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Data science and automation in the process of theorizing: Machine learning’s power of induction in the co-duction cycle

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  • Daan Kolkman
  • Gwendolyn K Lee
  • Arjen van Witteloostuijn

Abstract

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Suggested Citation

  • Daan Kolkman & Gwendolyn K Lee & Arjen van Witteloostuijn, 2024. "Data science and automation in the process of theorizing: Machine learning’s power of induction in the co-duction cycle," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-30, November.
    • Handle: RePEc:plo:pone00:0309318
    DOI: 10.1371/journal.pone.0309318
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