Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories

doi:10.1016/j.ijinfomgt.2015.04.007

International Journal of Information Management

Volume 35, Issue 4, August 2015, Pages 408-418

https://doi.org/10.1016/j.ijinfomgt.2015.04.007 Get rights and content

Highlights

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Propose data sharing model based on motivational, institutional, and resource factors.
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The research model was validated by the results of a survey of 361 social scientists.
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Career benefit and risk significantly affect social scientists’ data sharing attitude.
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Attitude, effort, and norm significantly influence social scientists’ data sharing.
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Funding agency and journal's pressures and data repository need to be more encouraged.

Abstract

The purpose of this study is to locate individual, institutional, and resource factors that influence data sharing behaviors among social scientists. Given the benefits to the social science disciplines in the advancement of scholarship, and the recent data sharing policy changes of funding agencies, it is necessary to determine the factors that support and impede data sharing behaviors. A research model was developed and validated based on the results of a survey of 361 social scientists. The model is informed by theory of planned behavior and institutional theory to map underlying individual motivations, institutional pressures, and availability of resources facilitating social scientists’ data sharing. It was found that social scientists’ data sharing behaviors are mainly driven by personal motivations (i.e., perceived career benefit and risk, perceived effort, and attitude toward data sharing) and perceived normative pressure. Funding agencies’ pressure, journals’ pressure, and availability of data repository were not found to be significant factors in influencing social scientists’ data sharing. This research suggests that personal motivations and norm of data sharing currently support social scientists’ data sharing; however, institutional pressures by funding agencies and journals and data repository need to be further encouraged to better facilitate social scientists’ data sharing behaviors.

Introduction

Raw data sets have become important “information currency” for scholarly communication (Davis & Vickery, 2007). Not only do data sets add value to traditional journal publications, they also increase transparency and facilitate high quality, continued research. Existing studies confirm, however, that data sharing in the social sciences is not widely practiced due to a variety of factors, including infrastructural and institutional barriers, ethical concerns, and personal reasons. The objective of the present study is to investigate the institutional and individual factors that influence social scientists’ data sharing behaviors and to provide a model, based on institutional theory and the theory of planned behavior, for understanding and predicting behavior. Examining both individual motivation and institutional contexts provides a holistic view of data sharing practices across diverse social science disciplines and can inform policy-making decisions regarding the design and practices surrounding data archives. As data sharing is not a norm, this study can help to identify ways to encourage and support data sharing in the social sciences.

Reasons for encouraging data sharing in the social sciences are many (Fienberg, 1994, King, 1995). Data sharing increases the transparency of quantitative analytic work, thereby lending more credibility to research findings, providing evidence to support analytic frameworks and decisions, and a source for researchers to consult when considering how to build upon existing studies. Having data openly available means that replication and verification is made immediately possible. Shared data allows testing different hypotheses and building better research studies. Furthermore, openly shared data facilitates participation from multiple perspectives, allowing access to the data for more disciplines and for researchers from different backgrounds. It reduces costs by avoiding the duplication of data collection efforts. Additionally, data made available through sharing contributes to the education of students. It is important to note that national scientific organizations and funding agencies have increasingly issued data archiving policies, and agencies including the National Science Foundation (NSF), National Institutes of Health (NIH), and the Institute for Museum and Library Services (IMLS) now require data sharing and management plans as part of grant applications. Given the benefits to the social science disciplines in the advancement of scholarship, and the recent data sharing policy changes of funding agencies, there is a particularly pressing need to determine the factors that impede and support data sharing practices.

Data sharing has been defined somewhat differently in various studies. For the purpose of this research, data sharing is broadly defined as an individual scientist's behavior in providing their raw (or preprocessed) data of his/her published work to other scientists by making it accessible through central/local data repositories or by sending data via personal communication methods upon request.

The first sections of this paper present the literature relevant to the study and an overview of the theoretical frameworks that support our model for data sharing behavior. Section 4 describes the development of the model and hypotheses, and Section 5 provides a description of the research method, including how the survey data was collected and used. In Section 6 we present the results and analysis of the data, and Section 7 provides a discussion, which includes an account of qualitative data derived from participants’ comments. The paper concludes by addressing limitations and considerations for further study.

Section snippets

Literature review

Researchers have examined a variety of dimensions related to data sharing. Across this research there is consensus that, although there is increasing awareness of the benefits of openly shared data, policies and standards are inconsistent across the social science disciplines and institutions, and data sharing is not a common practice among social scientists. Freese (2007) has argued that disciplines within the social sciences have different norms for data sharing, and suggests that much of the

Theoretical framework

The present study makes strides toward building a model to explain and predict data sharing behavior by building upon new institutional theory (DiMaggio and Powell, 1983, Scott, 2001) and the theory of planned behavior (Ajzen, 1991, Fishbein and Ajzen, 1975). New institutional theory accounts for the context in which individual social scientists are acting, whereas the theory of planned behavior helps to explain the underlying motivations and availability of resources facilitating social

Research model

The research model below provides a specific map of social scientists’ data sharing behaviors. The research model is designed to understand and distinguish both individual, institutional, and resource factors influencing social scientists’ data sharing behaviors. The theory of planned behavior can provide insights into how social actors’ behaviors are influenced by their attitudinal beliefs and attitude toward their behaviors and perceived behavioral controls or perceived availabilities of

Research method

A survey was used to systematically investigate the extent to which the data sharing factors identified from the theory of planned behavior and institutional theory influence social scientists’ actual data sharing behaviors. The survey data have been analyzed using descriptive statistics, reliability and validity analysis, and structural analysis. Participants were allowed to provide comments, an account of which is included in Section 7 of this paper.

Data analysis and results

A Structural Equation Modeling (SEM) approach was employed as a primary data analysis method in order to evaluate the proposed research model and hypothesized relationships. This research used a variance-based Partial Least Squares (PLS) method over a covariance-based SEM method because the PLS-SEM is appropriate for the exploratory studies with fewer limitations (Chin, 1998). The PLS does not require normality (Hair, Black, Babin, Anderson, & Tatham, 2006), and it can be done with a small

Discussion and conclusions

This final section describes the findings and includes written comments provided by survey participants. This study found that social scientists’ data sharing behaviors are significantly driven by personal motivations (i.e., perceived career benefit and risk, perceived effort, and attitude toward data sharing) and perceived normative pressure. As data sharing in the social sciences is not a widely established research practice, and as social science researchers are more apt to engage in

Limitations and future research

Continuing research in scientific data sharing should expand upon the relationships examined in this research, as well as include closer investigations into some of the research constructs employed in this research. Future research will also need to consider organizational-level factors influencing social scientists’ data sharing behaviors, such as organizational supports and resources involved in scientists’ data sharing behaviors.

Where the research found that there were not significant

Acknowledgements

We would like to acknowledge the ProQuest Pivot for allowing us to use its Community of Scientists (CoS) Scholar Database in recruiting the survey participants.

Youngseek Kim is an assistant professor in the School of Library and Information Science at the University of Kentucky. He completed his Ph.D. at Syracuse University, and he received the Eugene Garfield Doctoral Dissertation Award from the Association for Library and Information Science Education. His current research efforts in eScience try to understand the nature of scientists’ data practices, focusing on data sharing and reuse, the education of eScience professionals, and adoption and use

References (99)

I. Ajzen
The theory of planned behavior
Organizational Behavior and Human Decision Process
(1991)
P. Andreoli-Versbach et al.
Open access to data: An ideal professed but not practised
Research Policy
(2014)
A. Bajaj et al.
A feedback model to understand information system usage
Information & Management
(1998)
C.-M. Chiu et al.
Understanding Web-based learning continuance intention: The role of subjective task value
Information & Management
(2008)
H.M. Davis et al.
Datasets, a shift in the currency of scholarly communication: Implications for library collections and acquisitions
Serials Review
(2007)
M.S. Featherman et al.
Predicting e-services adoption: A perceived risk facets perspective
International Journal of Human Computer Studies
(2003)
K.E. Kolekofski et al.
Beliefs and attitudes affecting intentions to share information in an organizational setting
Information and Management
(2003)
J. Lu et al.
Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology
The Journal of Strategic Information Systems
(2005)
M. McLure Wasko et al.
it is what one does: Why people participate and help others in electronic communities of practice
Journal of Strategic Information Systems
(2000)
S. Ryu et al.
Knowledge sharing behavior of physicians in hospitals
Expert Systems with Applications
(2003)

H.-D. Yang et al.

It's all about attitude: Revisiting the technology acceptance model

Decision Support Systems

(2004)

A. Abbott

Notes on replication

Sociological Methods & Research

(2007)

I. Ajzen et al.

Uncerstanding attitudes and predicting social behavior

(1980)

I. Ajzen et al.

The influence of attitudes on behavior

J.C. Anderson et al.

Structural equation modeling in practice: A review and recommended two-step approach

Psychological Bulletin

(1988)

P. Arzberger et al.

Promoting access to public research data for scientific, economic, and social development

Data Science Journal

(2004)

N.F. Awad et al.

The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization

MIS Quarterly

(2006)

M.J. Bebeau et al.

Authorship and publication practices in the social sciences: Historical reflections on current practices

Science and Engineering Ethics

(2011)

S. Bell

Institutionalism

D. Blumenthal et al.

Data withholding in genetics and the other life sciences: Prevalences and predictors

Academic Medicine

(2006)

G.-W. Bock et al.

Breaking the myths of rewards: An exploratory study of attitudes about knowledge sharing

Information Resources Management Journal

(2002)

G.-W. Bock et al.

Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate

MIS Quarterly

(2005)

C.L. Borgman

The digital future is now: A call to action for the humanities

Digital Humanities Quarterly

(2009)

C.L. Borgman et al.

Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries

International Journal on Digital Libraries

(2007)

A. Burton-Jones et al.

Reconceptualizing system usage: An approach and empirical test

Information Systems Research

(2006)

E.G. Campbell et al.

Data-sharing and data-withholding in genetics and the life sciences: results of a national survey of technology transfer officers

Journal of Health Care Law and Policy

(2003)

E.G. Campbell et al.

Data withholding in academic genetics – Evidence from a national survey

Journal of the American Medical Association

(2002)

S.J. Ceci

Scientists attitudes toward data sharing

Science Technology & Human Values

(1988)

W.W. Chin

The partial least squares approach to structural equation modeling

Modern Methods for Business Research

(1998)

S.Y. Choi et al.

The effects of socio-technical enablers on knowledge sharing: An exploratory examination

Journal of Information Science

(2008)

L. Cliggett

Qualitative data archiving in the digital age: Strategies for data preservation and sharing

The Qualitative Report

(2013)

M.H. Cragin et al.

Data sharing, small science and institutional repositories

Philosophical Transactions of the Royal Society A – Mathematical Physical and Engineering Sciences

(2010)

J.R. Craig et al.

Psychology in action – Retention of raw data – Problem revisited

American Psychologist

(1973)

F.D. Davis

Perceived usefulness, perceived ease of use, and user acceptance in information technology

MIS Quarterly

(1989)

F.D. Davis et al.

User acceptance of computer technology: A comparison of two theoretical models

Management Science

(1989)

A. Dickinger et al.

The role of perceived enjoyment and social norm in the adoption of technology with network externalities

European Journal of Information Systems

(2008)

P.J. DiMaggio et al.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields

American Sociological Review

(1983)

C. Emmelhainz

Controlled vocabulary standards for anthropological datasets

International Journal of Digital Curation

(2014)

A. Field

Discovering statistics using SPSS

(2009)

S.E. Fienberg

Sharing statistical data in the biomedical and health sciences: Ethical, institutional, legal, and professional dimensions

Annual Review of Public Health

(1994)

M. Fishbein et al.

Belief, attitude, intention, and behavior

(1975)

C. Fornell et al.

Two structural equation models: LISREL and PLS applied to consumer exit-voice theory

Journal of Marketing Research (JMR)

(1982)

C. Fornell et al.

Structural equation models with unobservable variables and measurement error: Algebra and statistics

Journal of Marketing Research

(1981)

N.F. Foster et al.

Understanding faculty to improve content recruitment for institutional repositories

D-Lib Magazine

(2005)

J. Freese

Replication standards for quantitative social science why not sociology?

Sociological Methods & Research

(2007)

A.H. Gold et al.

Knowledge management: An organizational capabilities perspective

Journal of Management Information Systems

(2001)

J.F. Hair et al.

Multivariate data analysis

(2006)

P.P.M.A.R. Heugens et al.

Structure! agency! (and other quarrels): A meta-analysis of institutional theories of organization

Academy of Management Journal

(2009)

J. Jasperson et al.

A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems

MIS Quarterly

(2005)

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Melissa Adler is Assistant Professor in the School of Library and Information Science and Faculty Affiliate with the Committee on Social Theory at the University of Kentucky. She received her PhD from the School of Library and Information Studies with a PhD minor in Gender and Women's Studies from University of Wisconsin–Madison. She is currently serving as Chair-elect for the Classification Research Group of the Association for Information Science & Technology. Her book manuscript, Perverse Subjects: Becoming Bodies in the Library, is currently under review.

¹: Tel.: +1 859 218 2294; fax: +1 859 257 4205.

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Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories

Highlights

Abstract

Introduction

Section snippets

Literature review

Theoretical framework

Research model

Research method

Data analysis and results

Discussion and conclusions

Limitations and future research

Acknowledgements

Organizational Behavior and Human Decision Process

Research Policy

Information & Management

Information & Management

Serials Review

International Journal of Human Computer Studies

Information and Management

The Journal of Strategic Information Systems

Journal of Strategic Information Systems

Expert Systems with Applications

Decision Support Systems

Notes on replication

Sociological Methods & Research

Uncerstanding attitudes and predicting social behavior

The influence of attitudes on behavior

Structural equation modeling in practice: A review and recommended two-step approach

Psychological Bulletin

Promoting access to public research data for scientific, economic, and social development

Data Science Journal

The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization

MIS Quarterly

Authorship and publication practices in the social sciences: Historical reflections on current practices

Science and Engineering Ethics

Institutionalism

Data withholding in genetics and the other life sciences: Prevalences and predictors

Academic Medicine

Breaking the myths of rewards: An exploratory study of attitudes about knowledge sharing

Information Resources Management Journal

Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate

MIS Quarterly

The digital future is now: A call to action for the humanities

Digital Humanities Quarterly

Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries

International Journal on Digital Libraries

Reconceptualizing system usage: An approach and empirical test

Information Systems Research

Data-sharing and data-withholding in genetics and the life sciences: results of a national survey of technology transfer officers

Journal of Health Care Law and Policy

Data withholding in academic genetics – Evidence from a national survey

Journal of the American Medical Association

Scientists attitudes toward data sharing

Science Technology & Human Values

The partial least squares approach to structural equation modeling

Modern Methods for Business Research

The effects of socio-technical enablers on knowledge sharing: An exploratory examination

Journal of Information Science

Qualitative data archiving in the digital age: Strategies for data preservation and sharing

The Qualitative Report

Data sharing, small science and institutional repositories

Philosophical Transactions of the Royal Society A – Mathematical Physical and Engineering Sciences

Psychology in action – Retention of raw data – Problem revisited

American Psychologist

Perceived usefulness, perceived ease of use, and user acceptance in information technology

MIS Quarterly

User acceptance of computer technology: A comparison of two theoretical models

Management Science

The role of perceived enjoyment and social norm in the adoption of technology with network externalities

European Journal of Information Systems

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields

American Sociological Review

Controlled vocabulary standards for anthropological datasets

International Journal of Digital Curation

Discovering statistics using SPSS

Sharing statistical data in the biomedical and health sciences: Ethical, institutional, legal, and professional dimensions

Annual Review of Public Health

Belief, attitude, intention, and behavior

Two structural equation models: LISREL and PLS applied to consumer exit-voice theory