[AISWorld] JSIS cfp. Special Issue: The challenges and opportunities of Œdatification¹ - DEADLINE EXTENDED to January 15, 2016

[Galliers, Robert]

DEADLINE EXTENDED

As a result of a number of requests, we have decided to extend the deadline for submissions of this JSIS special issue to January 15, 2016

CALL FOR PAPERS

The Journal of Strategic Information Systems

Special Issue:
The challenges and opportunities of ‘datification’
Strategic impacts of ‘big’ (and ‘small’) and real time data – for society and for organizational decision makers

In their recent JSIS Viewpoint article, ‘‘Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’’’, Sue Newell and Marco Marabelli raised major concerns regarding our increasing reliance on algorithmic decision-making (Newell and Marabelli, 2015). As a result, they made an urgent call for action for research by IS scholars that would critically assess society’s apparent taken-for-granted and unknowing acquiescence to this increasingly prevalent phenomenon. This call for papers is in response to their invitation for research that critically considers issues arising from our direct reliance on data, whether ‘big’ or ‘small’. The call is also directly linked to the conversation at a U.S. NSF-funded research agenda-setting workshop on the Social, Economic, and Workforce consequences of Big Data (Markus and Topi, 2015).

Newell and Marabelli (2015) argue that the many digital devices that are increasingly in continuous use are capable of enabling the monitoring of ‘‘the minutiae of an individual’s everyday life’’. Such data are often processed by pre-determined algorithms that lead to decisions that follow on directly without further human intervention (often with the claim that the decisions are for the individual’s benefit). Algorithmic decision-making incorporates two main characteristics: 1) the reliance on the part of decision-makers on ‘information’ that is produced by algorithms that process data (often in huge quantities), and 2) the rationale underpinning ‘suggestions’ made by the algorithms that are often either ignored or unknown on the part of those same decision-makers (Mayer-Schonberger and Cukier, 2013).

The strategic value of these data for organizations (mostly businesses, but also government agencies) is unquestionable. However, the implications for individuals and wider society are much less clear – and often questionable.

Given that we as individuals are ‘‘walking data generators’’ (McAfee and Brynjolfsson, 2012, p. 5), we allow others to profile us as potential customers who would therefore ‘benefit’ from personalized products and services. According to Newell and Marabelli, we most often remain ‘‘unaware of how the data they produce are being used, and by whom and with what consequences’’. They further argue that key issues associated, for example, with privacy, control and dependence arise, often as a result of unwitting use, and should therefore ‘‘be brought to the fore and thoughtfully discussed’’. The aim of their Viewpoint article was thus to lay a foundation for this discussion to take place – in the IS community and beyond.

Huge quantities of digital trace data are collected through digitized devices (captured, for example, via social networks, online shopping, blogs, ATM withdrawals and the like) and through in-built sensors. The latter technologies include those that are equipped with GPS systems (e.g., smartphones and other surveillance and monitoring devices) and thus have the ability to identify a user’s location (see Abbas et al., 2014; Michael and Michael, 2011, for social implications, and Lyon, 2001, 2003, 2014, for privacy implications). As such, they fall under the ‘big data’ umbrella (Hedman et al., 2013; Wu and Brynjolfsson, 2009). The big data analytics concept bears similarity to the older and more familiar concept of business intelligence that has been studied for the past decade or so (e.g., Negash, 2004; Power, 2002; Rouibah and Ould-ali, 2002; Shollo and Galliers, 2015; Thomsen, 2003), with the difference that, in the big data context, the sources and types of data are significantly more varied and often get their relevance from real-time processing.

But what of ‘little data’? As Newell and Marabelli point out, ‘‘While using big data and algorithmic decision-making to observe trends and so discriminate between groups of individuals can have social consequences that are potentially unfair, this targeting can now be taken further when data are used not to predict group trends but to predict the behavior of a specific individual’’.

Little data (based on ‘big’ data) thus focuses on the everyday activities of specific individuals, using vast computing capacity to collect and analyze extremely granular data (Munford, 2014). An example would be to record whether an individual is driving safely or not (for the ‘benefit’ of insurance companies or concerned parents of teenagers) – see Abbas et al. (2014) for a literature review.

In a nutshell, the problem with ‘datification’ is that ‘‘somebody else may . . . use the data thus produced – often with purposes different from those originally intended’’. Tensions that have been highlighted include:

  *   􏰀  Control versus freedom (whether informed or uninformed)

  *   􏰀  Dependence versus independence

  *   􏰀  Privacy versus security.


Previous calls for papers have tended to consider the emergence of ‘big data analytics’ from the perspective of businesses and the research possibilities arising (see, for example, Baesens et al., 2014). This call for papers provides a broad, reflexive and critical mandate in the mold of the recent JSIS Viewpoint article (Newell and Marabelli, 2015), the January 2014 research agenda-setting workshop on implications and consequences of big data analytics (Markus and Topi, 2015), last December’s ICIS panel (Topi et al., 2014), and the recent JIT article by Markus (2015). We seek submissions that consider such tensions as these from a strategic, ethical and/or policy perspective. Implications for senior decision makers in organizations (whether public or private) and for individual citizens should be addressed. Markus and Topi (2015) call for research that is value-sensitive, design- oriented, transdisciplinary, sociotechnical, and anticipatory, and we join this call. Considerations of what makes for appropriate research methods applicable in this context would also be welcome, as would critical and trans-disciplinary approaches to the subject matter.


References

Abbas, R., Katina, M., Michael, M.G., 2014. The regulatory considerations and ethical dilemmas of location-based services (LBS): a literature review. Inf. Technol. People 27 (1), 2–20.

Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J., Zhao, J.L., 2014. Transformational Issues of Big Data and Analytics in Networked Business. MIS Quarterly 38 (2), 629–631.

Hedman, J., Srinivasan, N., Lindgren, R., 2013. Digital traces or information systems: sociomateriality made researchable. In: Proceedings of 34th ICIS. Milan, Italy.

Lyon, D., 2001. Surveillance Society: Monitoring Everyday Life. Open University Press, Buckingham, UK.
Lyon, D., 2003. Surveillance as Social Sorting: Privacy, Risk, and Digital Discrimination. Routledge, London.
Lyon, D., 2014. Surveillance, Snowden, and big data: capacities, consequences, critique. Big Data Soc. 1 (2).
Markus, M.L., 2015. New games, new rules, new scoreboards: the potential consequences of big data. J. Inf. Technol.
Markus, M.L., Topi, H. 2015. Big Data, Big Decisions for Government, Business, and Society. Report on a Research Agenda Setting Workshop Funded by the U.S. National Science Foundation, Award# 1348929.

Mayer-Schonberger, V., Cukier, K., 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, New York, NY. McAfee, A., Brynjolfsson, E., 2012. Big data: the management revolution. Harv. Bus. Rev. 90 (10), 60–68.

Michael, K., Michael, M.G., 2011. The social and behavioral implications of location-based services. J. Loc. Based Serv. 5 (3–4), 121–137.
Munford, M., 2014. Rule changes and big data revolutionise Caterham F1 chances. The Telegraph, Technology Section, 23 February 2014.<http://www.telegraph.co.uk/technology/technology-topics/10654658/Rule-changes-and-big-data-revolutionise-Caterham-F1-chances.html>

Negash, S., 2004. Business intelligence, Comms. of the Assoc. for Inf. Syst. 13 (article 15). <http://aisel.aisnet.org/cais/vol13/iss1/15>

Newell, S., Marabelli, M., 2015. Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of‘datification’ J. Strateg. Inf. Syst. 24 (1).

Power, D.J., 2002. Decisions Support Systems: Concepts and Resources for Managers. Quorum Books, Westport, CT.
Rouibah, K., Ould-ali, S., 2002. Puzzle: a concept and prototype for linking business intelligence to business strategy. J. Strateg. Inf. Syst. 11 (2), 133–152.

Shollo, A., Galliers, R.D., 2015. Towards an understanding of the role of business intelligence systems in organisational knowing. Inf. Syst. J. (forthcoming). doi: 10.1111/isj.12071.

Thomsen, E., 2003. BI’s promised land. Intell. Enterp. 6 (4), 21–25.
Topi, H., Clemons, E.K., Lee, M.K.O., Newell, S., Shanks, G., Winter, S.J., 2014. Big Data, Big Decisions: Reflections on AIS’s Role in Ethical Guidance and Oversight. In: Proceedings of 35th ICIS, Auckland, NZ.

Wu, L., Brynjolfsson, E., 2009. The future of prediction: how Google searches foreshadow housing prices and quantities. In: Proceedings of 31st ICIS, Pheonix, AZ.


Important Dates

Submission deadline: December 14, 2015  January 15, 2016
First reviews back: March 21, 2016 (indicative)
Second revisions due by: June 6, 2016 (indicative)
Final acceptance: September 5, 2016 (indicative)
Publication date: Either December 2016 or March 2017 (targeted)


Editors

Professor Bob Galliers, Bentley University, USA and Centre for Information Management, Loughborough University, UK: rgalliers at bentley.edu

Professor Sue Newell, Sussex University, UK: Sue.Newell at sussex.ac.uk

Professor Graeme Shanks, University of Melbourne, Australia: gshanks at unimelb.edu.au

Professor Heikki Topi, Bentley University, USA: htopi at bentley.edu