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/.