Some academics go into the office every day; some are rarely ever seen on campus. Is one way better than the other? Who better to ask than the brilliant Ella Hafermalz who spent her career on the topic of remote work and its implications for belonging, community, collaboration, and performance. She points out that academia has always been a distributed and flexible profession. Researchers need flexibility and freedom to figure out their own best way of solving problems and doing their work, some of which may mean sitting at a desk, but maybe also involve lab or field work. On the other hand, pure freedom for individual academics makes a university nothing more than a collection of hired guns without a true community. How do we find the best balance and what is a good balance to begin with?

Episode reading list

Chang, S. (2025): China's unemployed young adults who are pretending to have jobs. BBC News, 11 August 2025, https://www.bbc.com/news/articles/cdd3ep76g3go.

Hafermalz, E., & Riemer, K. (2021). Productive and Connected While Working from Home: What Client-facing Remote Workers can Learn from Telenurses about 'Belonging Through Technology'. European Journal of Information Systems, 30(1), 89-99.

Huysman, M. (2025). Studying AI in the Wild: Reflections from the AI@Work Research Group. Journal of Management Studies, https://doi.org/10.1111/joms.70021.

The Professor and the Madman. https://www.imdb.com/title/tt5932728/.

Hafermalz, E. (2021). Out of the Panopticon and into Exile: Visibility and Control in Distributed New Culture Organizations. Organization Studies, 42(5), 697–717.

Rovelli, C. (2022). Helgoland: The Strange and Beautiful Story of Quantum Physics. Penguin Books.

Carroll, S. (2019). Something Deeply Hidden: Quantum Worlds and the Emergence of Spacetime. Dutton.

Sting, F. J., Tarakci, M., & Recker, J. (2024). Performance Implications of Digital Disruption in Strategic Competition. MIS Quarterly, 48(3), 1263-1278.

Archive.org: Philosophy 185 Heidegger: Lectures from the course Philosophy 185 Heidegger by Hubert Dreyfus. https://fourble.co.uk/podcast/philosophy185heidegger.

Baudrillard, J. (1981). Simulacra and Simulation. University of Michigan Press.

Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered Workforce: Understanding its Affordances and Side Effects. Business Horizons, 67(5), 511-523.

Haubrich, G. F., Soekijad, M., & Hafermalz, E. (2025). 'What's Up with Work?'Bringing Screens into a Theory of Hybrid Working Situations. Academy of Management Annual Meeting Proceedings, https://doi.org/10.5465/AMPROC.2025.10670abstract.

Tekeste, M. (2025). Under Pressure: Becoming the Good Enough Academic. Organization, https://doi.org/10.1177/13505084251383285.

LinkedIn Community: The Digital Visibility Group: https://www.linkedin.com/groups/13346086/.

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.