Reputation, trust, and norms as mechanisms forming academic reciprocity in data sharing: an empirical test of theory of collective action
| DOI | https://doi.org/10.1108/AJIM-08-2021-0242 |
| Published date | 13 May 2022 |
| Date | 13 May 2022 |
| Pages | 1174-1195 |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
| Author | Youngseek Kim |
Reputation, trust, and norms as
mechanisms forming academic
reciprocity in data sharing:
an empirical test of theory
of collective action
Youngseek Kim
Department of Library and Information Science, Sungkyunkwan University,
Seoul, Republic of Korea
Abstract
Purpose –This research investigated how biological scientists’perceived academic reputation, community
trust, and norms all influence their perceived academic reciprocity, which eventuallyleads to their data sharing
intentions.
Design/methodology/approach –A research model was developed based on the theory of collective action,
and the research model was empirically evaluated by using the Structural Equation Modeling method based on
a total of 649 survey responses.
Findings –The results suggest that perceived academic reputation significantly increases perceived
community trust, norm of data sharing, and academic reciprocity. Also, both perceived community trust and
norm of data sharing significantly increases biological scientists’perceived academic reciprocity, which
significantly affect their data sharing intentions.In addition, both perceived community trust and norm of data
sharing significantly affect the relationship between perceived academic reciprocity and data sharing
intention.
Research limitations/implications –This research shows that the theory of collective action provides a
new theoretical lens for understanding scientists’data sharing behaviors based on the mechanisms of
reputation, trust, norm, and reciprocity within a research community.
Practical implications –This research offers several practical implications for facilitating scientists’data
sharing behaviors within a research community by increasing scientists’perceived academic reciprocity
through the mechanisms of reputation, trust, and norm of data sharing.
Originality/value –The collective action perspective in data sharing has been newly proposed in this
research; the research sheds light on how scientists’perceived academic reciprocity and data sharing intention
can be encouraged by building trust, reputation, and norm in a research community.
Keywords Data sharing, Reciprocity, Reputation, Trust, Norm, Biological science
Paper type Research paper
Introduction
In the past decade, scientific data sharing has become important to promote science and
engineering research (Borgman, 2012;Tenopir et al., 2015). Scientific data sharing can help
scientists conduct open and transparent scientific research (Borgman, 2007;Grahe, 2018),
build better research using other scientists’data (Vickers,2011;Goncalves and Musen, 2019),
and eventually advancescience based on the shared data sets (Tenopir et al., 2020;Walliset al.,
2013). However, even though data sharing provides benefits to both scientific communities
AJIM
74,6
1174
The original version of this article was presented at the Annual Meeting of the Association for
Information Science and Technology in Vancouver, Canada on November 10-14, 2018. Both survey data
and instrument have been made publicly available via Open ICPSR (Inter-university Consortium for
Political and Social Research) and can be accessed at https://doi.org/10.3886/E105060V1.
The author would like to acknowledge the ProQuest Pivot for allowing to use its Community of
Scientists (CoS) Scholar Database in recruiting the survey participants.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-3806.htm
Received 23 August 2021
Revised 28 December 2021
20 April 2022
Accepted 23 April 2022
Aslib Journal of Information
Management
Vol. 74 No. 6, 2022
pp. 1174-1195
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-08-2021-0242
and individual scientists, prior studies show that scientificdata sharing is not yet a common
research practice in many disciplines (Tenopir et al., 2020;Kimand Stanton, 2016).
In order to better understand scientists’data sharing behaviors, it is important to
understand the nature of science. Science is considered as a social institution, and scientists
interact with other members of their research communities and also with the broader scientist
community (Ziman, 2000). Scientists work based on the race for priority and the profession’s
reward of a favorable reputation (Merton and Sztompka, 1996). This reputation mechanism –
especially with regards to article publishing –is supported by recognition systems
(e.g. having a published work cited), community trust, and norms (Kling and Spector, 2003).
This mechanism eventually encourages scientists to share their research findings through
publications rather than keeping those findings for themselves (Merton and Sztompka, 1996).
This research considers biological scientists’data sharing behaviors as collective actions
based on the reputation mechanism of science. This research focuses on how biological
scientists’perceptions of academic reputation enhancement, community trust, and norm of
data sharing all affect biological scientists’perceived academic reciprocity, which eventually
leads to their data sharing intentions. This research utilized Ostrom’s (2003) theory of
collective action to explain biological scientists’data sharing intentions based on their
perceptions of reputation, trust, and norm of data sharing. This research especially focuses on
how reputation, trust, and norms can affect biological scientists’perceived academic
reciprocity, which in turn influences their data sharing intentions.
Literature review
Tenopir et al. (2020,2015) argued that data sharing is not yet a well-established research
practice for many scientific disciplines. In the field of biological sciences, several studies have
focused on whether biological scientists allow or deny other researchers access to their data
(Duke and Porter, 2013;Savage and Vickers, 2009). Blumenthal et al. (1997) compared data
sharing rates between genetic researchers and non-genetic researchers, and they reported
that genetic researchers were less likely to share their data corresponding with published
articles than non-genetic researchers in the life sciences. Vogeli et al. (2006) surveyed science
trainees regarding data withholding behaviors and found that 23.0% of trainees were denied
access to publication related materials and 20.6% were denied access to unpublished
research. Vogeli et al. (2006) also reported that 7.9% of science trainees had denied other
researchers’requests to access the data for their own published research. However,
behavioral studies showed that the actual rate of data withholding within the scientific
community is actually higher (Savage and Vickers, 2009;Houldcroft et al., 2017).
Data sharing scholarship has examined the diverse factorseither facilitating or hindering
scientists’data sharing behaviors.Those factors can be divided into individual, institutional,
and resource influences. With regards to institutional factors, prior studies found that
scientists’data sharing behaviors are strongly influenced by funding agencies’policies and
journals’policies aboutdata sharing. In terms of funding agency’s pressure, McCullough et al.
(2008) and Piwowar (2010) found that the regulative pressures from NIH significantly
increased biologicalscientists’data sharing behaviors; however, some studies did not find any
significantrelationship between funding agencies’pressures and data sharing behaviors (Kim
and Zhang, 2015;Kim and Stanton, 2016). In terms of journal pressure, many journals now
require authors to share informationwith other researchers either by depositing their data in
publicly available data repositoriesor by providing the data freely upon request (Vasilevsky
et al., 2017). Piwowar and Chapman (2008) found that journals’policies on data sharing
significantly increased biological scientists’sharing of microarray data in a data repository.
However, Savageand Vickers (2009) reported that only one author provided his/her datawhen
they requested data from ten authors of articles published in a journal with a strong data
sharing policy. In contrast to funding agencies and journals, which encourage data sharing,
Biological
scientists’
academic
reciprocity
1175
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