Network Centrality and Performance: Effects in the Automotive Industry.
| Autor | Ferreira, Augusto Squarsado |
I Introduction
Network analysis can be a mighty resource for understanding the economy and society (Borgatti, Mehra, Brass, & Labianca, 2009; Jackson, 2010; Kilduff & Brass, 2010). Network phenomena can be related to a new form of governance and a method to understand the socioeconomic world (Borgatti et al., 2009; Owen Smith & Powell, 2008). As a form of governance, networks encompass several forms of partnerships, such as alliances, consortiums, joint ventures, and other multiple modes of cooperation in research and development, production, and marketing (Owen Smith & Powell, 2008; Lewis, 2011; Provan & Kenis, 2008). These initiatives are usually strategical, providing mutual benefits through cooperation (Bamford & Forrester, 2003; Das & Teng 2003; Gulati, Wohlgezogen, & Zhelyazkov, 2012).
As a new form of governance, network analysis encompasses new challenges to understand the structural and relational aspects of social life (Owen Smith & Powell, 2008), especially because economic transactions are socially embedded and cannot be explained strictly in economic terms (Sacomano & Paulillo, 2012; Smelser & Swedberg, 2010; Swedberg & Granovetter, 1992). Economic sociology suggests that the dynamics of the economy are embedded in social relations (Granovetter, 2018; Owen Smith & Powell, 2008), which encourages the use of social network analysis (SNA) to address relational and structural configurations of economic arrangements (Borgatti, Brass, & Halgin, 2014). Network analysis suggests contemplating the social context as the core of any economic phenomenon, and not as a mere externality (Convert & Heilbron, 2007). In this approach, organizations have to be contextualized in the structural and relational positions they occupy in complex business relationships (Borgatti et al., 2014; Owen Smith & Powell, 2008; Sacomano, Matui, Candido, & Amaral, 2016).
Within this literature, just a few studies provide ways to relate specific performance measures with the positions of organizations in the structure of networks (Gulati & Gargiulo, 1999; Provan, Fish, & Sydow, 2007; Wang, Zhao, Li, & Li, 2015; Zaheer, Gozubuyuk, & Milanov, 2010). Provan et al. (2007) highlight different studies concerning performance metrics at the "whole network" level. Similarly, Zaheer et al. (2007) propose a framework which can be used to organize network performance relationships, and Wang et al. (2015) address the relationship between innovation and network centrality metrics.
This topic may also be related to the emergent forms of governance in the automobile industry: cross-shareholding, joint ventures, manufacturing contracts, and alliances (Freyssenet, 2009; Sacomano et al., 2016; Wang et al., 2016). These forms of governance have grown substantially in the automotive industry, involving different structures (Freyssenet, 2009; Matui & Sacomano, 2017; Wang et al., 2016). They may be observed in partnerships such as Renault-Nissan (https://www.economist.com/ business/2010/06/10/all-together-now, retrieved in 2020, June 29th), Ford-Mazda (Freyssenet, 2009), General Motors-PSA, Volkswagen-Suzuki, Renault-Nissan-Daimler AG (Wang et al., 2016), Fiat-Chrysler (Ichijo & Kohlbacher, 2008; Lee & Jo, 2007), Nissan-Mitsubishi (https://www.economist.com/business/2016/05/12/ nissan-and-mitsubishi-make-an-alliance, retrieved in 2020, June 29th), and Fiat-Peugeot (https://www.reuters.com/ article/us-fca-m-a-psa/fiat-chrysler-peugeot-maker-psa-amend-merger-terms-to-conserve-cash-idUSKBN2653AE, retrieved in 2020, June 29th). Understanding them is essential in the current context of economic integration, high competition, and complexity in terms of production standards and productive scale (Gomes-Casseres, 1994; Wit & Meyer, 2010).
Although some studies explore specific performance measures and network structural positions (Gulati & Gargiulo, 1999; Provan et al., 2007; Wang et al., 2015; Zaheer et al., 2010), no research was found to relate specific performance measures and network structural positions in the automobile industry. Furthermore, previous studies have not embraced the following aspects at the same time: (i) the plurality of relationships found in the automotive industry (Soda, 2011; Tatarynowicz, Sytch, & Gulati, 2016) and (ii) the relationship between actors' centrality metrics and organizational attributes such as revenue, production, and performance (Powell et al., 1999; Shipilov, 2009).
In this article, we assess the relationship between network positioning through indegree centrality and organizational performance in the automotive industry, using production level, revenue, and profit indicators. Our sample includes a major portion of the organizations in the automobile industry, including around 85% of world vehicle production (https://www.oica.net/category/ production-statistics/, retrieved in 2020, June 29th). We explore whether a positive relationship is observable between multiple performance indicators and centrality metrics in network positioning. Different types of governance structures and performance indicators were considered at the same time, as well as two levels of analysis regarding the centrality metrics: one focused on the organization's performance by itself (organization's indegree centrality) and the other focused on the organizational performance of prominent automotive groups (subgroup's indegree centrality). Therefore, as specific objectives, we evaluate the indegree centrality for each actor in the network and identify the communities and groups, followed by an evaluation of indegree centrality as well, to finally explore the similarity between indegree centrality and performance.
We incorporate indegree centrality because we wanted to examine how the inflow of money was similar to the indegree centrality for each actor and subgroup; and we encompass performance indicators since these are the most practical and straightforward measures to deal with. Centrality is a useful network property, with rich inferences about actors' structural and relational positions (Borgatti, Everett, & Freeman, 2002). An actor's centrality, for example, implies prominent positions in the network to access innovation, information, markets, and other competitive and institutional resources.
Our results show that there is a positive relationship between indegree centrality and the automakers' organizational revenue. The Louvain algorithm (Aynaud, Blondel, Guillaume, & Lambiotte, 2013; Gach & Hao, 2013) detected a strong community structure for the network, where groups also displayed greater performance attributes associated with higher indegree centrality.
The paper has the following structure. First, we carry out a literature review, exploring networks and governance, as well as the concepts of centrality and performance. Second, we explain our method in two stages, data collection and data analysis, emphasizing our sources, the procedures followed, and the metrics used. Third, we present our results through graphs and descriptive statistics and discuss our findings.
2 Literature Review
2.1 Networks: governance and analysis
There are two approaches to the study of networks in economics: networks as a form of governance and networks as a method of analysis (Smith-Doerr & Powell, 2005). While in economic theory the rational choice approach emphasizes atomistic decision-making processes (Schulz, 2016), the network approach assumes that economic transactions are embedded in social relations (Granovetter, 1973; Granovetter, 2017). Networks objectively represent social structures that both enable and constrain economic action (Brailly, Favre, Chatellet, & Lazega, 2016; Granovetter, 1973; Smith-Doerr & Powell, 2005). They facilitate access to multiple forms of material (economic capital, economies of scale) and immaterial (information, knowledge, prestige) resources for firms, enabling their actions and economic performance (Gulati, 2007; Powell et al., 1999; Uzzi, 1997; Westphal, Gulati, & Shortell, 1997). They also define limits to organizations and function as conduits that can propagate social instabilities and uncertainty.
Networks entail positive or negative interactions, involving relationships of cooperation or conflict. In the case of firm alliances, the structure of interactions is positive and tends to operate as a form of resource, enabling social connections and the gains related to them (Granovetter, 2017). For some authors, the network structure of industries is considered a constellation of alliances (Garcia-Pont & Nohria, 2002; Nohria & Garcia-Pont, 1991). Scholars have studied how firms' alliances in a particular industry may explain their heterogeneous performance (Bamford, Gomes-Casseres, & Robinson, 2003; Koka & Prescott, 2008). In these assessments, the authors assume that companies are not homogeneous, but instead have varied scales, types of products and services regarding price, features, and quality, and types of customers served, etc. (Das & Teng, 2003; Goerzen, 2007). As a result, the network structure of industries encompasses the exchange of information, resources, and influence (Gulati, 2007; Powell et al., 1999).
Companies with a similar strategic focus compete directly with one another and have been called strategic groups (Nohria & Garcia-Pont, 1991). These companies tend to occupy structurally equivalent positions in complex industrial networks, reflecting the intersubjective representations of the managers about who their main competitors are (Porac, Thomas, Wilson, Paton, & Kanfer, 1995). They have common ties with companies from other strategic groups, establishing partnerships with them and strategic blocks that depend on relationships of trust (Nohria & Garcia-Pont, 1991; Ratajczak-Mrozek, 2017; Sacomano et al., 2013). These alliances establish specific combinations of network resources, combinations that are not easily replicable by other competing blocks (Gulati, 2007). The groups'...
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