A key problem in empirically oriented research, especially inductive and abductive work, is figuring out which theoretical lens or scaffold to apply to uncover novel insights. In other words, which theory should you use? We discuss a few heuristics scholars can draw on to reach a higher level of scholarly maturity, namely disposition, empirical salience, outcome definition, skepticism, and reflexivity.

Episode reading list

Recker, J. (2021). Scientific Research in Information Systems: A Beginner's Guide (2nd ed.). Springer.

Quine, W. V. O. (1961). Two Dogmas of Empiricism. In W. V. O. Quine (Ed.), From a Logical Point of View (pp. 20-46). Cambridge University Press.

Duhem, P. (1998). Physical Theory and Experiment. In M. Curd & J. A. Cover (Eds.), Philosophy of Science: The Central Issues (pp. 257-279). Norton.

Popper, K. R. (1959). The Logic of Scientific Discovery. Basic Books.

Glikson, E., & Woolley, A. W. (2020). Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals, 14(2), 627-660.

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.

Kotter, J. P. (1996). Leading Change. Harvard Business School Press.

Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review, 2(3), 196-217.

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.

Lindberg, A., Berente, N., Gaskin, J., & Lyytinen, K. (2016). Coordinating Interdependencies in Online Communities: A Study of an Open Source Software Project. Information Systems Research, 27(4), 751-772.

Chandar, B. (2025): AI and Labor Markets: What We Know and Don't Know. https://digitaleconomy.stanford.edu/news/ai-and-labor-markets-what-we-know-and-dont-know/.

Most think that algorithms are the modern root cause of innovations. But Marta Stelmaszak 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. https://www.datastudiesbibliography.org/.

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, https://ssrn.com/abstract=4574855.

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.

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 Angelos Kostis and Paavo Ritala 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, https://doi.org/10.1111/joms.13246.

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, https://cmr.berkeley.edu/2025/07/rethinking-ai-agents-a-principal-agent-perspective/.

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, https://doi.org/10.25300/MISQ/2025/18058.

Forster, E. M. (1909). The Machine Stops. The Oxford and Cambridge Review, November 1909, https://www.cs.ucdavis.edu/~koehl/Teaching/ECS188/PDF_files/Machine_stops.pdf.