Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize? (Forthcoming in Organization Science)

64 Pages Posted: 19 Mar 2018 Last revised: 11 Sep 2020

Date Written: September 11, 2020

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 Organization Science Perspective-paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part due to 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

Suggested Citation

Shrestha, Yash Raj and He, Vivianna Fang and Puranam, Phanish and von Krogh, Georg, Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize? (Forthcoming in Organization Science) (September 11, 2020). INSEAD Working Paper No. 2018/11/STR, Available at SSRN: https://ssrn.com/abstract=3140617 or http://dx.doi.org/10.2139/ssrn.3140617

Yash Raj Shrestha (Contact Author)

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Vivianna Fang He

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Phanish Puranam

INSEAD ( email )

1 Ayer Rajah Avenue
Singapore, 138676
Singapore

HOME PAGE: http://www.insead.edu/facultyresearch/faculty/profiles/ppuranam/

Georg Von Krogh

ETH Zurich ( email )

D-MTEC, SMI, WEV J 411
Weinbergstrasse 56/58
Zurich, 8092
Switzerland
+41 44 632 88 50 (Phone)

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