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Scientists' data reuse behaviors: A multilevel analysis

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  • Youngseek Kim
  • Ayoung Yoon

Abstract

This study explores the factors that influence the data reuse behaviors of scientists and identifies the generalized patterns that occur in data reuse across various disciplines. This research employed an integrated theoretical framework combining institutional theory and the theory of planned behavior. The combined theoretical framework can apply the institutional theory at the individual level and extend the theory of planned behavior by including relevant contexts. This study utilized a survey method to test the proposed research model and hypotheses. Study participants were recruited from the Community of Science's (CoS) Scholar Database, and a total of 1,528 scientists responded to the survey. A multilevel analysis method was used to analyze the 1,237 qualified responses. This research showed that scientists' data reuse intentions are influenced by both disciplinary level factors (availability of data repositories) and individual level factors (perceived usefulness, perceived concern, and the availability of internal resources). This study has practical implications for promoting data reuse practices. Three main areas that need to be improved are identified: Educating scientists, providing internal supports, and providing external resources and supports such as data repositories.

Suggested Citation

  • Youngseek Kim & Ayoung Yoon, 2017. "Scientists' data reuse behaviors: A multilevel analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2709-2719, December.
    • Handle: RePEc:bla:jinfst:v:68:y:2017:i:12:p:2709-2719
    DOI: 10.1002/asi.23892
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    1. Christa E. Winkler & Rebecca Fay Berenbon, 2021. "Validation of a survey for measuring scientists' attitudes toward data reuse," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 449-453, April.
    2. Xiaoguang Wang & Qingyu Duan & Mengli Liang, 2021. "Understanding the process of data reuse: An extensive review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(9), pages 1161-1182, September.
    3. Bettina Suhr & Johanna Dungl & Alexander Stocker, 2020. "Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-26, September.
    4. Claire M Mason & Paul J Box & Shanae M Burns, 2020. "Research data sharing in the Australian national science agency: Understanding the relative importance of organisational, disciplinary and domain-specific influences," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    5. Dolores Gallardo-Vázquez & Flavio Hourneaux Junior & Marcelo Luiz Dias da Silva Gabriel & Luis Enrique Valdez-Juárez, 2021. "On Earth as It Is in Heaven: Proxy Measurements to Assess Sustainable Development Goals at the Company Level through CSR Indicators," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    6. Anneke Zuiderwijk & Rhythima Shinde & Wei Jeng, 2020. "What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-49, September.

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