Relationship between Social Capital, Seniority and Knowledge Sharing Challenges: A Case Study of a Foreign R&D Subsidiary in China

China is increasingly transforming itself from “made in China” image to “innovated in China” (Stanley et al., 2013). With the country’s strategy and environment change, foreign multinational enterprises (MNEs) R&D activities have been established China and started build up their innovation capabilities initially for sales to the Chinese market and more recently for other markets as well (Zedtwitz et al., 2007; Chen, 2008; Govindarajan, 2012). With increasing customer demands and governmental requirements, inter- and intra-organizational knowledge sharing is essential for building up innovation capabilities in the subsidiary and create the capabilities for knowledge outflows back to the MNE network (Qi et al., 2014; Awate et al., 2015). However, facilitating knowledge sharing is a difficult task and typically takes considerable amount of time to manage across organizational and functional boundaries (von Zedtwitz et al., 2004; Lam & Lambermont-Ford, 2010). Therefore, social capital is a structural, relational and cognitive resource that plays a considerable role within organizations for accessing and sharing knowledge subsidiaries (Nahapiet & Ghoshal, 1998; Kostova & Roth, 2003; Kauppila et al., 2011). Especially within large MNEs, employees utilize their social capital to bypass long and inefficient organizational processes, seek expert knowledge and improve the organizations searching ability (Gubbins & Dooley, 2014). According to Nahapiet & Ghoshal (1998), social capital also contributes to the creation of new intellectual capital. With time and experience, employees build up and strengthen their social ties across the MNE and reduces knowledge sharing challenges (Moore & Birkinshaw, 1998; Duan et al., 2010). Drawing on knowledge-based perspective, this study investigates the relationship between social capital and knowledge sharing challenges in a German automotive R&D subsidiary with around 500 employees in China. The data was collected during a four months research stay in 2016 and is based on 56 interviews of R&D management and staff. Previous research has explored the relationship between social capital and knowledge transfer (Gooderham et al., 2011; Inkpen et al., 2016), social capital and value creation (Tsai & Ghoshal, 1998), social capital and R&D teams (Reagans & Zuckerman, 2001; Allen et al., 2007) and social capital and innovation (Landry et al., 2002; Pérez-Luño et al., 2011; Gubbins & Dooley, 2014). Following the methodology of social network analysis (SNA), we use data gathered through interviews to construct the social networks. The interviewees were selected to cover all 10% of employees within all R&D departments. All directors department directors or their deputies were interviewed. Based on a mixture on the department directors’ recommendation (50%) and random sampling (50%), managers and staff were selected. The structure of the data allows depicting the links between the interviewee and their most frequent contacts. In social network analysis, the ties – or edges – may represent different kinds of relationships. According to Borgatti et al. (2009), a big proportion of social network research studies how four basic types of relations – similarities, social relations, interactions, and flows – affect each other. In this specific case, the interviewee was asked to name co-workers with whom they interact the most often. This interaction is strictly of professional nature and provides a notion of tight communication between two employees. The interaction can be of close proximity, in the case the interviewee mentioned a project or department co-worker with whom they interact face-to-face, or of long distance, in the case the interviewee mentioned a co-worker who works in another MNE location. We use the following metrics to analyze the networks in our data. To get a grasp of the network and its interconnectedness, we utilize the measure of community detection. To understand the role of a specific actor (node) in the network, we use the eigenvector centrality. - Community detection: We clustered the actors by applying the Louvain algorithm (cf. Blondel et al., 2008), which aim is to optimize modularity, defined as a value that measures the density of links inside communities compared to links between communities. Through an iterative process, the algorithm builds communities that have higher density of links. In this paper, we compute the modularity algorithm included in the Gephi software to determine statistical communities. - Eigenvector centrality allows identifying the most influential users. It defines actors that are connected to important nodes. A person can have few but important connections resulting in a high eigenvector centrality. Earlier pioneers of this method include Wassily W. Leontief (1976) and John R. Seeley (1951). This network depicts the structure of interactions and the most important actors in the network. A thorough analysis of the network compares the total sum of knowledge sharing challenges the interviewees face with their respective position and seniority within the company. This second network hints to the idea that the longer a person spends in the company, the more integrated they become in the social networks, developing tighter ties and resulting in lower knowledge sharing challenges. The paper contributes to the theory of knowledge management in MNEs. Practically, it provides managers in foreign R&D subsidiaries insights into the relations between social capital, knowledge sharing challenges and seniority.