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With the rapid development of Web 2.0 in recent years, increasing numbers of enterprises have established their Internet-based platforms to attract customers’ contributions to enterprise innovation. A virtual innovation platform is a place where users can propose new product design or innovative ideas directly to a specific company (Hwang et al., 2019). For example, Dell Group established the IdeaStorm to collect users’ ideas and suggestions for improving product and production. Even the enterprises in developing countries, such as Haier Group in China, have also built the innovation platforms that which encourage domestic users to participate in their product innovation. For these enterprise-initiated innovation platforms, stimulating users to produce more and better innovation ideas is the key to success and sustainability (Liang et al., 2016).
Many scholars have investigated on how to improve the users’ innovation outcomes from different perspectives, such as IT/IS design (Gharib et al., 2017; Islam & Rahman, 2017), the platform boundary and openness design (Balka et al., 2014; Liang et al., 2016), the incentives for user participation and contribution (Frey et al., 2011; Baldus et al., 2015; Hossain, 2017), and the leader users identification (Jeppesen & Laursen, 2009; Bulgurcu et al., 2018). Moreover, the popularity and usage of the social media in innovation platforms boosts a new perspective — the user interactions and relations (Kosonen et al., 2013; Hassan et al., 2019).
Many studies have shown that online interactive relations (e.g., friending or following others, voting or commenting on others’ ideas) among individuals can help to improve user innovation, such as extending the duration of users’ participation, contributing more knowledge (e.g., the reviews or comments) or ideas, and improving the quality of ideas (Blohm et al., 2011; Chen et al., 2012; Kosonen et al., 2013; Chan et al., 2015). Previous studies mostly investigate the online relations from quantity dimension (e.g., the number of followers/friends/commentators) (Wasko & Faraj, 2005; Trier, 2008; Chen et al., 2012). Yet some recent studies use social network analysis to characterize the online relations from multiple dimensions (e.g., direction, quantity, strength, etc.) and explore their impacts on user innovation (Chan et al., 2015; Hwang et al., 2019; Rishika & Ramaprasad, 2019). However, the literature has not demonstrated how these relations improve users’ innovation outcomes and the different roles for the relations’ multiple dimensions. It is necessary to explore the influencing path of social network relations on innovation outcomes under the context of virtual platforms. Only the influencing mechanism is figured out, the reasonable suggestions for platforms improving user innovation outcomes can be given.
According to social capital theory, one’s social relations enable individuals in a social network to access and exchange useful resources from others, which would have an effect on their innovation outcomes (Fowler & Christakis, 2010). Tsai and Ghoshal (1998) has confirmed that the resource exchange between departments in the intrafirm networks mediated the influence of social relations on departments’ innovation performance. Thus, tracking the users’ interactive behaviors and analyzing the contents of interactions shed the light on exploring the working mechanism of how users’ online relations influence innovation outcomes. Recently, researchers have started to use big data to analyze the unstructured content (e.g., texts, images) generated by online users (Sapountzi & Psannis, 2018). The common practices for the textual content analysis include keywords extraction (Stephen et al., 2016), topic and event detection (Vavliakis et al., 2012; Panagiotou et al., 2016), and sentiment analysis (Poria et al., 2016; Yu et al., 2016). The textual content analysis is able to track and analyze the textual contents of user interaction in order to explore the influencing paths of users’ online relations on innovation outcomes.