Do you think AI will have an impact on science? You are wrong. It will not–it already does. The annual International Conference on Information Systems received over 1,000 more paper submissions this year. Our main journals report a 20%, 40%, or even 100% increase in submission numbers. This could be great if these papers were good, if we simply saw more and better research being produced. Problem is: We don't. What we see is an AI slop tsunami of less readable papers, hastily produced, with marginal insights if any. How should we handle this situation? We discuss a few possible levers on the supply and demand side of research that we as a field could implement.

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We devote enormous time and effort to developing a research idea, crafting a research design, executing a study, and then writing and polishing a paper – only to then find out that the reviewers do not find our contribution interesting enough to publish the paper. Years of work—wasted. There must be a better way. Thankfully, Murat Tarakci has an idea - flipping the script: deal with demand uncertainty first and with empirical uncertainty second. But before you do that you still need to ask one key question: Is this project worthy of me? Tune in to find out.

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We have Daniel Schlagwein on the show, who is what Germans call a "Tausendsassa:" He is both a practitioner and researcher of digital nomadism, the editor-in-chief of the Journal of Information Technology, and president of the AIS special interest group on Grounded Theory Methodology. We touch upon all three of these aspects, but at the core we want to know from Daniel whether generative AI tools are automating grounded theory and thereby eliminate what used to be at the heart of a humanistic and constructionist approach to doing research – or are they merely leveling the playing field for qualitative field researchers by giving them computational support matching those tools that quantitative researchers have had for a long time. Daniel argues that it depends on the specific flavor of the grounded theory method you are using to determine whether and how you can leverage generative AI for such research.

References

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Jiwasiddi, A., Schlagwein, D., Cahalane, M. C., Cecez-Kecmanovic, D., Leong, C., & Ractham, P. (2024). Digital Nomadism as a New Part of the Visitor Economy: The Case of the 'Digital Nomad Capital' Chiang Mai, Thailand. Information Systems Journal, 34(5), 1493–1535.

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Gopal, R., Li, J., Riemer, K., Sarker, S., Singh, P. V., Susarla, A., Bichler, M., & Thatcher, J. B. (2025). Inventing with Machines: Generative AI and the Evolving Landscape of IS Research. Information Systems Research, 36(4), 1949–1967.

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Wiesche, M., Jurisch, M., Yetton, P., & Krcmar, H. (2019). Grounded Theory Methodology in Information Systems Research. MIS Quarterly, 41(3), 685–701.

Sarker, S., Xiao, X., Beaulieu, T., & Lee, A. S. (2018). Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2). Journal of the Association for Information Systems, 19(8), 752–774.

AIS Special Interest Group on Grounded Theory Methodology (SIG GTM): https://aisnet.org/members/member_engagement/groups.aspx?code=SIGGTM.

Recker, J., Zeiss, R., & Mueller, M. (2024). iRepair or I Repair? A Dialectical Process Analysis of Control Enactment on the iPhone Repair Aftermarket. MIS Quarterly, 48(1), 321–346.