Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?

Yash Raj Shrestha (), Vivianna Fang He (), Phanish Puranam () and Georg von Krogh ()
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Yash Raj Shrestha: Department of Management, Technology, and Economics, ETH Zürich, Zurich CH 8092, Switzerland
Vivianna Fang He: Management Department, École Supérieure des Sciences Economiques et Commerciales (ESSEC) Business School, 95021 Cergy-Pontoise Cedex, France
Phanish Puranam: Strategy Department, INSEAD, Singapore, Singapore 138676
Georg von Krogh: Department of Management, Technology, and Economics, ETH Zürich, Zurich CH 8092, Switzerland

Organization Science, 2021, vol. 32, issue 3, 856-880

Abstract: Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction , yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.

Keywords: machine learning; algorithmic induction; theory building (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ororsc:v:32:y:2021:i:3:p:856-880

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