Institutional and individual factors affecting scientists' data‐sharing behaviors: A multilevel analysis

Kim, Youngseek; Stanton, Jeffrey M.

doi:10.1002/asi.23424

Cited by 139 publications

(253 citation statements)

References 105 publications

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“…• Reusing other researchers' data reduces the time/cost/effort I spend on my research (Bock, Zmud, Kim, & Lee, 2005;Kim & Stanton, 2016;Wasko & Faraj, 2000) Perceived concern • If I reuse other researchers' data, I worry that I might misinterpret the data…”

Section: Constructmentioning

confidence: 99%

“…• If I reuse other researchers' data, I worry that I might not publish with that data (Featherman & Pavlou, 2003;Kim & Stanton, 2016;Pavlou, 2003) Perceived effort…”

Section: Constructmentioning

confidence: 99%

“…• Reusing other researchers' data requires time and effort to process data sets for a new study (Davis, 1989;Kim & Stanton, 2016;Thompson, Higgins, & Howell, 1991) Attitude toward data reuse…”

Section: Constructmentioning

confidence: 99%

“…• In my discipline, many of researchers currently reuse data • In my discipline, reusing other researchers' data is a common practice (Kim & Stanton, 2016;Kostova & Roth, 2002;Son & Benbasat, 2007) Availability of data repository…”

Section: Constructmentioning

confidence: 99%

“…• In my discipline, researchers can easily access data repositories to reuse data (Kim & Stanton, 2016;Taylor & Todd, 1995;Venkatesh, Morris, Davis, & Davis, 2003) Intention to reuse data…”

Section: Constructmentioning

confidence: 99%

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Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

Yoon

Kim

2017

Library & Information Science Research

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A B S T R A C TMany disciplines within the social sciences have a dynamic culture of sharing and reusing data. Because social science data differ from data in the hard sciences, it is necessary to explicitly examine social science data reuse. This study explores the data reuse behaviors of social scientists in order to better understand both the factors that influence those social scientists' intentions to reuse data and the extent to which those factors influence actual data reuse. Using an integrated theoretical model developed from the theory of planned behavior (TPB) and the technology acceptance model (TAM), this study provides a broad explanation of the relationships among factors influencing social scientists' data reuse. A total of 292 survey responses were analyzed using structural equation modeling. Findings suggest that social scientists' data reuse intentions are directly influenced by the subjective norm of data reuse, attitudes toward data reuse, and perceived effort involved in data reuse. Attitude toward data reuse mediated social scientists' intentions to reuse data, leading to the indirect influence of the perceived usefulness and perceived concern of data reuse, as well as the indirect influence of the subjective norm of data reuse. Finally, the availability of a data repository indirectly influenced social scientists' intentions to reuse data by reducing the perceived effort involved.

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Section: Constructmentioning

confidence: 99%

“…• If I reuse other researchers' data, I worry that I might not publish with that data (Featherman & Pavlou, 2003;Kim & Stanton, 2016;Pavlou, 2003) Perceived effort…”

Section: Constructmentioning

confidence: 99%

Section: Constructmentioning

confidence: 99%

Section: Constructmentioning

confidence: 99%

Section: Constructmentioning

confidence: 99%

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Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

Yoon

Kim

2017

Library & Information Science Research

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Assessing the Extent and Impact of Online Data Sharing in Eddy Covariance Flux Research

Dai

Xiong

et al. 2018

JGR Biogeosciences

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Research data sharing is appealing for its potential benefits on sharers' scientific impact and is also advocated by various policies. How do scientific benefits and policies correlate with practical ecological data sharing? In this study, we investigated data‐sharing practices in eddy covariance flux research as a typical case. First, we collected researchers' data‐sharing information from major observation networks. Then, we downloaded bibliometric data from the Web of Science and evaluated scientific impact using LeaderRank, a synthetic algorithm that takes both citation and cooperation impacts into consideration. Our results demonstrated the following: (1) specific to eddy covariance flux research, 8% of researchers published information in public data portals, whereas 64% of researchers provided their available data online in a downloadable form; (2) regional differences in data sharing, publications, and observation networks existed; and (3) the data sharers in impact‐ranked ecologists followed a long‐tail distribution, which suggested that, although sharing data is not necessary for researchers to be influential, data sharers are more likely to be high‐impact researchers. Differentiated policies should be proposed to encourage ecologists in the long tail of data sharers, and from regions with little tradition of data sharing, to embrace a more open model of science.

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Scientists' data reuse behaviors: A multilevel analysis

Kim

Yoon

2017

Asso for Info Science & Tech

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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.

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Institutional and individual factors affecting scientists' data‐sharing behaviors: A multilevel analysis

Cited by 139 publications

References 105 publications

Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

Assessing the Extent and Impact of Online Data Sharing in Eddy Covariance Flux Research

Scientists' data reuse behaviors: A multilevel analysis

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