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Motivations for self‐archiving on an academic social networking site: A study on researchgate

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  • Jongwook Lee
  • Sanghee Oh
  • Hang Dong
  • Fang Wang
  • Gary Burnett

Abstract

This study investigates motivations for self‐archiving research items on academic social networking sites (ASNSs). A model of these motivations was developed based on two existing motivation models: motivation for self‐archiving in academia and motivations for information sharing in social media. The proposed model is composed of 18 factors drawn from personal, social, professional, and external contexts, including enjoyment, personal/professional gain, reputation, learning, self‐efficacy, altruism, reciprocity, trust, community interest, social engagement, publicity, accessibility, self‐archiving culture, influence of external actors, credibility, system stability, copyright concerns, additional time, and effort. Two hundred and twenty‐six ResearchGate users participated in the survey. Accessibility was the most highly rated factor, followed by altruism, reciprocity, trust, self‐efficacy, reputation, publicity, and others. Personal, social, and professional factors were also highly rated, while external factors were rated relatively low. Motivations were correlated with one another, demonstrating that RG motivations for self‐archiving could increase or decrease based on several factors in combination with motivations from the personal, social, professional, and external contexts. We believe the findings from this study can increase our understanding of users' motivations in sharing their research and provide useful implications for the development and improvement of ASNS services, thereby attracting more active users.

Suggested Citation

  • Jongwook Lee & Sanghee Oh & Hang Dong & Fang Wang & Gary Burnett, 2019. "Motivations for self‐archiving on an academic social networking site: A study on researchgate," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(6), pages 563-574, June.
    • Handle: RePEc:bla:jinfst:v:70:y:2019:i:6:p:563-574
    DOI: 10.1002/asi.24138
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    Cited by:

    1. Chen Yang & Tingting Liu & Xiaohong Chen & Yiyang Bian & Yuewen Liu, 2020. "HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 429-449, April.
    2. Renqiang, Xie & Wende, Zhang, 2022. "An empirical study on the impact of platform environmental factors on knowledge sharing in virtual communities," Technology in Society, Elsevier, vol. 71(C).
    3. Vincenzo Corvello & Maria Cristina Chimenti & Carlo Giglio & Saverino Verteramo, 2020. "An Investigation on the Use by Academic Researchers of Knowledge from Scientific Social Networking Sites," Sustainability, MDPI, vol. 12(22), pages 1-16, November.
    4. Vivek Kumar Singh & Satya Swarup Srichandan & Hiran H. Lathabai, 2022. "ResearchGate and Google Scholar: how much do they differ in publications, citations and different metrics and why?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1515-1542, March.
    5. Bin-Nashwan, Saeed Awadh & Sadallah, Mouad & Bouteraa, Mohamed, 2023. "Use of ChatGPT in academia: Academic integrity hangs in the balance," Technology in Society, Elsevier, vol. 75(C).
    6. Weiwei Yan & Qian Liu & Ruoyu Chen & Shengwei Yi, 2020. "Social networks formed by follower–followee relationships on academic social networking sites: an examination of corporation users," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2083-2101, September.
    7. Sergio Copiello, 2020. "Digital multimedia tools, research impact, stated and revealed preferences: a rejoinder on the issue of video abstracts," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 543-551, April.

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