The European Journal of Information Systems

Special Issue Call for Papers: 

Dark Side of Analytics and AI

 **Closed for new submissions**

Guest Editors

  • Patrick Mikalef, Department of Computer Science, Norwegian University of Science and Technology (NTNU), Norway email:

  • Aleš Popovic, School of Economics and Business, University of Ljubljana, Slovenia

  • Jenny Eriksson Lundström, Department of Informatics and Media, Uppsala University, Sweden email:

  • Kieran Conboy, J.E. Cairnes School of Business & Economics, National University of Ireland Galway, Ireland email:

Overview of Special Issue

We are living in an age of data deluge. Everywhere we go, everything we say, everything we buy leaves a digital footprint that is recorded and stored (Vidgen, Shaw, & Grant, 2017). Combining big data, analytics and artificial intelligence (AI) have signaled a revolution in the way data can be processed and the types of insights that can be generated (Kersting & Meyer, 2018). Mainstream information systems research generally celebrates the proliferation of analytics and AI for its economic and business potential, and its capacity to create novel ways of working, organizing and developing new products and services. In contrast, critical research has highlighted some of the negative consequences of such technologies, where the emergence of big data analytics and AI threaten individual rights of organisational members, and hinder business value. From a macro perspective, the potential of such technologies coupled with the immense data that large organisations now manage to control, may cause a power imbalance and unwanted authority of certain businesses (Zuboff, 2015). The concentrated control of data in a small number of organisations threaten to create a stark imbalance, and there is a pressing need to address the question of power and authority with the widest possible frame. Clearly, there are many aspects concerning the moral, social, and psychological implications for our everyday lives (O’Neil, 2015).

With this call for papers, we aim to extend the critical reflection on the impact and unintended consequences of big data analytics and AI. Specifically, we aim to attract submissions that delve into the many misplaced assumptions and potential dangers that AI might introduce in the organizational setting. Such negative implications tend to be often overlooked by empirical research studies and their consequence seldom discussed. We invite the submission of original manuscripts that advance empirical, theoretical, and conceptual understanding of the consequences and effects of how big data analytics and AI drive digital business strategy. Manuscripts must have substantial implications for theory and practice, and we welcome both empirical papers and conceptual theory development papers, as well as other genres. We are particularly interested in manuscripts that address the challenges and changes that these technological innovations bring to strategic management theories, and how such technologies may change the way we think about how organisations operate and compete. The special issue is designed to embrace a variety of perspectives on emerging technological innovations, information systems, and digital business strategy research. Purely computer science papers are not within the scope of this special issue.


Topics of interest include:

− Critical perspectives on analytics and AI

− The impact of analytics and AI on stress and loss of autonomy

− Ethical implications of analytics and AI

− Unexpected and un- or under-explored aspects of business analytic and AI

− Implications of analytics and AI on work and workers, e.g. automation, the changing nature of work.

− Organisational learning and innovation from analytics and AI

− Analytics and AI and their impact on business strategy formulation

− Digital business strategy and value destruction using analytics and AI

− The changing and/or automated nature of business decision-making in the age of analytics and AI

− Governance challenges of digital business strategy in analytics and AI projects

− Inertial forces, path-dependencies and hindrances of digital capabilities in analytics and AI projects

− Business value and unanticipated consequences of analytics and AI in the organisational context

− Micro-foundations of digital business strategy in the age of analytics and AI

− Managerial issues concerning the implementation of analytics and AI projects

− Business model reconfiguration in the age of analytics and AI

− Data-driven competitive advantage in changing competitive markets

− Organisational structure, skills, management thinking, algorithmic management, strategic decision-making and leadership in the age of analytics and AI


Important dates


  • Development workshop at ICIS 2019:

    • Extended abstracts submission commences September 30th, 2019 and abstracts accepted until final date of November 10th, 2019

    • Authors will be notified of decision within 2 weeks of their submission.

    • Workshop event: 15th – 18th December 2019


  • EJIS Special Issue

    • Initial paper submission deadline: April 30th, 2020

    • First round authors notification: July 31st, 2020

    • Invited revisions deadline: September 15th, 2020

    • Second round authors notification: October 31st, 2020

    • Final revision deadline: December 15th, 2020

    • Final authors notification: February 15th, 2021

    • Projected publication: Spring 2021


Development Workshop


The objective of the workshop is to (i) provide an opportunity for prospective authors to discuss their work with special issue guest editors, (ii) to receive feedback on specific pieces of work to best position them for submission to EJIS. We invite the submission of extended abstracts. While there is no guarantee that work presented at the workshop will be subsequently published in EJIS, it is expected that some will eventually be published in the journal, once further developed. Submission to and participation in the workshop is encouraged but not required to submit to the special issue.


By this date or earlier, please submit an abstract of no more than 2 single-spaced pages of text and up to 2 figures. We will not count figures and references in the 2-page limit. Extended abstracts should be between 1,000 and 3,000 words and follow the formatting guidelines of ICIS papers (see here).

The content of the abstract can vary but must include the proposed theoretical and practical contributions of the study. To submit a paper to the workshop, email an electronic copy of your paper in word or PDF format to: Include in the body of the email the name, institution, and email address of each author.


Associate Editors

− Paul Pavlou, Bauer College of Business, University of Houston, USA

− Elena Parmiggiani, Norwegian University of Science and Technology, Norway

− Edward Curry, National University of Galway (NUIG) and Lero, Ireland

− Mary Beth Watson-Manheim, UIC Business of University of Illinois, USA

− Daniel Schlagwein, University of Sydney Business School, Australia

− Emma Forsgren, Leeds University, UK

− Giovanni Sartor, European University Institute, Italy

− Daniel Schlagwein, The University of Sydney Business School, Australia

− Samuel Fosso Wamba, Toulouse Business School, France

− Uday Kulkarni, Arizona State University, USA

− Peter Wahlgren, The Swedish Law and Informatics Research Institute, Sweden

− Adegboyega Ojo, National University of Galway (NUIG), Ireland

− Stella Pachidi, Cambridge Judge Business School, UK

− Dubravka Cecez-Kecmanovic, UNSW Business School, Australia

− Terence Saldanha, Terry College of Business, University of Georgia, USA



Kersting, K., & Meyer, U. (2018). From Big Data to Big Artificial Intelligence? Algorithmic Challenges and Opportunities of Big Data, 32(1), 3-8.

Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639.

Zuboff, S. (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75-89.