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Top1. Introduction
As a type of products based on the communication and information technology (e.g., Web 2.0), social media, applications for information exchange and creation, provide people with multiple channels of knowledge diffusion and knowledge innovation (Kaplan & Haenlein, 2010; Filo et al., 2015). Through adopting social media, individuals can access knowledge faster and propose new ideas after exchanging their thoughts with others, indirectly achieving the individuals’ self-development and promoting organizations (Filo et al., 2015). However, knowledge sharing behaviors do not naturally occur (Lu et al., 2006). Past researchers have explored motivators of knowledge sharing on social media based on different theories (e.g., social capital theory, technology acceptance model, social cognitive and social exchange theories) in broad contexts (Ahmed et al. 2018) and need more discussions as follows.
First, past researchers have adopted around 29 theories to explore knowledge sharing on social media. However, they fail to reach consensus in some effects of motivators (e.g., reputation, social factors, the norm of reciprocity, and altruism) and fail to adequately explore the quality and correlations of motivators (Gagne, 2009). Since knowledge cannot transfer from individual to individual naturally unless knowledge owners take the initiative to tell others (Welschen et al., 2012), people’s willingness is vital to the initiation of sharing behavior. Deci and Ryan (1985) assert that externally induced incentives and internally evoked incentives can inspire behaviors, and different types of incentives have various qualities in inspiring behaviors. Different from the theories adopted in the past literature that used to focus on the relationship between motivators and knowledge sharing behaviors or intentions, the self-determination theory (Deci & Ryan, 1985) can be adopted to explore the online knowledge sharing phenomenon from the perspective of interactive effects of different motivations. Exploring the relationships among motivators may help researchers better understand the process of how knowledge sharing happens. Therefore, it is proper to adopt SDT to explore knowledge sharing behavior on social media. Second, although social media provide more approaches for researchers to exchange data (e.g., Birnholtz, 2007; Kaye et al., 2009), not enough papers have studied in how to inspire efficient knowledge exchange among academia who are the main contributors of knowledge innovation (Ahmed et al. 2018). Finally, out of taking knowledge competition advantage, researchers who are in the same field may less share knowledge with members. Fischer and Zigmond (2010) believe that the cross-disciplinary environment may inspire researchers to release the fear of losing advantages and exchange ideas with others. However, few past papers investigate researchers’ knowledge sharing behaviors in a multidisciplinary virtual community on social media. Therefore, it is necessary to study how different motivators of knowledge sharing influence researchers with different major backgrounds in sharing knowledge in the cross-disciplinary community on social media.
This study introduces the background to the research in Part 2. Based on the theoretical background, the paper then proposes the hypotheses in Part 3. Next, this paper presents the research methods in Part 4. Part 5 and part 6 demonstrate the results. Next, the study discusses the contributions in Part 7. Finally, Part 8 concludes the whole study.