THE ONE WHERE NICK EXPLAINS HOW TO DO COMPUTATIONALLY INTENSIVE THEORY CONSTRUCTION (1 June 2022)

Description

June is here and a new paper is out that argues that empirical patterns are publishable in our top journals. Really? Really. In this episode, Jan plays the interviewer and Nick is the interviewee in what is essentially a Q&A session about computationally intensive theory construction, which Nick argues will be a key part of the future of information systems research.

Episode Reading List

  • Miranda, S. M., Berente, N., Seidel, S., Safadi, H., & Burton-Jones, A. (2022). Computationally Intensive Theory Construction: A Primer for Authors and Reviewers. MIS Quarterly, 46(2), iii-xviii.
  • The AIS SIG DITE (Digital Innovation, Transformation, and Entrepreneurship) Digital Research Seminar Series: https://www.bwl.uni-hamburg.de/en/isdi/forschung/research-seminar.html.
  • Barclay, D. W., Higgins, C. A., & Thompson, R. L. (1995). The Partial Least Squares Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration. Technology Studies: Special Issue on Research Methodology, 2(2), 285-324.
  • Bettis, R. A. (2012). The Search for Asterisks: Compromised Statistical Tests and Flawed Theories. Strategic Management Journal, 33(1), 108-113.
  • Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review, 2(3), 196-217.
  • Berente, N., Seidel, S., & Safadi, H. (2019). Data-Driven Computationally-Intensive Theory Development. Information Systems Research, 30(1), 50-64.
  • Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370.
  • Recker, J. (2010). Explaining Usage of Process Modeling Grammars: Comparing Three Theoretical Models in the Study of Two Grammars. Information & Management, 47(5-6), 316-324.
  • Mertens, W., & Recker, J. (2020). New Guidelines for Null Hypothesis Significance Testing in Hypothetico-Deductive IS Research. Journal of the Association for Information Systems, 21(4), 1072-1102.
  • Figl, K., & Recker, J. (2016). Process Innovation as Creative Problem-Solving: An Experimental Study of Textual Descriptions and Diagrams. Information & Management, 53(6), 767-786.
  • Vaast, E., & Levina, N. (2006). Multiple Faces of Codification: Organizational Redesign in an IT Organization. Organization Science, 17(2), 190-201.
  • Langley, P. (2000). The Computational Support of Scientific Discovery. International Journal of Human-Computer Studies, 53(3), 393-410.
  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996). Advances in Knowledge Discovery and Data Mining. AAAI Press.
  • Glaser, B. G. (2008). Doing Quantitative Grounded Theory. Sociology Press.
  • Adamic, L. A., & Glance, N. (2005). The Political Blogosphere and the 2004 U.S. Election: Divided They Blog. Paper presented at the 3rd International Workshop on Link Discovery, Chicago, Illinois.
  • Lazer, D., Pentland, A. P., Adamic, L. A., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721-723.
  • Kitchens, B., Johnson, S. L., & Gray, P. H. (2020). Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption. MIS Quarterly, 44(4), 1619-1649.
  • Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. (1986). Induction: Processes of Inference, Learning, and Discovery. MIT Press.
  • Greenwood, B. N., & Wattal, S. (2017). Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities. MIS Quarterly, 41(1), 163-187.
  • Tiwana, A., & Kim, S. K. (2019). From Bricks to an Edifice: Cultivating Strong Inference in Information Systems Research. Information Systems Research, 30(3), 1029-1036.
  • Xu, H., Zhang, N., & Zhou, L. (2020). Validity Concerns in Research Using Organic Data. Journal of Management, 46(7), 1257-1274.
  • Sturm, T., Gerlach, J. P., Pumplun, L., Mesbah, N., Peters, F., Tauchert, C., Nan, N., & Buxmann, P. (2021). Coordinating Human and Machine Learning for Effective Organizational Learning. MIS Quarterly, 45(3), 1581-1602.
  • Pentland, B. T., Vaast, E., & Ryan Wolf, J. (2021). Theorizing Process Dynamics with Directed Graphs: A Diachronic Analysis of Digital Trace Data. MIS Quarterly, 45(2), 967-984.
  • van de Ven, A. (2017). AMD – Advancing Discoveries Through Empirical Exploration. Academy of Management Discoveries, 3(4), 329-330.

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