Youngseek Kim

Date of Award


Degree Type


Embargo Date


Degree Name

Doctor of Philosophy (PhD)


Library and Information Science


Zhang, Ping


Cyberinfrastructure, Data Reuse, Data Sharing, eScience, Institutional Theory, Multilevel Analysis

Subject Categories

Library and Information Science


In modern research activities, scientific data sharing is essential, especially in terms of data-intensive science and scholarly communication. Scientific communities are making ongoing endeavors to promote scientific data sharing. Currently, however, data sharing is not always well-deployed throughout diverse science and engineering disciplines. Disciplinary traditions, organizational barriers, lack of technological infrastructure, and individual perceptions often contribute to limit scientists from sharing their data. Since scientists' data sharing practices are embedded in their respective disciplinary contexts, it is necessary to examine institutional influences as well as individual motivations on scientists' data sharing behaviors. The objective of this research is to investigate the institutional and individual factors which influence scientists' data sharing behaviors in diverse scientific communities. Two theoretical perspectives, institutional theory and theory of planned behavior, are employed in developing a conceptual model, which shows the complementary nature of the institutional and individual factors influencing scientists' data sharing behaviors. Institutional theory can explain the context in which individual scientists are acting; whereas the theory of planned behavior can explain the underlying motivations behind scientists' data sharing behaviors in an institutional context. This research uses a mixed-method approach by combining qualitative and quantitative methods: (1) interviews with the scientists in diverse scientific disciplines to understand the extent to which they share their data with other researchers and explore institutional and individual factors affecting their data sharing behaviors; and (2) survey research to examine to what extent those institutional and individual factors influence scientists' data sharing behaviors in diverse scientific disciplines. The interview study with 25 scientists shows three groups of data sharing factors, including institutional influences (i.e. regulative pressures by funding agencies and journals and normative pressure); individual motivations (i.e. perceived benefit, risk, effort and scholarly altruism); and institutional resources (i.e. metadata and data repositories). The national survey (with 1,317 scientists in 43 disciplines) shows that regulative pressure by journals; normative pressure at a discipline level; and perceived career benefit and scholarly altruism at an individual level have significant positive relationships with data sharing behaviors; and that perceived effort has a significant negative relationship. Regulative pressure by funding agencies and the availability of data repositories at a discipline level and perceived career risk at an individual level were not found to have any significant relationships with data sharing behaviors