.000 centrality (0.119) (0.760) (0.745) (0.180) (0.008) (0.296) (0.417) (0.000) (0.204) (0.721) (0.008) (0.000) (0.000) (0.000) † † 16. Small world 0.000 1.000 0.240 0.173 −0.132 0.107 0.063 0.002 −0.032 0.161* −0.011 0.038 −0.069 0.041 0.023 −0.179** 0.114 −0.157* 0.201** 1.000 clusters (0.051) (0.114) (0.354) (0.973) (0.634) (0.017) (0.877) (0.576) (0.311) (0.551) (0.739) (0.008) (0.091) (0.020) (0.003) 17. Number of 0.000 39.000 5.500 5.900 0.386*** 0.006 0.001 0.339*** 0.091 −0.150* 0.272*** 0.008 0.079 0.168* 0.058 0.248*** −0.044 0.341*** 0.157* −0.071 1.000 plants located in (0.000) (0.929) (0.989) (0.000) (0.182) (0.026) (0.000) (0.911) (0.244) (0.013) (0.391) (0.000) (0.516) (0.000) (0.020) (0.294) Japan † † 18. Supplier– 0.000 24.000 1.671 3.281 0.414*** −0.021 −0.071 0.248*** 0.162* −0.037 0.245*** 0.155* 0.098 0.125 0.180** 0.542*** −0.171* 0.541*** 0.192** −0.131 0.487*** 1.000 supplier (0.000) (0.757) (0.298) (0.000) (0.016) (0.582) (0.000) (0.022) (0.147) (0.066) (0.008) (0.000) (0.011) (0.000) (0.004) (0.054) (0.000) innovations

Note: In addition, our results indicate that multicollinearity is not a concern, as the Variance Inflation Factor (VIF) average (mean) score is 1.59, which is below the threshold value of 10. *** 807 † p < .001; ** p < .01; * p < .05; p < 0.10. 808 TABLE 2 The antecedent and moderating factors that influence supplier–supplier innovations (2015–2018)

Variables Model 1 Model 2 Model 3 Model 4 Variables Model 5 Model 6 Model 7 Control variables Control variables Firm size 0.309*** (0.000) 0.228*** (0.000) 0.176** (0.004) 0.266*** (0.000) Firm size 0.294*** (0.000) 0.186** (0.003) 0.193** (0.002) Firm age −0.037 (0.561) −0.013 (0.817) −0.013 (0.798) −0.016 (0.744) Firm age −0.037 (0.555) −0.06 (0.912) −0.005 (0.924) † † † † † International −0.124 (0.059) −0.137* (0.018) −0.102 (0.058) −0.089 (0.080) International −0.131* (0.046) −0.101 (0.060) −0.090 (0.090) governance governance † † R&D centers 0.139 (0.053) 0.065 (0.308) −0.022 (0.714) −0.057 (0.319) R&D centers 0.131 (0.067) −0.035 (0.571) −0.064 (0.293) Module system 0.048 (0.465) 0.043 (0.452) 0.052 (0.332) 0.077 (0.135) Module system 0.065 (0.322) 0.056 (0.299) 0.086 (0.123) Engine system 0.004 (0.953) −0.024 (0.672) 0.012 (0.818) 0.033 (0.488) Engine system 0.01 (0.932) 0.013 (0.804) 0.012 (0.812) Green technology 0.054 (0.440) 0.022 (0.721) 0.018 (0.759) 0.051 (0.346) Green technology 0.037 (0.603) 0.020 (0.734) 0.032 (0.595) supplier supplier Toyota investment 0.036 (0.604) −0.081 (0.196) −0.036 (0.536) −0.078 (0.159) Toyota investment 0.051 (0.465) −0.001 (0.986) −0.000 (0.999) Supplier investment 0.012 (0.859) 0.042 (0.462) 0.026 (0.627) 0.030 (0.555) Supplier investment 0.015 (0.811) 0.015 (0.784) −0.007 (0.891) † Long-term relationship 0.068 (0.289) 0.105 (0.065) 0.051 (0.331) 0.035 (0.483) Long-term relationship 0.070 (0.273) 0.029 (0.583) 0.044 (0.395) Market competition 0.090 (0.190) 0.040 (0.511) 0.061 (0.279) 0.069 (0.184) Market competition 0.078 (0.257) 0.045 (0.430) 0.046 (0.412) Main effect Main effect † Supplier indegree – 0.470*** (0.000) 0.307*** (0.000) 0.191 (0.363) Supplier outdegree −0.105 (0.109) −0.212*** (0.001) −0.385 (0.084) centrality centrality Moderator effects Moderator effects Betweenness centrality ––0.238** (0.003) 0.149 (0.291) Betweenness centrality – 0.425*** (0.000) 0.446*** (0.000) † Closeness centrality ––−0.121 (0.050) −0.203** (0.009) Closeness centrality – 0.041 (0.528) 0.200 (0.407) Small world clusters ––0.036 (0.517) −0.291* (0.018) Small world clusters – 0.000 (0.997) 0.060 (0.449) Number of plants ––0.269*** (0.000) 0.178** (0.003) Number of plants – 0.249*** (0.000) 0.161* (0.014) located in Japan located in Japan Interaction effects Interaction effects Supplier indegree –––−0.219 (0.132) Supplier outdegree –– 0.077 (0.522) centrality × centrality × Betweenness Betweenness centrality centrality POTTER Supplier indegree –––−0.571* (0.017) Supplier outdegree –– 0.218 (0.450) centrality × Closeness centrality ×

centrality Closeness centrality AND

Supplier indegree –––−0.787** (0.004) Supplier outdegree –– 0.081 (0.302) WILHELM centrality × Small centrality × Small world clusters world clusters POTTER AND WILHELM 809 0.222*** (0.001) − efficients with a variable called path 219 219 219 5.23*** 12.49*** 10.97*** 0.189 0.458 0.478 –– F Number × 2 R centrality of plants located in Japan FIGURE 2 The moderating effect of closeness centrality N 0.412*** (0.000) Supplier outdegree < .10. p † < .05; p < .01; * p supplier innovations during the 4-year period from 2015 to 2018. Additionally, Sharma et al. (2019) suggest that small world clusters can some- – < .001; ** p 219 219 219 219 0.183 0.366 0.460 0.544 Adjusted ––– 5.43*** 11.46*** 12.62*** 13.99*** Overall model F Number

(Continued) FIGURE 3 The moderating effect of small world clusters × 2 R p = .450), or small world clusters (B = .081; p = .302). Dependent variable: Number of supplier centrality of plants located in Japan Only the number of plants located in Japan has a signifi- VariablesSupplier indegree Model 1 Model 2 Model 3 Model 4 Variables Model 5 Model 6 Model 7 N Overall model Adjusted TABLE 2 Note: times be detected by their path lengths within the supply network. Therefore, in a separate analysis, we replaced our measure of average clustering co lengths and found similar results. *** cant, negative moderating effect on the relationship 810 POTTER AND WILHELM

FIGURE 4 The moderating effect of the number of plants FIGURE 5 The moderating effect of the number of plants located in Japan (supplier's indegree centrality) located in Japan (supplier's outdegree centrality) between supplier outdegree centrality and supplier– supplier innovations (B = −0.222; p < .001). The results size and scope of its external supply base. For example, from the moderation analysis reveal that when firms firms with a high external indegree centrality likely manu- have many manufacturing plants located within Japan a facture products that require them to integrate materials negative relationship emerges between supplier out- from external sources, together with different components degree centrality and the intensity of supplier–supplier from suppliers within the supply network. Furthermore, innovations (B = −2.200; p < .009). By comparison, when firms that know how to source from different external sup- firms have a small number of manufacturing plants in pliers likely have sufficient purchasing capabilities to man- Japan there is no moderating effect (B = −0.326; age their upstream ties with suppliers within the supply p = .655). The interaction plot in Figure 5 confirms that network too. A second instrumental variable, external out- the number of plants located in Japan has a negative degree dependence, instead captures the percentage of moderating effect on how supplier outdegree centrality product ties with external customers, outside the Toyota influences supplier–supplier innovations. supply network. By taking a percentage measure, we can capture the relative importance of external customers to each firm. With a large external customer base, firms 5.3 | Endogeneity likely adopt an external orientation in sourcing their mate- rials, components, and parts from a wider range of sup- 5.3.1 | Supplier indegree centrality pliers so that they can meet the unique demands of different automakers (Kim et al., 2011). Therefore, we In accordance with recommendations by Ketokivi and expect that external outdegree dependence will be associ- McIntosh (2017) and Woolridge (2016), we used a 2SLS ated with externally oriented firms that source from rela- regression with instrumental variables to check for endo- tively fewer suppliers within the supply network. geneity. The instrumental variable, external indegree cen- Furthermore, as external indegree centrality and external trality, measures the total number of ties each firm has to outdegree dependence help to capture the orientation of suppliers outside the Toyota supply network. Kim firms towards external partners they are unlikely to influ- et al. (2011) and Kito et al. (2014) show that the way a firm ence the co-development of supplier–supplier innovations manages its ties in a supply network often depends on the within the supply network. POTTER AND WILHELM 811

TABLE 3 Investigating endogeneity using 2SLS regression with instrumental variables

Model 2 supplier– Model 1 supplier supplier Model 3 supplier Model 4 supplier– Indegree centrality innovations Outdegree supplier Variables (OLS) (2SLS) Variables centrality (OLS) innovations (2SLS) Control variables Control variables † Firm size 0.117 (0.075) 0.817*** (0.000) Firm size −0.014 (0.802) 0.938*** (0.000) Firm age −0.089 (0.128) −0.060 (0.740) Firm age 0.018 (0.715) −0.123 (0.541) International −0.079 (0.219) −1.795** (0.017) International 0.013 (0.805) −1.821* (0.032) governance governance † R&D centers 0.139* (0.037) 0.268 (0.211) R&D centers −0.026 (0.646) 0.412 (0.071) Module system −0.028 (0.643) 0.558 (0.430) Module system 0.106* (0.042) 0.981 (0.231) Engine system 0.051 (0.384) −0.161 (0.750) Engine system 0.055 (0.274) 0.060 (0.915) Green technology 0.030 (0.654) 0.202 (0.631) Green technology −0.042 (0.459) 0.167 (0.727) supplier supplier † Toyota investment 0.188** (0.004) −0.455 (0.422) Toyota investment 0.091 (0.096) 0.509 (0.367) Supplier investment −0.048 (0.427) 0.255 (0.532) Supplier investment 0.040 (0.438) 0.132 (0.772) † Long-term relationship −0.049 (0.406) 0.747 (0.085) Long-term 0.117** (0.023) 0.553 (0.248) relationship † Market competition 0.112 (0.079) 0.167 (0.400) Market competition −0.066 (0.221) 0.226 (0.303) Explanatory variable Explanatory variable † Supplier indegree – 1.197** (0.008) Supplier outdegree – −0.594 (0.056) centrality centrality Instrumental variables Instrumental variables External indegree 0.278*** (0.000) – Internal outdegree 0.715*** (0.000) – centrality dependence External outdegree −0.259*** (0.000) – Japan −0.127* (0.023) – dependence manufacturing dependence R2 0.348 0.392 R2 0.523 0.229 N 219 219 N 219 219

Note: Supplier–supplier innovations is the dependent variable in Models 1, 3, 4, and 6. Supplier indegree centrality is the dependent variable in Model 2 and Supplier outdegree centrality is the dependent variable in Model 5. The measure of supplier–supplier innovations focuses on † the number of supplier–supplier innovations during the 4-year period from 2015 to 2018. ***p < .001; **p < .01; * p < .05; p < .10.

To be valid instrumental variables, external indegree instrumental variables by applying over-identifying centrality and external outdegree dependence must relate restriction tests. Although it is not possible to test the sta- significantly to supplier indegree centrality. Therefore, in tistical independence of the instrumental variables from the first stage of the 2SLS estimation, we include supplier the error terms for the dependent variable, we can use indegree centrality as a dependent variable and regress it this test of over-identifying restrictions to confirm their against the control and instrumental variables (Bellamy relevance (Bellamy et al., 2014). The null hypothesis et al., 2014). The results in Model 1 of Table 3 reveal that states that we have valid instrumental variables, so an external indegree centrality (B = 0.278; p < .000) and insignificant p value in the Sargan and Bassman tests external outdegree dependence (B = −0.259; p < .000) would indicate that we can use these instrumental vari- both have significant effects on the dependent variable, ables to test for the presence of endogeneity supplier indegree centrality. As suggested by Bellamy (Woolridge, 2016). Both the Sargan (p = .694) and Bas- et al. (2014), we also check the validity of the sman (p = .703) tests are statistically insignificant, in 812 POTTER AND WILHELM support of the validity of the instrumental variables. Fur- With supplier outdegree centrality as the dependent thermore, the partial F statistic suggests they are strong variable (Model 3), the first stage of the 2SLS regression rather than weak (Woolridge, 2016). indicates its positive association with internal outdegree In the second stage of the 2SLS estimation procedure dependence (B = 0.715; p < .000), whereas Japan supplier–supplier innovations is used as the dependent manufacturing dependence is negatively related to it variable and is regressed against supplier indegree central- (B = −0.127; p < .023). In the over-identifying restrictions ity as the explanatory variable, as well as the predicted tests, neither the Sargan (p = .310) nor Bassman (p = .325) values from the first-stage estimation as independent vari- test is statistically significant at a 95% level, so we appear ables (Bellamy et al., 2014). The results in Model 2 in to have valid instrumental variables (Bellamy et al., 2014). Table 3 reveal that supplier indegree centrality relates pos- The partial F statistic suggests that these instrumental var- itively to supplier–supplier innovations (B = 1.197; iables are not weak either (Woolridge, 2016). p < .008). According to the Durbin–Wu–Hausman (DWH) In the second stage, we regress supplier–supplier post-estimation test of endogeneity, our results are not innovations as the dependent variable on supplier out- influenced by endogeneity concerns. The significance degree centrality and the predicted values from the first- levels for the Durbin score (p = .396) and Wu–Hausman stage estimation (Bellamy et al., 2014). As we detail in test (p = .412) are greater than 0.10, so we do not reject Model 4 in Table 3, supplier outdegree centrality is nega- the null hypothesis that our variables are exogenous tively related to supplier–supplier innovations (Bellamy et al., 2014). Overall, the relationship between (B = −0.594; p < .056), at a 90% significance level. The supplier indegree centrality and supplier–supplier innova- Durbin score (p = .278) and Wu–Hausman test (p = .294) tions does not appear subject to endogeneity concerns. are both insignificant. These results suggest that the rela- tionship between supplier outdegree centrality and supplier–supplier innovations is not unduly affected by 5.3.2 | Supplier outdegree centrality endogeneity issues.

We also explored potential endogeneity in the relation- ship between supplier outdegree centrality and supplier– 6 | DISCUSSION supplier innovations, with a similar method. The instrumental variable internal outdegree dependence mea- With this study, we contribute to supply network-enabled sures the percentage of each firm's product ties that are innovation literature by studying the phenomenon of with customers from the supply network, so we can cap- supplier–supplier innovation (Hong & Hartley, 2011; ture the internal orientation of each firm toward cus- Narasimhan & Narayanan, 2013). Network ties to tomers within the supply network. We expect that firms upstream suppliers and downstream customers can have for which a greater share of their total customer base is different implications for the co-development of sup- within the Toyota supply network will supply products to plier–supplier innovations. Moreover, we find that the many network members. As a second instrumental vari- positive effect of supplier indegree centrality on supplier– able, we use Japan manufacturing dependence, which supplier innovations can be moderated by how firms are measures the percentage of each firm's total manufactur- structurally embedded within the supply network ing plants located in Japan. In the highly competitive (Kim, 2014). While betweenness centrality does not Japanese automotive industry, firms that are more depen- exert any significant moderating effects, closeness cen- dent on Japan as a manufacturing location may sell their trality, and embeddedness in small-clusters seem to products to a wider variety of Japanese automakers and have a substituting rather than complementary effects suppliers. Therefore, we anticipate that Japan manu- on the relationship between supplier indegree centrality facturing dependence is negatively associated with sup- and supplier–supplier innovations. Beyond the structural plier outdegree centrality, because firms with a large characteristics of the supply network, the geographical proportion of manufacturing plants in Japan will focus locations of plants also matters, and the number of on selling their products to the wide range of Japanese manufacturing plants a firm operates in Japan is found to automakers and customers outside the Toyota supply net- strengthen the positive effect of supplier indegree central- work. Consequently, as these instrumental variables are ity on supplier–supplier innovations, whereas it accentu- concerned with the orientation of the firm towards an ates the negative effect of supplier outdegree centrality. internal customer base and its dependence on Japan as a We summarize the main empirical findings in Table 4 and manufacturing location they are unlikely to have a signif- discuss them in more detail next. icant effect on the co-development of supplier–supplier As our findings reveal, supplier indegree centrality innovations across the supply network. has a positive effect on the co-development of supplier– POTTER AND WILHELM 813

TABLE 4 Summary of empirical findings

Hypothesis Empirical finding Hypothesis Empirical finding H1a Supported H1b Effect opposite that hypothesized H2a Not significant H2b Not significant H3a Effect opposite that hypothesized H3b Not significant H4a Supported H4b Not significant H5a Supported H5b Effect opposite that hypothesized

supplier innovations with other members of the Toyota the fuel cell industry found that broker firms contribute supply network. This result aligns with previous evidence to the development of innovations but only under cer- that firms with a large portfolio of suppliers can absorb tain circumstances. Brokers seemingly might use their the latest knowledge and foster supplier–supplier innova- power and prominence to coordinate flows of existing tions (Choi & Kim, 2008; Kim et al., 2011). The impor- materials and products, but they appear less effective in tance of supplier indegree centrality also reflects the terms of facilitating knowledge flows to support the unique contributions of systems integrators, such as joint design and creation of new products (Carnovale & Corporation, that manage a large number of Yeniyurt, 2015; Dong & Yang, 2016). Supplier–supplier upstream ties within the Toyota supply network and innovations in turn may be evolving in a decentralized coordinate the joint development of new products with manner across the supply network, without requiring other suppliers (Hobday et al., 2005; Kim et al., 2011; Kito intermediaries such as brokerstofacilitatethisprocess. et al., 2014). Next, we find that closeness centrality weakens the Contrary to our expectations though, supplier out- positive effect supplier indegree centrality has on the degree centrality has a negative effect on the number of formation of supplier–supplier innovations. In a simi- other network members with which each firm develops lar vein, research by Dong and Yang (2016) argues that supplier–supplier innovations in the Toyota supply net- closeness centrality can sometimes impede the devel- work. Rudberg and Olhager (2003) emphasize some opment of new products. More broadly, research by important distinctions between upstream ties with sup- Fang, Lee, Palmatier, and Han (2016) reveals that net- pliers and downstream ties with customers, which in turn work centrality can be a double-edged sword that helps influence the flow of products and knowledge within a to facilitate incremental innovations, but has a nega- supply network. As previous studies have highlighted, it tive effect on the development of breakthrough new may be critical to consolidate customer bases and build products. Our result suggests that it is mainly firms in strong ties with a few, key customers within the supply remote positions in the periphery of the supply net- network (Rost, 2011; Zhou et al., 2014). The negative work that make use of their upstream ties with sup- effect also might arise because firms with multiple down- pliers as a way to access valuable knowledge for the stream ties tend to be allocators that distribute standard- development of supplier–supplier innovations. This ized materials and codified information to multiple finding also signals the importance of paying more customers, without truly fostering effective interfirm attention to the role of remote firms in the periphery of innovations (Kim et al., 2011; Lu & Shang, 2017). Alloca- the supply network, as a less studied topic in the sup- tors often produce and supply common parts and stan- ply chain literature (Carnovale et al., 2016; Schilling & dardized components (e.g., springs, plastic fasteners, Phelps, 2007). Whereas prior literature emphasizes clips) but undertake limited R&D (Kito et al., 2014). central broker or navigator positions (Kim et al., 2011; We initially proposed that network brokers with high Leenders & Dolfsma, 2016; Owen-Smith & betweenness centrality would be able to use their power Powell, 2004), our research indicates that remote firms and influence to ensure their network ties were used for in the periphery may be better able to leverage their the co-development of supplier–supplier innovations ties with other network members to generate supplier– (Carnovale & Yeniyurt, 2015; Leenders & Dolfsma, 2016). supplier innovations. Instead, the results reveal no significant moderating With regard to small world clusters (Kito et al., 2014; effect of betweenness centrality on the relationship Sharma et al., 2019), we confirm our expectations by between supplier degree centrality and the formation of uncovering their negative moderating effect on the rela- supplier–supplier innovations. In a similar manner, tionship between supplier indegree centrality and sup- research by Vsudeva, Zaheer, and Hernandez (2013) into plier–supplier innovations. As a plausible explanation, we 814 POTTER AND WILHELM note that the greater density of knowledge flows in a small relatively few interfirm NPD projects with key customers. world cluster limits the number of upstream ties the firm This finding also implies there may be location-specific needs to absorb knowledge, because all cluster members advantages to maintaining a domestic manufacturing already possess similar levels of knowledge (Dyer & network that enables firms to leverage their fewer, stron- Nobeoka, 2000; Sharma et al., 2019). Firms outside small ger ties with key customers to co-develop innovations in world clusters instead require a greater number of the supply network. upstream ties to absorb the necessary knowledge for gen- Finally, in our post hoc analysis, we also find that erating supplier–supplier innovations (Kito et al., 2014). many of the above results are more pronounced among Moreover, the latter firms have the freedom, autonomy, firms that are developing eco-innovations that focus on and independence to use their upstream ties with sup- fuel cell, hybrid, and electric powertrain platforms pliers to participate in different inter-firm NPD projects (Appendix A1; Borgstedt et al., 2017; Potter & across the supply network, rather than being confined to a Graham, 2018). In particular, we find evidence that the few members of a small world cluster. effect of supplier indegree centrality is more pronounced Additionally, in line with our expectations, we find among firms that are generating green technologies. that the number of manufacturing plants located in Likewise, the moderating effects of closeness centrality, Japan helps to strengthen the relationship between sup- small world clusters, and the number of manufacturing plier indegree centrality and the frequency of supplier– plants in Japan appear to be stronger among firms that supplier innovations. Specifically, it may be the case that create green technologies within the automotive industry. firms with a large manufacturing presence within Japan These findings suggest that the transition to green vehicle have helped to develop the engineering and R&D capabil- architectures is also reshaping how firms use their net- ities of their local suppliers so they become more effective work ties to co-develop supplier–supplier innovations at generating supplier–supplier innovations (Dyer & across the supply network. Nobeoka, 2000; Lawson et al., 2015). Furthermore, firms with a large domestic manufacturing network with mother plants, showcase suppliers, and lead factories 6.1 | Managerial implications within Japan exhibit a strong orientation toward explor- ing new knowledge and developing new products with Our findings suggest several takeaways for managers local suppliers, as prior research has established (Dyer & from automakers and suppliers that are seeking to foster Nobeoka, 2000; Ferdows et al., 2016; Potter & the development of supplier–supplier innovations across Graham, 2018). With this finding we highlight the need their supply networks more strategically. Systems inte- to go beyond the analysis of structural characteristics of grators can use their various upstream ties with suppliers the supply network and take the geographic dimension to jointly create supplier–supplier innovations; managers of domestic manufacturing networks into account thus may want to deepen supply relationships with these (Cheng et al., 2015; Rudberg & Olhager, 2003). critical nodes in their supply networks. Managers also Regarding the relationship between supplier out- could work directly with suppliers to help them realize degree centrality and supplier–supplier innovation, we some of the benefits of consolidating their downstream do not find many moderating effects; when firms consoli- ties with customers, which then would offer beneficial date their customer base by focusing on a few strong ties effects for the co-development of supplier–supplier inno- with key customers, it often leads to supplier–supplier vations. Although network brokers receive a lot of atten- innovations, irrespective of whether they function as bro- tion from managers, due to their control and power over kers, are centrally positioned in the supply network, or the flow of materials and products, they may not be as join small world clusters (Kim et al., 2011; Rost, 2011). effective for facilitating the formation of inter-firm inno- However, the number of manufacturing plants in Japan vations as previously envisioned. Moreover, managers can strengthen the negative relationship between should not overlook the remote firms, positioned on the supplier outdegree centrality and the formation of sup- periphery of the supply network; they often use their plier–supplier innovations. This finding suggests that upstream ties to co-develop innovations with a range of consolidating a customer base into a small number of suppliers. Finally, managers may want to restructure downstream ties with key clients is more likely to result their purchasing practices, away from small world clus- in supplier–supplier innovations if the firms also have a ters, to ensure that member firms have sufficient freedom lot of manufacturing plants in Japan. In these conditions, and autonomy to use their upstream ties with suppliers supplier–supplier innovations can flourish, because to co-develop products with organizations throughout the large domestic manufacturing networks allow these firms supply network. Our findings also reveal there may be to devote their extensive engineering capabilities to location-specific advantages of retaining manufacturing POTTER AND WILHELM 815 capabilities within Japan as this is shown to help terms of their ability to collaborate within inter-firm strengthen the positive effect upstream ties with suppliers NPD projects. Finally, further research is needed to have on the formation of supplier–supplier innovations. determine what happens if automakers and suppliers restructure small world clusters, including the implica- tions for the co-development of supplier–supplier inno- 6.2 | Research limitations and directions vations within the supply network.

Although we have taken a number of steps, this study has ENDNOTES several limitations. First, our research is based upon the 1 Similar to Basole (2016), we use Gephi 9.2 network software to firms within the Toyota supply network, and this restricts develop our measures of betweenness centrality, closeness cen- the generalizability of our results to other research set- trality, and small world clusters (i.e., average clustering coeffi- tings. In the unique Toyota supply network, supplier– cient) using data from Marklines about the ties between firms supplier innovations may be especially prevalent, because within the Toyota supply network. 2 Toyota and its suppliers actively and heavily invest in We explored if our results also applied to supplier–supplier inno- R&D, compared with other firms and networks. Second, vations in a different time span, namely, the 4 years prior to 2015 (2011–2015), which would cover the time leading up to the 2015 due to data limitations, we were unable to study the longer launch of the fourth-generation Toyota Prius and Toyota Mirai. term evolution of the supply network and how this This analysis confirmed that the results remain robust; the direc- reshapes patterns of inter-firm innovations (Park, Bel- tion and magnitude of the hypothesized relationships both are lamy, & Basole, 2018). Furthermore, the changing dynam- similar to our main findings. ics of new ties, dormant ties, service ties, and managerial ties might be relevant to so-called nexus suppliers but do ORCID not appear in our study (Yan, Choi, Kim, & Yang, 2015). Antony Potter https://orcid.org/0000-0001-7918-8576 Third, we note the problems related to using patent data Miriam Wilhelm https://orcid.org/0000-0001-5782-4674 to measure supplier–supplier innovations (Bellamy et al., 2014). 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R&D Management, 35 https://doi.org/10.1002/joom.1124 (2), 203–215. Salvador, F., & Villena, V. H. (2013). Supplier integration and NPD outcomes: Conditional moderation effects of modular design competence. Journal of Supply Chain Management, 49(1), 87–113. APPENDIX: ROBUSTNESS ANALYSIS Schilling, M., & Phelps, C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Green technology suppliers – Management Science, 53(7), 1113 1126. Additionally, we are interested in whether our empirical Sharma, A., Kumar, V., Yan, J., Borah, S., & Adhikary, A. (2019). findings are more pronounced among firms that are Understanding the structural characteristics of a firm's whole buyer–supplier network and its impact on international busi- green technology suppliers that have patented eco-inno- ness performance. Journal of International Business Studies, 50 vations. Rather than using split sample analysis, we inter- (3), 365–392. acted with our control variable Green Technology Takeishi, A., & Fujimoto, T. (2001). Modularisation in the auto Supplier with the explanatory variable, moderators, and industry: Interlinked multiple hierarchies of product, produc- the interaction terms (Table A1). This approach helps to tion and supplier systems. International Journal of Automotive overcome some of the unobserved characteristics of green Technology and Management, 1(4), 379–396. technology suppliers that can also influence the depen- Todo, Y., Matous, P., & Inoue, H. (2016). The strength of long ties and the weakness of strong ties: Knowledge diffusion through dent variable, especially as this approach is unlikely to supply chain networks. Research Policy, 45, 1890–1906. generate problems associated with selection biases. Using 818 TABLE A1 Robustness analysis: Examining the effect of Green Technology Suppliers (GTS) on supplier indegree centrality (Models 1–3) and supplier outdegree centrality (Models 4–6)

Variables Model 1 Model 2 Model 3 Variables Model 4 Model 5 Model 6 Control variables Control variables Firm size 0.228*** (0.000) 0.198*** (0.001) 0.278*** (0.000) Firm size 0.294*** (0.000) 0.215*** (0.001) 0.219*** (0.001) Firm age −0.013 (0.817) −0.018 (0.733) −0.025 (0.611) Firm age −0.037 (0.555) −0.000 (0.994) 0.003 (0.959) † International governance −0.137* (0.018) −0.109 (0.054) −0.114* (0.030) International governance −0.131* (0.046) −0.083 (0.145) −0.080 (0.162) † R&D centers 0.065 (0.308) −0.018 (0.776) −0.038 (0.520) R&D centers 0.131 (0.067) −0.024 (0.714) −0.038 (0.558) † † Module system 0.043 (0.452) 0.062 (0.268) 0.078 (0.138) Module system 0.065 (0.322) 0.097 (0.091) 0.114 (0.053) Engine system −0.024 (0.672) 0.009 (0.872) 0.040 (0.416) Engine system 0.005 (0.932) −0.000 (0.996) −0.004 (0.940) Toyota investment −0.081 (0.196) −0.042 (0.491) −0.092 (0.102) Toyota investment 0.051 (0.465) 0.010 (0.864) 0.014 (0.823) Supplier investment 0.042 (0.462) 0.015 (0.781) 0.022 (0.674) Supplier investment 0.016 (0.811) 0.017 (0.759) 0.010 (0.867) † Long-term relationship 0.105 (0.065) 0.057 (0.296) 0.042 (0.404) Long-term relationship 0.070 (0.273) 0.038 (0.490) 0.055 (0.322) Market competition 0.040 (0.511) 0.064 (0.266) 0.073 (0.171) Market competition 0.078 (0.257) 0.051 (0.396) 0.043 (0.475) Main effect Main effect Supplier Indegree centrality 0.470*** (0.000) 0.603*** (0.001) 0.593*** (0.001) Supplier outdegree centrality −0.105 (0.109) −0.062 (0.483) −0.064 (0.470) Green technology Green technology Green technology supplier (GTS) 0.022 (0.721) 0.026 (0.681) 0.012 (0.845) Green technology supplier (GTS) 0.037 (0.603) 0.083 (0.175) 0.039 (0.732) † Supplier indegree centrality × GTS – −0.279 (0.142) −0.395 (0.126) Supplier outdegree centrality × – −0.150 (0.093) −0.236 (0.374) GTS Moderator effects Moderator effects Betweenness centrality × GTS – 0.193* (0.015) 0.025 (0.866) Betweenness centrality × GTS – 0.367*** (0.000) 0.410*** (0.000) Closeness centrality × GTS – −0.082 (0.225) −0.128 (0.112) Closeness centrality × GTS – 0.059 (0.384) 0.137 (0.625) Small world clusters × GTS – 0.002 (0.968) −0.274** (0.009) Small world clusters × GTS – −0.035 (0.538) 0.058 (0.575) Number of plants located in Japan × – 0.231*** (0.000) 0.120* (0.049) Number of plants located in – 0.199*** (0.001) 0.118 (0.107) GTS Japan × GTS Interaction effects Interaction effects Supplier indegree centrality × ––−0.174 (0.255) Supplier outdegree centrality × ––0.122 (0.451) Betweenness centrality × GTS Betweenness centrality × GTS Supplier indegree centrality × ––−0.615* (0.017) Supplier outdegree centrality × ––0.104 (0.785)

Closeness centrality × GTS closeness centrality × GTS POTTER Supplier indegree centrality × Small ––−0.812** (0.005) Supplier outdegree centrality × ––0.111 (0.298)

world clusters × GTS Small world clusters × GTS AND † Supplier indegree centrality × ––0.453*** (0.000) Supplier outdegree centrality × ––−0.167 (0.053) WILHELM Number of plants located in Japan number of plants located in × GTS Japan × GTS POTTER AND WILHELM 819

this approach, the results in Model 1 reveal that supplier indegree centrality has a positive effect on the occurrence of supplier–supplier innovations (B = 0.470; p < .000). The results in Models 1, 2 and 3 suggests that the effect of supplier indegree centrality on supplier–supplier inno- vations may be more pronounced among firms that are developing green technologies within the automotive industry. In Model 3, we also find that betweenness cen- trality has no significant moderating effect (B = −0.174; p = .255). In contrast, closeness centrality (B = −0.615; p < .017), small world clusters (B = −0.812; p < .005), and the number of plants located in Japan (B = 0.453; p < .000) have moderating effects on the relationship between supplier indegree centrality and supplier– supplier innovations. These findings suggest that these 0.189 0.407 0.409 5.23*** 9.80*** 8.18*** 219 219 219 moderating effects are more pronounced among firms that have developed eco-innovations for new green vehi- cle architectures. Next, the results in Model 4 identify that supplier out- degree centrality has no significant effect on the develop- ment of supplier–supplier innovations (B = −0.105; F p = .109). Finally, we observe that only the number of 2

R plants located in Japan (B = −0.167; p < .053) has a par- tial moderating effect but this is significant at the 90% level (Model 6). Overall, these results indicate that new N green vehicle architectures may be playing an important role by reshaping how firms use their upstream ties with suppliers to co-develop innovations within the supply network. It also appears that the moderating effects we observed in our earlier analysis are more pronounced among the firms developing green technologies. How- ever, the transition towards green vehicle architectures does not appear to influence how downstream ties with customers influence the development of supplier– supplier innovations across the supply network. supplier innovations from 2015 to 2018. Green Technology Supplier (GTS) measures whether a firm undertakes research into green technolo- – 0.366 0.432 0.523 Adjusted 11.46*** 10.76*** 12.55*** Overall model 219 219 219 < .10. p † < .05; p < .01; * p (Continued) F 2 R < .001; ** p dependent variable: Number of supplier VariablesAdjusted Model 1 Model 2 Model 3 Variables Model 4 Model 5 Model 6 Overall model N TABLE A1 Note: gies. ***