Liebe Kolleginnen und Kollegen,

 

Gerne möchte ich Sie darauf aufmerksam machen, dass die Einreichfrist im Call for Papers der Special Issue “Data Governance, Digital Innovation, and Societal Challenges” in Information & Organization (VHB JQ 3 „B“; CABS AJG „3“) auf den 22. November 2021 verlängert wurde. Wir freuen uns über Einreichungen aus der WINFO-Community!

 

https://www.journals.elsevier.com/information-and-organization/call-for-papers/data-governance-digital-innovation-and-grand-challenges

 

Beste Grüße

Lauri Wessel

 

Information and Organization

Call for Papers Special issue on

Data Governance, Digital Innovation and Grand Challenges

 

 

Guest Editors: 

 

Elizabeth Davidson, University of Hawai’i, Shidler College of Business

Jenifer Winter, University of Hawai’i, School of Communications

Lauri Wessel, European New School of Digital Studies @ European University Viadrina Frankfurt (Oder)

Susan Winter, University of Maryland College Park, School of Information Studies

 

Today, previously unimaginable varieties and volumes of data about nearly all aspects of human actions are collected, aggregated, and analyzed (Abbasi et al. 2016; George et al. 2014; Lycett 2013). These data are valuable resources for multiple stakeholders to extract value consistent with their interests and priorities, which may support or conflict with other stakeholders’ interests (Gangadharan 2014; Winter 2014). Data resources are typically treated as proprietary assets of the organizations (and IT platforms) that capture or otherwise acquire data (Data Governance Institute, nd). However, if we consider data as societal resources rather than (or in addition to) privately held assets, then how data governance takes shape and whether governance addresses diverse goals, priorities, and interests become important research and policy questions (Rosenbaum, 2010; Winter and Davidson, 2017; Zuboff 2015).

 

Data governance is a broad concept that includes the processes and institutional structures for managing data, and the policies and practices for granting accessing to and authorizing acceptable uses of data (Data Governance Institute, nd ; Rosenbaum, 2010). Information technologies and systems not only generate the growing stockpiles of digitized data but are also crucial elements in data governance policies and practices (Legner et al., 2020; Markus and Bui, 2012).

 

Our goals in this special issue are to stimulate and advance academic and practice-focused knowledge on data governance that addresses the broad range of research questions associated with advancing digital innovation and for meeting grand challenges.

 

Data governance and digital innovation. There is immense potential for digital innovation with emerging technologies such as AI, machine learning, analytics, and IoT. These emerging technologies depend in large part on large, accessible data resources, for instance in AI/machine learning. Whether data collected in once context can be effectively, fairly and ethically applied in other contexts raises data governance and stewardship issues, such as whether a data set is implicitly biased or whether individuals whose data are represented consent to this reuse or not (Gangadharan 2014; Janssen et al. 2013; Winter 2014; Zuboff 2015). Equally important are questions about facilitating access to and reuse of data for innovation.  Addressing such questions entail an array of policy, organizational, practice, and technological approaches (George et al, 2014).

 

Data governance and grand challenges: Grand challenges are critical national or global problems, which might be solved though science, technology and innovation but which require moving beyond existing organizational, technological, and scientific approaches. There is immense potential for data resources to be used to address grand challenges such as uneven economic development, government transparency, energy conservation, and advances in medical research and care, to name just a few. How potentially relevant data are governed – by whom, for what purposes, on whose behalf and through what sociotechnical actions and structures – present imperative questions to business leaders, policy makers, technologists, and researchers (Perkmann and Schildt, 2015; Susha et al, 2017).

 

We seek submissions that address how the benefits, risks, and consequences of today’s data-rich world can be understood and managed, in part, through the lens of data governance, so that data resources might be harnessed for innovation and societal good amidst many competing value claims and substantial risks for privacy and security.  Topics of interest include, but are not limited to:

 

·      Cases / analyses of organizational forms and practices for governing data in specific socio-economic contexts (healthcare, education, energy use, commerce, sports, etc.) that contribute to social-theoretical understanding of data governance.

·      Administrative, organizational and technological practices that allow anonymized personal data to be shared for research or policy, as well as risks of re-identification. Papers specifying algorithms are not appropriate for this journal, though organizational application of technological solutions may be.

·      Ethical and practical concerns for researchers or policy makers repurposing data resources with businesses, technology firms, and other researchers.

·      Rationalities or logics that guide policy and decision-making about data aggregation, stockpiling, monetizing, reuse within and across organizational settings.

·      Theory-informed studies of the implications of privacy and data use regulations (e.g., GDPR, California data use law) on organizational practices for data governance.

·      Case studies of how “privacy by design” is implemented and the implications for data governance.

·      Data stewardship roles, responsibilities, processes or practices for ensuring data resources have sufficient quality for intended uses, eg. are accurate, complete and/or representative for intended uses.

·      Opportunities, problems, or failures of data stewardship and governance; unintended consequences of utilizing aggregated data beyond the context where data originates.

·      Cases / analyses of data governance challenges that inform policy and regulatory innovation or reform related to data governance (in local, national, or international jurisdictions).

·      Studies that develop design theory about how to design data governance structures and practices for addressing grand challenges.

 

Regular submission to Information and Organization, as well as submissions to the Research Impact and Contributions to Knowledge (RICK) section will be considered. Authors are encouraged to review the aims and scope statement for the journal (https://www.journals.elsevier.com/information-and-organization) and review abstracts of recent publications via the Science Direct link on the website to better understand the journal’s focus and publication genre.  Regular submissions should have the potential for a substantive contribution to theory that complements empirical results or case studies reports. RICK submissions are briefer (5000 words) and address the impact or translation of scholarly knowledge broadly. Authors considering a RICK submission should review the overview of RICK genre on the website and recent RICK publications (https://www.journals.elsevier.com/information-and-organization/call-for-papers/special-section-call-for-papers-research-impact-and-contribt).

 

Important dates:

Submission deadline:      November 22, 2021

First round decisions:      April 15, 2022

Revisions due:                September 15, 2022

Second round decisions:  December 15, 2022

Final submission:            January 31, 2023

 

 

 

References

Abbasi, Ahmed, Suprateek Sarker, and Roger HL Chiang. "Big data research in information systems: Toward an inclusive research agenda." Journal of the Association for Information Systems 17.2 (2016): 3.

Data Governance Institute. (nd). Data governance definition. Retrieved from http://www.datagovernance.com/adg_data_governance_definition/

Gangadharan, S. (Ed.) (2014). Data and discrimination:  Collected essays. Washington DC: Open Technology Institute.

George, G., Haas & Pentland. 2014. “Big Data and Management,” Academy of Management Journal (57:2), pp. 321-326.

Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 101493.

Legner, Christine, Tobias Pentek, and Boris Otto. "Accumulating Design Knowledge with Reference Models: Insights from 12 Years’ Research into Data Management." Journal of the Association for Information Systems 21.3 (2020): 2.7

Link, Georg J.P.; Lumbard, Kevin; Conboy, Kieran; Feldman, Michael; Feller, Joseph; George, Jordana; Germonprez, Matt; Goggins, Sean; Jeske, Debora; Kiely, Gaye; Schuster, Kristen; and Willis, Matt (2017) "Contemporary Issues of Open Data in Information Systems Research: Considerations and Recommendations," Communications of the Association for Information Systems: Vol. 41 , Article 25.

Lycett, M. 2013 “Datafication”: Making Sense of (Big) Data in a Complex World,” European Journal of Information Systems (22:4), pp. 381-386.

Markus, M. L., & Bui, Q. N. (2012). Going concerns: The governance of interorganizational coordination hubs. Journal of Management Information Systems, 28(4), 163-198.

Perkmann, M., & Schildt, H. (2015). Open data partnerships between firms and universities: The role of boundary organizations. Research Policy, 44(5), 1133-1143.

Rosenbaum, S. 2010. “Data Governance and Stewardship: Designing Data Stewardship Entities and Advancing Data Access”, Health Services Research (45:5p2), pp. 1442-1455.

Susha, I., Janssen, M., Verhulst, S. (2017) Data collaboratives as “bazaars”?: A review of coordination problems and mechanisms to match demand for data with supply. Transforming Government: People, Process and Policy, 11(1): 157-172

Winter, J.S. (2014). Surveillance in ubiquitous network societies: Normative conflicts related to the consumer in-store supermarket experience in the context of the Internet of Things. Ethics and Information Technology, 16(1), 27-41. doi:10.1007/s10676-013-9332-3.

Winter, J.S., & Davidson, E.  (2017). “Investigating values in personal health data governance models.” 23^rd Americas Conference on Information Systems (AMCIS). August 2017, Boston, MA.

Zuboff, S. 2015. “Big Other: Surveillance Capitalism and the Prospects of an Information Civilization,” Journal of Information Technology (30:1), pp. 75-89.