Full length articleFactors that affect scientists' knowledge sharing behavior in health and life sciences research communities: Differences between explicit and implicit knowledge
Introduction
In the past, knowledge among scientists has mainly been passed on by word of mouth among peers and colleagues, and across personal relationships built upon mutual respect, trust, and shared interests (J. Bartlett and Neugebauer, 2005, Kaye et al., 2009). The emergence of the World Wide Web has provided a solution of knowledge dissemination (Neumann & Prusak, 2007). Knowledge networking, which can be defined as “a special case of social networks in which the links of the network represent shared or related knowledge” (Jones, 2001), has increasingly become more and more prevalent across a number of data-intensive technical and scientific domains including computer and information science, biology and genomics, climate science, neuron science, and high energy physics. In today's online environment, knowledge sharing is not limited to acquaintances or colleagues within specific organizations, but further includes a growing number of geographically dispersed individuals who share knowledge with comparative strangers in online communities.
Awareness of benefits of knowledge networking is rapidly growing in health and life sciences research communities (De Silva & Vance, 2017) First, knowledge networking enables scientists to confirm research findings with minimal processes (Krathwohl, 1993; Neumann & Prusak, 2007). Scientists can learn more from each other by combining their specialized knowledge with new insights, alternative views from other competent experts in a timely manner (De Silva and Vance, 2017, Sonnenwald, 2007).
Secondly, knowledge networking is well-suited for encouraging collaborative scientific discovery and creativity in research communities (De Silva and Vance, 2017, de Matos et al., 2013, Neumann and Prusak, 2007, Ward et al., 2013). It is obvious that knowledge networking helps reduce the cognitive burdens of scientists associated with uncertainty and data complexity (Neumann and Prusak, 2007, Ward et al., 2013). Thus, there has been a dramatic increase in knowledge networking platforms to support health and life sciences research communities such as open-access journals (e.g., PLoS, PubMed or ResearchGate), scientific resource sharing platforms (e.g., myExperiment or Galaxy), and collaborative discussion forums (e.g., SEQanswers or BioStar).
Even with recent advances to further support health and life scientists to share knowledge online, the fact is that the vast majority do not actively share. For instance, a recent empirical study by Park and Gabbard (2013) identified that only a small portion of health and life scientists are actively engaged in online knowledge networking regardless of age or years of research experience. A considerable number of these scientists were identified as lurkers; who consume shared knowledge, but do not contribute to the knowledge base (Preece, Nonnecke, & Andrews, 2004).
For the most part, previous literature has focused on sharing data (Birnholtz, 2007, Field et al., 2009, Kaye et al., 2009) even through in practice scientists do indeed share both data and acquired knowledge from educational training and practical experience, as well as understanding and interpretation of data and information. Since knowledge empowers people to chose from larger sets of actions in the face of uncertain and unusual situations (Neumann & Prusak, 2007), knowledge sharing behavior in scientific communities deserves special attention.
It is time to understand why scientists do or do not engage in knowledge networking and how their motivations, perceived benefits, and perceived barriers affect their sharing behavior. This inquiry in turn, can help user experience (UX) communities that are willing to promote the benefits and mitigate the perceived risks and ultimately encourage a knowledge sharing culture.
The current study therefore aims to (1) examine significant factors that influence knowledge sharing behavior, and to (2) discuss how each of the factors can be addressed to get scientists to share different types of knowledge within virtual communities. We first address differences between data, information, and knowledge as well as the types of knowledge that can be shared in online environments. We show the significance of online knowledge networking among scientists in current data-intensive research processes. We then present hypotheses developed to determine key factors (i.e., determinants) of knowledge sharing behavior and to develop a predictive research model that describes why scientists choose to share or not share the different types of knowledge. We describe our survey instrument and analyze survey responses using Partial Least Squares (PLS) to understand how hypothesized determinants influence scientists' intention to share knowledge in open scientific communities. Based on our results, we distill and discuss (1) what factors influence scientists' motivation and hindrance related to knowledge sharing and how these factors influence scientists’ intentions, and (2) how motivation and hindrance perspectives influence sharing of certain knowledge types. Lastly, we present design implications to encourage and reinforce knowledge sharing motivations.
Section snippets
Background
In this section, we describe key differences among data, information, and knowledge as well as types of knowledge typically shared among individuals. We then address why knowledge networking is important to support data-intensive scientific processes in health and life sciences and discuss key determinants that may affect scientists’ willingness to share knowledge.
Research model and hypotheses
Based on iterative interviews with domain experts and substantive literature review, we expect that motivation factors (reciprocal benefit, anticipated relationship, reputation and altruism) and fear of being scooped simultaneously effect scientists' willingness to share knowledge. Specifically we are interested in understanding how these determinants impact scientists' willingness to share explicit and implicit knowledge respectively, and whether or not different determinants are associated
Participants
We surveyed 141 scientists from institutes and public health and life science organizations in the United States. We recruited scientists of bioinformatics resources since these individuals represent target users who are current or potential users of knowledge networking platforms. All participants were actively involved in health and life sciences research.
Measurement development
We adopted the survey questions from established scales that have been validated by previous studies. Specifically, we adopted survey
Analysis and results
We analyzed the results from three perspectives. First, we briefly show demographic information of participants and experience with bioinformatics resources as well as knowledge networking platforms. This is useful in understanding users’ overall experience situated within the ecology of bioinformatics. We then examined why many scientists lurk rather than engage in online knowledge sharing, to inform design of future knowledge-sharing platforms in this domain. Lastly, we analyzed the data
Discussion and implications
In this study, we attempt to empirically examine current experiences with online knowledge networking and how positive and negative motivational factors affect scientists’ intention to share explicit and/or implicit knowledge.
Conclusion and future study
As health and life sciences become more data intensive and cross-disciplinary, the concept of knowledge networking is rapidly emerging to support contemporary data-intensive research. Although, online knowledge networking has hoped to facilitate scientific collaboration and innovation, current knowledge sharing practices have shown limited effectiveness to date. As such, there is still lack of consideration of ethical issues (e.g., free riders) as well as user expectations when engaging in
Acknowledgements
We'd like to thank the PATRIC expert bioinformaticians and research faculties for their support of this work including: Maulik Shukla, Andrew Warren, Rebecca Wattam, Rebecca Will, and Ronald Kenyon. This project has been funded in whole or in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract no. HHSN272200900040C. The content is solely the responsibility of the authors
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