MIS Quarterly
Call for Papers MISQ Special Issue On Transformational Issues of Big Data and Analytics in Networked Business
Guest Editors
- Bart Baesens, KU Leuven, Belgium
- Ravi Bapna, University of Minnesota, United States
- James R. Marsden, University of Connecticut, United States
- Jan Vanthienen, KU Leuven, Belgium
- J. Leon Zhao, City University of Hong Kong, Hong Kong, China
Motivation and Overview
IBM projects that every day we generate 2.5 quintillon bytes of data (IBM 2013). In relative terms, this means 90% of the data in the world has been created in the last two years. As the data piles up, managing and analyzing these information resources in the most optimal way become critical success factors in creating competitive advantage and strategic leverage.
We view big data and big data analytics as the mother-lode of disruptive change in a networked business environment. Our analytic processes and procedures must change. Our organizations must adapt. Our government and judicial systems must weigh and balance restraints on, or encouragement of, big data collection, analysis, and resulting decision making. No matter the area of application – marketing, product customization, health care, education, free or controlled markets, individual and national security – big data collection and analytics loom as the “Great Disrupters.” The presence and potential impact of big data continues to explode, increasing the need for basic and applied research across disciplines. We believe that IS should take leadership in this emerging field of research in an early stage as networked organizations are already struggling to find directions and strategies on big data investments. To advocate and structure new venues of research, researchers in Information Systems have begun to explore interesting and challenging topics in big data and analytics (Chen et al. 2012; Shmuéli G., Koppius 2011).
We need to develop and enhance analytic methods appropriate for big data that challenge the current corporate infrastructure in terms of data volume, data variety, data change velocity and verocity. We need fundamental research on how big data and big data analytics are likely to impact management structures and processes, organizations, and society. From the firm perspective, key questions include optimal collection, management, integration, analysis and exploitation of big data. From the individual perspective, issues include privacy and ethical use as compared to benefits from personalization. From the government perspective, issues related to privacy, protection of individual rights, and national security all arise as big data collection and analytics expand.
What links the challenges together are the fundamental underlying questions on how IS techniques, processes, and controls can help address the various research issues. We organize the special issue not by perspectives, but by the following four key topic areas, each of which cuts across multiple perspectives, with the understanding that IS is at the heart of successfully addressing each issue:
- Area 1: techniques, processes, and methods for collecting and analyzing big data
- Area 2: impact of big data availability and analytics on IS and Information Governance
- Area 3: privacy, rights, and security
- Area 4: new applications
Scope and Focus of the Special Issue
Topics of interest include, but are not limited to:
- Area 1: Techniques, processes, and methods for collecting and analyzing big data
- New techniques, processes and methods for the collection, management, storage, integration and exploitation of big data in a networked business and ICT environment;
- New techniques, processes and methods for the development, deployment and monitoring of analytical models in a networked business and ICT environment (e.g., analytic model development and representation, key analytic model requirements, model monitoring and backtesting, supporting ICT platforms, role of digital dashboards and OLAP, …)
- New methodological paradigms for big data analytics (e.g. prediction versus causation, fit between machine learning/data mining/econometrics and role of IS as an intersection discipline, measuring fundamental constructs of human behavior,…)
- Area 2: Impact of big data availability and analytics on IS and Information Governance
- Normative set up of big-data in a firm, vertical or horizontal data location strategies, impact on organizational design;
- Organizational, societal and managerial impact of big data and analytics and its influence at executive board level;
- Corporate governance for big data and analytics;
- Role of the CIO and need for a Chief Analytics Officer (CAO);
- Out- versus in-sourcing of big data and analytics (ICT impact, economical and organizational aspects, …);
- Area 3: Privacy, rights, and security
- Privacy and individual rights as compared to benefits from personalization;
- Governmental role in setting up privacy regulation, ensuring national security using big data and analytics;
- Ethical use of big data and analytics;
- Privacy preserving analytics;
- Area 4: New applications
- Using analytics for business process monitoring and improvement (process discovery, conformance and compliance checking, rule mining, delta analysis, …)
- Open data, big data and analytics in the cloud, visual analytics, mobile analytics, real-time analytics, social network/media analytics, analytics in healthcare, ….
Submitted papers can be either quantitative or qualitative and must contain new, unpublished, original and fundamental work relating to MIS Quarterly’s mission with strong managerial, organizational and societal relevance and implications. Purely theoretical papers, simple surveys, incremental contributions and/or journalistic descriptions are highly discouraged. Similarly, purely algorithmic development without practical applications and/or solely benchmarking exercises using test bed data sets are not part of the intended focus. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
Review Process and Deadlines
- Submission deadline: October 1st, 2014
- First round reviews: February 1st, 2015
- Workshop for authors who are invited to submit a revision: March 2015
- Revisions due by: June 15th, 2015
- Review round 2 decisions: September 15th, 2015
- Revisions due by: November 30th, 2015
- Final editorial decision by: December 31st, 2015
Special Issue Editorial Board
- Wil van der Aalst, Department of Mathematics & Computer Science, Eindhoven University of Technology
- Jesse Bockstedt, Eller College of Management, University of Arizona
- Michael Chau, Faculty of Business, University of Hong Kong
- Hsinchun Chen, Eller College of Management, University of Arizona
- Hsing K. Cheng, Warrington College of Business, University of Florida
- Roger Chiang, Lindner College of Business, University of Cincinnati
- Yulin Fang, College of Business, City University of Hong Kong
- Pedro Ferreira, Heinz College, Carnegie Mellon University
- Ram Gopal, School of Business, University of Connecticut
- Shawndra Hill, Wharton School, University of Pennsylvania
- Wolfgang Jank, College of Business, University of South Florida
- Akhil Kumar, Smeal College of Business, Penn State University
- Raymond Lau, College of Business, City University of Hong Kong
- Ting Peng Liang, National Sun Yat-sen University
- David Martens, Department of Engineering Management, University of Antwerp
- Gal Oestreicher-Singer, School of Business Administration, Tel Aviv University
- Eric Overby, Scheller School of Business, Georgia Tech
- Ram Pakath, Gatton College of Business & Economics, University of Kentucky
- Foster Provost, Stern School of Business, New York University
- Sudha Ram, Eller college of management, University of Arizona
- Yuqing Ren, Carlson School of Management, University of Minnesota
- Ramesh Sankaranarayanan, School of Business, University of Connecticut
- Rudy Setiono, School of Computing, National University of Singapore
- Galit Shmuéli, Indian School of Business
- Prasanna Tambe, Stern School of Business, New York University
- Arvind Tripathi, Business School, University of Auckland
- Harry Wang, Lerner College of Business, University of Delaware
- Geoff Webb, Faculty of Information Technology, Monash University
- Michael Zhang, School of Management, Hong Kong University of Science and Technology
- Eric Zheng, Department of Information Systems and Operations Management, University of Texas
References
- Gartner, Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support
- Big Data By 2015, http://www.gartner.com/newsroom/id/2207915 , 2012 IBM, http://www.ibm.com/big-data/us/en/.
- Chen H., Chiang R. H.L., Storey V.C., Business Intelligence and Analytics: From Big
- Data to Big Impact, MIS Quarterly, Vol. 36, No 4, pp.1-24, 2012.
- Shmuéli G., Koppius O.R., Predictive Analytics in Information Systems Research, MIS Quarterly, Vol. 35, No 3, pp. 553-572, 2011.
