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User Willingness toward Knowledge Sharing in Social Networks

Author

Listed:
  • Jie Zhao

    (School of Business, Anhui University, Hefei 230601, China)

  • Chanjuan Zhu

    (School of Business, Anhui University, Hefei 230601, China)

  • Zhixiang Peng

    (School of Business, Anhui University, Hefei 230601, China)

  • Xin Xu

    (School of Mathematics Science, Anhui University, Hefei 230601, China)

  • Yan Liu

    (School of Business, Anhui University, Hefei 230601, China)

Abstract

Social networks introduce new potential for people to share knowledge with others. However, it is not clear what factors influence user willingness toward knowledge sharing in social networks. Aiming to answer these questions, in this paper we analyze the major factors influencing user willingness toward knowledge sharing in social networks and propose a new research model that is inspired by the technology acceptance model (TAM). In particular, we introduce a new independent variable called perceived value which is described by four aspects: social value, entertainment value, emotion value, and information value. In addition, we introduce a new mediating variable, trust, to reflect the intermediating relationship between perceived value and knowledge-sharing willingness. We conduct an empirical analysis on questionnaire data and present comprehensive results on reliability and validity, factor analysis, correlation analysis, and mediating effects analysis. The results show that perceived value has a significant impact on knowledge-sharing willingness, and trust plays a partial intermediate role between perceived value and knowledge-sharing willingness. Further, we present some research implications for knowledge sharing and learning innovation in social networks, as well as some suggestions for organizations to advance knowledge sharing and learning innovation in the social-network age.

Suggested Citation

  • Jie Zhao & Chanjuan Zhu & Zhixiang Peng & Xin Xu & Yan Liu, 2018. "User Willingness toward Knowledge Sharing in Social Networks," Sustainability, MDPI, vol. 10(12), pages 1-27, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4680-:d:189083
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    References listed on IDEAS

    as
    1. Ha, Sejin & Stoel, Leslie, 2009. "Consumer e-shopping acceptance: Antecedents in a technology acceptance model," Journal of Business Research, Elsevier, vol. 62(5), pages 565-571, May.
    2. Jie Zhao & Jianfei Wang & Suping Fang & Peiquan Jin, 2018. "Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    3. Jie Zhao & Suping Fang & Peiquan Jin, 2018. "Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude," Sustainability, MDPI, vol. 10(2), pages 1-26, January.
    4. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
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    Cited by:

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    5. Yaxue Ma & Zhichao Ba & Yuxiang Zhao & Jin Mao & Gang Li, 2021. "Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7051-7085, August.
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    7. Jiaqi Liu & Zhenping Zhang & Jiayin Qi & Hong Wu & Manyi Chen, 2019. "Understanding the Impact of Opinion Leaders’ Characteristics on Online Group Knowledge-Sharing Engagement from In-Group and Out-Group Perspectives: Evidence from a Chinese Online Knowledge-Sharing Com," Sustainability, MDPI, vol. 11(16), pages 1-28, August.

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