An exploratory study of health scientists’ data reuse behaviors. Examining attitudinal, social, and resource factors

Date17 July 2017
DOIhttps://doi.org/10.1108/AJIM-12-2016-0201
Pages389-407
Published date17 July 2017
AuthorSoohyung Joo,Sujin Kim,Youngseek Kim
Subject MatterLibrary & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management
An exploratory study of health
scientistsdata reuse behaviors
Examining attitudinal, social, and
resource factors
Soohyung Joo
School of Information Science, University of Kentucky, Lexington, Kentucky, USA
Sujin Kim
Division of Biomedical Informatics and School of Information Science,
University of Kentucky, Lexington, Kentucky, USA, and
Youngseek Kim
School of Information Science, University of Kentucky, Lexington, Kentucky, USA
Abstract
Purpose The purpose of this paper is to examine how health scientistsattitudinal, social, and resource
factors affect their data reuse behaviors.
Design/methodology/approach A survey method was utilized to investigate to what extent attitudinal,
social, and resource factors influence health scientistsdata reuse behaviors. The health scientistsdata reuse
research model was validated by using partial least squares (PLS) based structural equation modeling
technique with a total of 161 health scientists in the USA.
Findings The analysis results showed that health scientistsdata reuse intentions are driven by attitude
toward data reuse, community norm of data reuse, disciplinary research climate, and organizational support
factors. This research also found that both perceived usefulness of data reuse and perceived concern involved
in data reuse have significant influences on health scientistsattitude toward data reuse.
Research limitations/implications This research evaluated its newly proposed research model based
on the theory of planned behavior using a sample from the community of scientistsscholar database. This
research showed an overall picture of how attitudinal, social, and resource factors influence health scientists
data reuse behaviors. This research is limited due to its sample size and low response rate, so this study is
considered as an exploratory study rather than a confirmatory study.
Practical implications This research sugges ted for health science re search communities , academic
institutions, and lib raries that diverse st rategies need to be util ized to promote health s cientistsdata
reuse behaviors.
Originality/value This research is one of initial studies in scientific data reuse which provided a holistic
map about health scientistsdata sharing behaviors. The findings of this study provide the groundwork for
strategies to facilitate data reuse practice in health science areas.
Keywords Data reuse, Data repository, Theory of planned behaviour, Data sharing, Social norm,
Health scientists
Paper type Research paper
1. Introduction
A scientific paradigm has shifted, opening the research framework to data sharing and reuse in
order to optimize scholarly findings in health science. Although concerns about data integrity,
technical challenges, privacy and confidentiality, and intellectual property have been raised
(e.g. Akers and Doty, 2013; Shen, 2016), sharing and reusing scientific data has become an
importantpartofbothresearchand scholarly debate (Tenopir et al., 2015). Recently, major
clinical data repositories such as clinicaltrials.gov, The DataSphere Project, PatientLikeMe, and Aslib Journal of Information
Management
Vol. 69 No. 4, 2017
pp. 389-407
© Emerald PublishingLimited
2050-3806
DOI 10.1108/AJIM-12-2016-0201
Received 7 December 2016
Revised 3 April 2017
14 April 2017
Accepted 9 May 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
The authors would like to acknowledge the CoS Pivot (ProQuest) for allowing them to use their scholar
database in recruiting the survey participants. The authors would also like to acknowledge
Darra Hofman for reviewing this paper.
389
Attitudinal,
social, and
resource
factors
others have begun to discuss how to share individual level patient data for secondary use
(Lo, 2015). In particular, the use and sharing among health scientists of clinical data containing
personally identifiable information about human subjects is governed by extensive legislation
and regulation, particularly when that data is made publicly accessible. Given the sensitive
nature of health data, there is concern that health scientists working with human subjects only
reluctantly share their trialsdata (Institute of Medicine, 2013; Diamond et al., 2009). However,
incremental deposits into and use of the data through repositories reflect the clear role that data
reuse will play in the future opportunities and challenges that these scholars will encounter.
Furthermore, health scientists face a new research climate which increases data analytic power
by combining multidimensional clinical data (e.g. electronic health records)with laboratory data
(e.g. gene/protein expression data) from multiple institutions. As health scientists are major
stakeholders in this new open research paradigm, it is important to understand how they
perceive data openness for their scientific discoveries.
As health researchersincreasingly sharetheir data, the reuse of that data requiresresearch
and understanding ( Jao et al., 2015; van Panhuis et al., 2014). A relatively smaller body of
research has focusedon data reuse as compared to data sharing.Data sharing and data reuse
are two sides ofthe same coin (Wallis, 2014); theultimate goal of data sharing isto make data
available to be used by others. Most data reuse research to date has been as part of data
sharing research,rather than specificallyfocusing on data reuse. Datareuserefers to the use
of data collected for one purpose to solve a new research problem (Zimmerman, 2008).
Basically, data reuse allows other researchers to explore new interpretations of data, using
other analyticaltechniques to examine the data in ways thatwere not analyzed in the original
study (Shen, 2016).Data reuse can lead to more findingsfrom the same data set and increase
the knowledge in the field. Despite the importance of data reuse, prior studies lookingat the
health sciences have focused primarily on data curation and sharing. In order to identify
and implement strategies that facilitate data reuse practices, it is imperative to explore the
different factors associated with data reuse behavior. The many factors that affect an
individual researchers intention to reuse someone elses data have not yet been discovered.
This exploratoryresearch examines the relationships among potentialmotivational factors in
data reuse behavior based on the sample collected through an online survey.
2. Literature review
2.1 Data reuse practice
Researchers in the area of e-sciences have explored factors influencing data reuse.
The availability of large computational data sets, along with sophisticated computing
technology and data analysis techniques, has led to the emergence of e-sciences as a new
research area and methodology. E-sciences apply these new computing technologies to
conduct data-driven scientific investigation (Bohle, 2013). It usually relies on large-scale data
and high performance computing through a research process encompassing data collection,
experiment, simulation, and visualization. Carlson and Anderson (2007) identified several
factors motivating e-science activities, such as large data management and requirements
from funding bodies. They emphasized the importance of trust in relation to data reuse
behavior. They claimed that trust in other researchersmeasurement is critical for scientists
to reuse data for new research. To ensure trust, Carlson and Anderson addressed the need to
record the details of changes of each data item as well as to allow researchers to assess the
reliability of data for themselves. Dallmeier-Tiessen et al. (2014) identified various other
factors associated with data sharing and reuse, such as individual incentives, infrastructure,
trust, data discovery, and so forth. They also held that data sharing and reuse behavior
would differ based on scientistsacademic disciplines, countries, ages, and sectors. In their
proposal of an open data framework, Zuiderwijk and Janssen (2014) argued that policy,
context, and organizational environment could influence open data use practices.
390
AJIM
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