One of the big topics at the AOM 2025 conference this summer was the use of large language models in the research process, especially in qualitative studies. We expand this discussion by asking: can qualitative research be automated—or augmented? Yes and no. Some of the advantages LLMs bring to the table are hard to ignore. LLMs can act as critical reviewers, as a consistency checker, as a provider of alternative perspectives on unstructured data, or to break path dependencies in the process of data analysis. They can also help find interesting outcomes that qualitative insights could explain. At the same time, the use of LLMs comes with thorny pitfalls. We know they are unreliable and hallucinate. And the output they create is… average at best. So if you use LLMs, make sure you are not using it for automation—do not lose touch with your craft or your data. Whatever tool you use, make sure you remain a virtuous scholar.

Episode reading list

Noblit, G. W., & Hare, R. D. (1988). Meta-Ethnography: Synthesising Qualitative Studies. Sage.

Recker, J. (2021). Improving the State-Tracking Ability of Corona Dashboards. European Journal of Information Systems, 30(5), 476-495.

Rynes, S., & Gephart Jr., R. P. (2004). Qualitative Research and the "Academy of Management Journal". Academy of Management Journal, 47(4), 454-462.

Geertz, C. (1973). The Interpretation Of Cultures. Basic Books.

Boland, R. J. (2001). The Tyranny of Space in Organizational Analysis. Information and Organization, 11(1), 3-23.

Weber, R. (2004). Editor's Comments: The Rhetoric of Positivism Versus Interpretivism: A Personal View. MIS Quarterly, 28(1), iii-xii.

Lehmann, J., Hukal, P., Recker, J., & Tumbas, S. (2025). Layering the Architecture of Digital Product Innovations: Firmware and Adapter Layers. Journal of the Association for Information Systems, 26, https://doi.org/10.17705/1jais.00956.

Lindberg, A., Berente, N., Howison, J., & Lyytinen, K. (2024). Discursive Modulation in Open Source Software: How Communities Shape Novelty and Complexity. MIS Quarterly, 48(4), 1395-1422.

Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press.

Goodhue, D. L., Lewis, W., & Thompson, R. L. (2012). Comparing PLS to Regression and LISREL: A Response to Marcoulides, Chin, and Saunders. MIS Quarterly, 36(3), 703-716.

Goodhue, D. L., Lewis, W., & Thompson, R. L. (2007). Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS With Product Indicators. Information Systems Research, 18(2), 211-227.

A new season of podcast episodes is starting and what better place to kick it off as the world’s largest business and management conference. We are recording this episode at AOM 2025 in beautiful Copenhagen, made possible through a generous invite from Attila Marton from CBS who organized a recording studio for us. Being here amid symposia, professional development workshops, panels, and paper presentations makes us wonder: what does it take to produce great, stimulating, and productive academic discourse? Does it depend on the people that get invited to speak, is it about their ideas, or what else? We sit down with our friend Philip Hukal with whom we share some stories from the events we’ve attended at AOM and we distil a few rules that characterize good intellectual debate: let there be cognitive conflict about the merit of ideas, be bold enough to propose new ideas, show humility for the craft and work of others, and be respectful to your colleagues.

Episode reading list

Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., Rahman, H. A., Islam, G., & Greenwood, M. (2024). The Future of Research in an Artificial Intelligence-Driven World. Journal of Management Inquiry, 33(3), 207-229.

Brynjolfsson, E., Collis, A., Diewert, W. E., Eggers, F., & Fox, K. J. (2025). GDP-B: Accounting for the Value of New and Free Goods. American Economic Journal: Macroeconomics, https://doi.org/10.1257/mac.20210319.

Stelmaszak, M., Wagner, E., & DuPont, N. N. (2024). Recognition in Personal Data: Data Warping, Recognition Concessions, and Social Justice. MIS Quarterly, 48(4), 1611-1636.

Habermas, J. (1984). Theory of Communicative Action, Volume 1: Reason and the Rationalization of Society. Heinemann.

Lehmann, J., Hukal, P., Recker, J., & Tumbas, S. (2025). Layering the Architecture of Digital Product Innovations: Firmware and Adapter Layers. Journal of the Association for Information Systems, 26, https://doi.org/10.17705/1jais.00956.

Is there collusion in our field? Do we have elites running wild, making sure that their work gets published whilst the rest of us struggles to find room to publish our own work? And are we handling conflicts of interest that may exist between authors and the editors who are charged with making decisions about their work? These are serious questions. They target the core of our field, they have the potential to undermine – or bolster – the legitimacy of all our scholarship, and they pose serious material consequences for all scholars, their careers and ultimately their lives. We came across a new paper that reports an analysis of the potential conflict of interest issues in academic publishing, and we use this paper to reflect on our experiences as both authors and editors. We try to draw a few conclusions and recommendations about how we can raise awareness and build institutional trust to minimize if not avoid any questionable or outright unethical practices in publishing.

Episode reading list

Association for Information Systems. AIS Podcast Library, https://aisnet.org/page/aispodcast.

Mindel, V., & Ciriello, R. (2025). Safeguarding Academic Legitimacy: Editorial Conflicts of Interest as a Principal-Agent Problem in Elite Business Journals. SSRN, https://ssrn.com/abstract=5315585.

Recker, J., Rosemann, M., Green, P., & Indulska, M. (2011). Do Ontological Deficiencies in Modeling Grammars Matter? MIS Quarterly, 35(1), 57-79.

Lee, J., & Berente, N. (2012). Digital Innovation and the Division of Innovative Labor: Digital Controls in the Automotive Industry. Organization Science, 23(5), 1428-1447.

Kane, G. C., Young, A., Majchrzak, A., & Ransbotham, S. (2021). Avoiding an Oppressive Future of Machine Learning: A Design Theory for Emancipatory Assistants. MIS Quarterly, 45(1), 371-396.

Grisold, T., Berente, N., & Seidel, S. (2025). Guardrails for Human-AI Ecologies: A Design Theory for Managing Norm-Based Coordination. MIS Quarterly, https://doi.org/10.25300/MISQ/2025/18058.

Boh, W., Melville, N. P., Baptista, J., Chasin, F., Horita, F., Ixmeier, A., Johnson, S. L., Ketter, W., Kranz, J., Miranda, S. M., Nan, N., Pentland, B. T., Recker, J., Sadeghi, S., Sarker, S., Sarker, S., Sutanto, J., Wang, P., & Wilopo, W. (2025). Digital Resilience for the Climate Crisis: Theoretical Perspectives and Ideas for Future Information Systems Research. MIS Quarterly, forthcoming.

Merton, R. K. (1968). The Matthew Effect in Science. Science, 159(3810), 56-63.

Tiwana, A., & Safadi, H. (2025). Silence Inside Systems: Roots and Generativity Consequences. Information Systems Research, https://doi.org/10.1287/isre.2022.0586.

Li, J., Li, M., Wang, X., & Thatcher, J. B. (2021). Strategic Directions for AI: The Role of CIOs and Boards of Directors. MIS Quarterly, 45(3), 1603-1643.

Pienta, D., Vishwamitra, N., Somanchi, S., Berente, N., & Thatcher, J. B. (2025). Do Crowds Validate False Data? Systematic Distortion and Affective Polarization. MIS Quarterly, 49(1), 347-366.