Scientists' data reuse behaviors: A multilevel analysis

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.

What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption

“…Theory of Reasoned Action (TRA) Curty, Crowston [40] Theory of Planned Behavior (TPB) Harper and Kim [41], Joo, Kim [17], Kim and Adler [42], Kim and Yoon [43], Yoon and Kim [44], Zenk-Möltgen, Akdeniz [45] Technology Adoption Model (TAM) Yoon and Kim [44] Integrated Unified Theory of Acceptance and Use of Technology (UTAUT) with the two-stage expectation confirmation theory of Information Systems (IS) continuance Zuiderwijk [19], Zuiderwijk and Cligge [46] Institutional theory Kim and Adler [42], Kim and Yoon [43] Coordination Theory Zuiderwijk [19] Grounded Theory da Costa and Leite [47] Motivation theories (e.g. Expectancy Theory, Reinforcement Theory, The Multi-Motive Information Systems Continuance Model)…”

Reusing Data Technical and Ethical Challenges

“…However, there are still technical and ethical questions to face, and, in our opinion, barriers to overcome to fully be able to profit from data sharing and reuse. In addition, sharing and reusing data for secondary analysis is not common in all research fields 69 .…”

The Time Efficiency Gain in Sharing and Reuse of Research Data

“…Zimmerman, 2008;Enke et al, 2012;Bruland et al, 2016;Sprague et al, 2017), and should be clean to be useful for new research. Reuse of an available dataset with the intention to perform a new scientific analysis as well is actually a complicated process (Kim and Yoon, 2017;Zimmerman, 2007). Finding and downloading data are only the first of many steps in this process.…”

“…Downloading is followed by the often necessary appraisal of a dataset on completeness, trustworthiness, and appropriateness, as these characteristics are key factors for re users to reuse the dataset in the end as a basis for their scientific work (Faniel et al, 2015;Yoon, 2016;Kim and Yoon, 2017). If the dataset seems trustworthy, and there is the intention to use it (Kim and Yoon, 2017) the data may still need filtering, cleaning and may need to be integrated with other information, before it is fit for reuse purposes. The fact that a time investment is needed to reuse a dataset, means that less time profit is made at reuse.…”