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Top1 Introduction
The online innovation platform refers to a virtual environment where masses of innovative users (e.g., customers, experts in certain fields,suppliers, and other interested individuals) can propose innovative ideas or solutions and interact with others to achieve collective innovation (Liang et al., 2016). In recent years, the wide use of Web 2.0 and social media technology has boosted many online innovation platforms, as represented by crowdsourcing and/or user communities (Schemmann et al., 2016; Mention et al., 2019). Some large companies have initiated their online innovation platforms to facilitate open innovation and improve their innovation performance (Palacios-Marqués, 2015). For example, Dell’s IdeaStorm community, Starbucks’s My Starbucks Idea and Salesforce.com have been pioneers in sponsoring the user innovation communities.
The continuous and high-quality innovation outcomes contributed by users are crucial for the success and the long-term operation of online innovation platforms (Palacios-Marqués, 2015; Rishika & Ramaprasad, 2019). However, not all the firm-initiated innovation platforms are successful, and studies on how to improve innovation outcomes of online users are necessary (von Briel & Recker, 2017). Social network theory, which emphasizes the importance of relationships among interacting individuals, deems that individuals’ behaviors and outcomes (e.g., innovation) can be dramatically affected by their social environments and social structures (Granovetter, 1985). Benefiting from the development of social network analysis method and the accessibility of online users’ interaction data, some scholars have explored the impact of online relationships on users’ innovation behaviors from the network perspective, aiming to find some theoretical evidence on how to improve users’ innovation outcomes.
Among the studies in this area, some scholars view online interactive relations as one of the most critical aspects of social capital, focusing on users’ online social ties and how they benefit user innovation (Yang & Li, 2016; Guo et al., 2017). The online users can interact with others by friending or following each other and by voting or commenting on others’ proposals, through which the online ties between users are developed (Arnaboldi et al., 2017). Online ties can benefit users with information and knowledge acquired from connected others and enable social learning between users (Riedl & Seidel, 2018). These online ties play an important role in inspiring users with new ideas. Moreover, the online interactions for all users form the network. Some scholars investigate the structure of the online social network and the diffusion of innovation in the network (Karsai et al., 2014; Arnaboldi et al., 2017). Based on innovation diffusion theory (Rogers, 2003) and the complex social network analysis method, they find evidence of the social influence among online network members; that is, one member’s behavior is influenced by the connected others, and the network structure affects the diffusion of information and innovation in the network (Centola, 2010). To summarize the points of prior studies, online users’ social networks would bring both resource benefits and social influence for network members, which would help online users promote their innovation outcomes. However, few studies consider the two aspects of the online social network together when they explore the networks’ effects on user innovation.
The practical and theoretical issues mentioned above motivated us to holistically examine the impact of online social networks on user innovation. To address this, we begin with social embeddedness to produce our dimensions of social network analysis. Sociologists argue that individuals and their behaviors are embedded in a society composed of a variety of social relations (Granovetter, 1985; Granovetter, 1992). Generally, there are two kinds of embeddedness — relational embeddedness and structural embeddedness, which provide the two dimensions when we explore the effects of online users’ social networks. Relational embeddedness stresses the nature of social relations and focuses on the role of social relations as a mechanism for gaining fine-grained benefits. Goyal (2012) views this as the local network effect, which examines how one user’s social relations influence his/her behavior and outcome through direct ties with other users. Structural embeddedness goes beyond direct ties and emphasizes the benefits of the structural position individuals occupy in the social network. Goyal (2012) identifies this as the global network effect, which examines how entire network relations (i.e., direct & indirect ties) influence user innovation through the network structure.