End users’ trust in data repositories: definition and influences on trust development

Abstract

While repositories' efforts to build trustworthy digital repositories (TDRs) led to the establishment of ISO standards, much less research has been done regarding the user's side, despite calls for an understanding of users' trust of TDRs. In order to learn about users' perspectives on trust in digital repositories, the present study investigated users' definitions of trust and factors that influence users' trust development, particularly addressing the users of three data repositories in the United States. A total of 19 participants were interviewed in this study. The results of this study indicate that users' definition of trust is largely based on a lack of deception, when it comes down to the specific context of data repositories. Regarding factors influencing the development of users' trust in repositories, organizational attributes, user communities (recommendations and frequent use), past experiences, repository processes (documentation, data cleaning, and quality checking), and users' perception of the repository roles were identified.

Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

“…Easy access to data was one of the most influential factors in determining social scientists' satisfaction with data reuse (Faniel, Kriesberg, & Yakel, 2016). Data repositories have a long history in social science, and they are known to support easy access to and reuse of available data through value-adding activities (Daniels, Faniel, Fear, & Yakel, 2012;Yoon, 2014a). However, social scientists searched for more data than was deposited in the repositories.…”

“…Reusers assess data for a good fit for the purpose of their study (Faniel, Kansa, Kansa, Barrera-Gomez, & Yakel, 2013), for data quality (Cragin & Shankar, 2006;Van House, 2002), or generally for reusability (Faniel & Jacobsen, 2010). Social scientists are also concerned with choosing good quality, trustworthy data and avoiding data with errors (Yoon, 2014a(Yoon, , 2016(Yoon, , 2017. Assessing data for each of these qualities requires different criteria; some important assessment factors which have been identified include data producers' ability to generate trustworthy data, other reusers' positive experiences using the data, and soundness of methodology used to produce data (Faniel & Jacobsen, 2010;Faniel, Kansa, Kansa, Barrera-Gomez, & Yakel, 2013;Yoon, 2017;Zimmerman, 2008).…”

“…Data repositories can reduce perceived effort, enhance positive attitudes, and influence the actual behaviors of social scientists regarding data reuse. In the social science context, data repositories play an important role in data sharing and reuse, although data reuse also still occurs through personto-person exchange and interaction (e.g., Faniel & Jacobsen, 2010;Yoon, 2014a). Social science data repositories contribute to data reuse, not just through easy access to data, but also by providing value-added services which ensure current and future use of that data, such as managing provenance, correcting errors, and providing supporting documentation, all of which reduce the effort expectancy required for data reuse (Daniels et al, 2012;Fear & Donaldson, 2012).…”

“…Social science data repositories contribute to data reuse, not just through easy access to data, but also by providing value-added services which ensure current and future use of that data, such as managing provenance, correcting errors, and providing supporting documentation, all of which reduce the effort expectancy required for data reuse (Daniels et al, 2012;Fear & Donaldson, 2012). Data repositories also help data reusers trust data because of the repositories' function, reputation, and structure, which encourages researchers to reuse data from repositories (Yakel, Faniel, Kriesberg, & Yoon, 2013;Yoon, 2014a). Thus, the availability of a data repository would reduce social scientists' perceived effort and positively influence their intentions to reuse other scientists' data.…”

Strengthening of Intellectual Capital Dimension

“…The identified trust building behaviors will be later converted into design features and interface design objects to be used in communication and collaboration systems (Morita & Burns, 2014), has dimensions that are predictability, integrity and benevolence (Darrough, 2006), included repository functions, transparency, structural assurance to include guarantees of preservation and sustainability (Yoon, 2014), three dimension of trust consist of integrity, benevolence and ability (Robbin & Judge, 2013), relationships, community, cooperation and mutual commitment that characterize social capital (Asiaei & Jusoh, 2015).…”