this IS research

• Data is the fuel that sets innovation on fire

  Most think that algorithms are the modern root cause of innovations. But says not only are organizations today powered by data, they innovate through data. With several other colleagues, Marta is bringing data studies back to the forefront of information systems research. She produces workshops, a forthcoming book, and an online bibliography with seminal readings. We talk to Marta about the relationship between data and meaning, representation versus innovation, and whether we all soon live in a hyperreality created through synthetic data that lost all connection to the real-world. Episode reading list Alaimo, C., & Kallinikos, J. (2022). Organizations Decentered: Data Objects, Technology and Knowledge. Organization Science, 33(1), 19-37. Aaltonen, A., Stelmaszak, M., & Xu, D. The Data Studies Bibliography. . Chen, H., Chiang, R., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impacts. MIS Quarterly, 36(4), 1165-1188. Wand, Y., & Wang, R. Y. (1996). Anchoring Data Quality Dimensions in Ontological Foundations. Communications of the ACM, 39(11), 86-95. Xu, D., Stelmaszak, M., & Aaltonen, A. (2025). What is Changing the Game in Data Research? Insights from the “Innovating in Data-based Reality” Professional Development Workshop. Communications of the Association for Information Systems, 56(8), 194-208. Kent, W. (1978). Data and Reality. North-Holland. Hirschheim, R., Klein, H. K., & Lyytinen, K. (1995). Information Systems Development and Data Modeling: Conceptual and Philosophical Foundations. Cambridge University Press. Goodhue, D. L., Wybo, M. D., & Kirsch, L. J. (1992). The Impact of Data Integration on the Costs and Benefits of Information Systems. MIS Quarterly, 16(3), 239-311. Aaltonen, A., & Stelmaszak, M. (2024). Data Innovation Lens: A New Way to Approach Data Design as Value Creation. SSRN, . Recker, J., Indulska, M., Green, P., Burton-Jones, A., & Weber, R. (2019). Information Systems as Representations: A Review of the Theory and Evidence. Journal of the Association for Information Systems, 20(6), 735-786. Bowker, G. C., & Star, S. L. (1999). Sorting Things Out: Classification and Its Consequences. MIT Press. Baudrillard, J. (1981). Simulacra and Simulation. University of Michigan Press. Harari, Y. N. (2024). Nexus: A Brief History of Information Networks from the Stone Age to AI. Random House. Wittgenstein, L. (1953). Philosophical Investigations. Basil Blackwell. 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. Aaltonen, A., Stelmaszak, M., & Lyytinen, K. (Eds.). (2026). Research Handbook on Digital Data: Interdisciplinary Perspectives. Edward Elgar Publishing. 

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• If you’re writing a paper about AI you are not allowed to talk about AI

  When we discuss artificial intelligence, what metaphors do we use to illustrate what we mean? Is artificial intelligence some sort of robot—like Ultron—or is it an organism—like a beehive? What happens to our expectations, our thinking, and our conclusions when we change these metaphors, say, from an entitative metaphor (say, an agent) to a relational metaphor (say, belonging to our work network)? We discuss these points with and who wrote a very interesting paper on how management scholars think about artificial intelligence.   Episode reading list Ramaul, L., Ritala, P., Kostis, A., & Aaltonen, P. (2025). Rethinking How We Theorize AI in Organization and Management: A Problematizing Review of Rationality and Anthropomorphism. Journal of Management Studies, . Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing Artificial Intelligence. MIS Quarterly, 45(3), 1433-1450. Alvesson, M., & Sandberg, J. (2020). The Problematizing Review: A Counterpoint to Elsbach and Van Knippenberg’s Argument for Integrative Reviews. Journal of Management Studies, 57(6), 1290-1304. Berente, N. (2020). Agile Development as the Root Metaphor for Strategy in Digital Innovation. In S. Nambisan, K. Lyytinen, & Y. Yoo (Eds.), Handbook of Digital Innovation (pp. 83-96). Edward Elgar. Pepper, S. C. (1942). World Hypotheses: A Study in Evidence. University of California Press. Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889-942. Russell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. Jarrahi, M. H., & Ritala, P. (2025). Rethinking AI Agents: A Principal-Agent Perspective. California Management Review Insights, . Boxenbaum, E., & Pedersen, J. S. (2009). Scandinavian Institutionalism – a Case of Institutional Work. In T. B. Lawrence, R. Suddaby, & B. Leca (Eds.), Institutional Work: Actors and Agency in Institutional Studies of Organizations (pp. 178-204). Cambridge University Press. Iivari, J., & Lyytinen, K. (1998). Research on Information Systems Development in Scandinavia-Unity in Plurality. Scandinavian Journal of Information Systems, 10(1), 135-186. Alvesson, M., & Sandberg, J. (2024). The Art of Phenomena Construction: A Framework for Coming Up with Research Phenomena beyond ‘the Usual Suspects'. Journal of Management Studies, 61(5), 1737-1765. Brunsson, N. (2003). The Organization of Hypocrisy: Talk, Decisions, and Actions in Organizations. Copenhagen Business School Press. Floyd, C., Mehl, W.-M., Reisin, F.-M., Schmidt, G., & Wolf, G. (1989). Out of Scandinavia: Alternative Approaches to Software Design and System Development. Human-Computer Interaction, 4(4), 253-350. Grisold, T., Berente, N., & Seidel, S. (2025). Guardrails for Human-AI Ecologies: A Design Theory for Managing Norm-Based Coordination. MIS Quarterly, 49, . Forster, E. M. (1909). The Machine Stops. The Oxford and Cambridge Review, November 1909, .   

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• Nick’s rules for a good PhD education

  We are together in South Bend and teach a class to PhD students in the Mendoza College of Business at the University of Notre Dame. Our joint teaching experience makes us wonder: What should all doctoral students learn or what should we all teach the next generation of IS students? We come up with Nick’s rules for a good PhD education: First, understand what knowledge and inferences are. Second, learn different methods and then deep dive into a primary method. Third, pick a domain and learn its foundations and history. Fourth, develop a mindset of mastery to become the world’s expert on your topic. And finally, develop and hone your writing skills.  Episode reading list Bacon, F. (1620/2019). Novum Organum. Anodos. Hume, D. (1748/1998). An Enquiry Concerning Human Understanding. In J. Perry & M. E. Bratman (Eds.), Introduction to Philosophy: Classical and Contemporary Readings (3rd ed., pp. 190-220). Oxford University Press. Popper, K. R. (1959). The Logic of Scientific Discovery. Basic Books. Yin, R. K. (2009). Case Study Research: Design and Methods (4th ed.). Sage Publications. Berente, N., Ivanov, D., & Vandenbosch, B. (2007). Process Compliance and Enterprise Systems Implementation. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences. Waikoloa, Hawaii, pp. 222-231. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-Dependent Algorithmic Aversion. Journal of Marketing Research, 56(5), 809-825. Recker, J. (2021). Scientific Research in Information Systems: A Beginner's Guide (2nd ed.). Springer. Mackie, J. L. (1965). Causes and Conditions. American Philosophical Quarterly, 2(4), 245-264. Gable, G. G. (1994). Integrating Case Study and Survey Research Methods: An Example in Information Systems. European Journal of Information Systems, 3(2), 112-126. Chalmers, A. F. (2013). What Is This Thing Called Science? (4th ed.). Hackett. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and Quasi-Experimental Designs for Generalized Causal Inference (2nd ed.). Houghton Mifflin. Taylor, F. W. (1911). The Principles of Scientific Management. Harper and Bros. March, J. G., & Simon, H. A. (1958). Organizations. John Wiley & Sons. Nelson, R. R., & Winter, S. G. (1982). An Evolutionary Theory of Economic Change. Harvard University Press. 

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• Should all qualitative researchers use LLMs?

  One of the big topics at the 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, . 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.  

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• Cognitive conflict, courage, humility, and respect: Ingredients for a productive academic discourse

  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 in beautiful Copenhagen, made possible through a generous invite from 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 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, . 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, .  

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