Factors of trust in data reuse
| Date | 11 November 2019 |
| Published date | 11 November 2019 |
| Pages | 1245-1262 |
| DOI | https://doi.org/10.1108/OIR-01-2019-0014 |
| Author | Ayoung Yoon,Yoo Young Lee |
| Subject Matter | Library & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management |
Factors of trust in data reuse
Ayoung Yoon
Department of Library and Information Science,
Indiana University –Purdue University Indianapolis, Indianapolis,
Indiana, USA, and
Yoo Young Lee
University Library, University of Ottawa, Ottawa, Canada
Abstract
Purpose –The purpose of this paper is to quantitatively examine factors of trust in data reuse from the
reusers’perspectives.
Design/methodology/approach –This study utilized a survey method to test the proposed hypotheses
and to empirically evaluate the research model, which was developed to examine the relationship each factor
of trust has with reusers’actual trust during data reuse.
Findings –This study found that the data producer (H1) and data quality (H3) were significant, as
predicted, while schol arly community (H3) and data in termediary (H4) were not significantly related to
reusers’trust in data.
Research limitations/implications –Further disciplinary specific examinations should be conducted to
complement the study findings and fully generalize the study findings.
Practical implications –The study finding presents the need for engaging data producers in the process of
data curation, preferably beginning in the early stages and encouraging them to work with curation
professionals to ensure data management quality. The study finding also suggests the need for re-defining
the boundaries of current curation work or collaborating with other professionals who can perform data
quality assessment that is related to scientific and methodological rigor.
Originality/value –By analyzing theoretical concepts in empirical research and validating the factors of
trust, this study fills this gap in the data reuse literature.
Keywords Data reuse, Data curation, Trust, Data service, Scholarly communication, Research data
Paper type Research paper
Introduction
The data curation community has been concerned about the issue of trust in data, most
commonly in relation to curation activities performed by data repositories. Trustworthy
Repositories Audit and Certification: Criteria and Checklist/ISO 16363 and Data Seal of
Approval are some well-known efforts to preserve and provide access to trusted content
through repository certification. Since Prieto (2009) argued the need to understand trust
from users’perspectives, several research studies have also demonstrated how a
repository’s intermediary role contributes to data reusers’trust (Donaldson and Conway,
2015; Frank et al., 2017; Yakel et al., 2013; Yoon, 2014a). While past studies underscore the
significance of users’trust in repository and curation activities, they also present the need to
investigate users’trust in the larger context of data reuse. The landscape of data sharing
and reuse is very dynamic, and data exchange for reuse often takes place without any
intermediary, such as in peer-to-peer exchanges.
Trust plays a fundamental role in data reuse. Past trust studies have demonstrated that
trust mediates and enhances knowledge sharing (e.g. Ho et al., 2010; Renzl, 2008) because
trust is fundamental in society and in human relationships (e.g. Gambetta, 1988; Weber et al.,
2004). Van House (2002) discussed the role of trust in a repository context related to sharing
knowledge and scholarship, but the role of trust is also notable outside of a repository
context, where a less intermediary role is involved in data exchange. Data reuse involves
various types of relationships and communication among different stakeholders, such as
data producers, data curators, data reusers and other scholarly communities. Yoon’s (2017)
Online Information Review
Vol. 43 No. 7, 2019
pp. 1245-1262
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-01-2019-0014
Received 9 January 2019
Revised 15 July 2019
Accepted 22 July 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
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Data reuse
study on data reusers’trust development presents this dynamic relationship with various
stakeholders, as well as the social perceptions embedded in reusers’trust judgments.
There has been growing attention toward the concept of trust in data reuse, and past
studies have explored specific aspects of data to be trusted –such as data integrity, quality
and provenance –both in and out of a repository context (Donaldson and Fear, 2011;
Lemieux, 2014; Mayernik et al., 2008; Yoon, 2016). While these studies contribute to the
understanding of the nature of trust, as well as trust factors, few studies solely focus on
understanding and identifying factors of trust in data reuse. Recently, Wolski et al. (2017)
discussed a trust framework for online data services, but the model was theoretical and
without empirical support. Built on previous studies exploring different trust factors during
data reuse, this study aims to quantitatively examine factors of trust in data reuse from the
reusers’perspectives. This study contributes to the field of trust research in data sharing,
reuse and curation by examining trust factors and providing implications to improve
current data reuse and curation practices.
Literature review
Not many studies have formally defined the term reuse. van de Sandt et al. (2019) argued
that the term reuse is a complicated concept and no common definition is proposed across
the disciplines yet. Despite the difficulties of proposing agreed definition across the
disciplines, researchers generally understand it to indicate the use of data by someone who
did not collect it. Therefore, reuse refers to a secondary use of data that is not defined by
their original purpose but is intended to address new problems (Karasti and Baker, 2008;
Zimmerman, 2008; Yoon, 2017). Broadly, reuse includes the reproduction or replication of
prior study results as it contributes to the existing knowledge (King, 1995). Recently, the
concept of repurposing has been added to the discussion of data reuse. In this context, data
reuse has been defined as the use of data more than once for the same purpose, while
data repurposing has been described as the use of data for a completely different purpose
(Data Governance and Quality, 2012). Faniel and Jacobsen (2010) pointed out that the
absence of a reuse definition causes major challenges in providing reusable data, even
though other studies have demonstrated that data reuse can be beneficial to researchers.
Recent literature argued the key benefit of reusing data for the wider research
communities (van de Sandt et al., 2019). Birnholtz and Bietz (2003) and Borgman (2011)
argued shared data can be used not only to validate existing results but also to generate new
findings built on the work of others. Re-analyzed data or data combined with new data can
also help to verify published results or arrive at new conclusions (National Academy of
Science, 2009). Thus, research data must be available for use beyond the purposes for which
they were initially collected to enable others to ask new questions of extant data, advance
solutions for complex human problems and the state of science, reproduce research and
expand the instruments and products of research to new communities (Borgman, 2010,
2011; Hey and Trefethen, 2003; Hey et al., 2009).
Previous research has demonstrated the relationship between data curation and data
reuse, and has suggested that well-curated data is an integral part of data reuse. Coates
(2014) argued that, because data are a key piece of the scholarly record, the management of
data has an impact on the integrity of the scholarly record and on the potential for data
sharing and reuse. Steinhart et al. (2008) argued that a well-developed data curation
infrastructure, by exposing data for reuse, would enable new discoveries and ensure access
to and preservation of scholarly outputs. The Digital Curation Center (DCC) (n.d.) also
argued that good practices of data curation can support data reuse in multiple ways;
they ensure that the appropriate steps are taken to make data available in the first place
(i.e. by presenting data and their associated descriptions in forms that are accessible and
understandable to reusers); they prevent the unauthorized use of data (i.e. by maintaining
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