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When are researchers willing to share their data? – Impacts of values and uncertainty on open data in academia

Author

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  • Stefan Stieglitz
  • Konstantin Wilms
  • Milad Mirbabaie
  • Lennart Hofeditz
  • Bela Brenger
  • Ania López
  • Stephanie Rehwald

Abstract

Background: E-science technologies have significantly increased the availability of data. Research grant providers such as the European Union increasingly require open access publishing of research results and data. However, despite its significance to research, the adoption rate of open data technology remains low across all disciplines, especially in Europe where research has primarily focused on technical solutions (such as Zenodo or the Open Science Framework) or considered only parts of the issue. Methods and findings: In this study, we emphasized the non-technical factors perceived value and uncertainty factors in the context of academia, which impact researchers’ acceptance of open data–the idea that researchers should not only publish their findings in the form of articles or reports, but also share the corresponding raw data sets. We present the results of a broad quantitative analysis including N = 995 researchers from 13 large to medium-sized universities in Germany. In order to test 11 hypotheses regarding researchers’ intentions to share their data, as well as detect any hierarchical or disciplinary differences, we employed a structured equation model (SEM) following the partial least squares (PLS) modeling approach. Conclusions: Grounded in the value-based theory, this article proclaims that most individuals in academia embrace open data when the perceived advantages outweigh the disadvantages. Furthermore, uncertainty factors impact the perceived value (consisting of the perceived advantages and disadvantages) of sharing research data. We found that researchers’ assumptions about effort required during the data preparation process were diminished by awareness of e-science technologies (such as Zenodo or the Open Science Framework), which also increased their tendency to perceive personal benefits via data exchange. Uncertainty factors seem to influence the intention to share data. Effects differ between disciplines and hierarchical levels.

Suggested Citation

  • Stefan Stieglitz & Konstantin Wilms & Milad Mirbabaie & Lennart Hofeditz & Bela Brenger & Ania López & Stephanie Rehwald, 2020. "When are researchers willing to share their data? – Impacts of values and uncertainty on open data in academia," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.
  • Handle: RePEc:plo:pone00:0234172
    DOI: 10.1371/journal.pone.0234172
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    References listed on IDEAS

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    1. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    2. Heather A Piwowar & Roger S Day & Douglas B Fridsma, 2007. "Sharing Detailed Research Data Is Associated with Increased Citation Rate," PLOS ONE, Public Library of Science, vol. 2(3), pages 1-5, March.
    3. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    4. A. Willem & M. Buelens, 2005. "Knowledge Sharing in Public Sector Organizations: The Effect of Organizational Characteristics on Interdepartmental Knowledge Sharing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/344, Ghent University, Faculty of Economics and Business Administration.
    5. Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2015. "What Drives Academic Data Sharing?," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-25, February.
    6. Vicente-Saez, Ruben & Martinez-Fuentes, Clara, 2018. "Open Science now: A systematic literature review for an integrated definition," Journal of Business Research, Elsevier, vol. 88(C), pages 428-436.
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    Cited by:

    1. Isabel Steinhardt & Mareike Bauer & Hannes Wünsche & Sonja Schimmler, 2023. "The connection of open science practices and the methodological approach of researchers," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3621-3636, August.
    2. Christian Vidal-Cabo & Enrique Alfonso Sánchez-Pérez & Antonia Ferrer-Sapena, 2024. "Perception and Reuse of Open Data in the Spanish University Teaching and Research Community," Data, MDPI, vol. 9(10), pages 1-21, October.

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