"Using AI to Build Theory"
Thursday, December 17th, 2020 at 9 am Eastern Time
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). Researchers in our fields are yet to fully 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 how.
We will draw on two papers forthcoming in Organization Science and Strategic Management Journal in which we describe the approach and its application:
1. Algorithm supported induction for building theory: How can be use prediction models to theories?
2. Resolving governance dispute in communities: A study on software license decisions.
Introduce our approach using machine learning algorithms to build theory; provide a step-by- step tutorial of how to do it; and share our codes with the ODC community.
Vivianna Fang He is an associate professor of management at ESSEC Business School. She received her PhD in business administration from The George Washington University. Her research focuses on collaborative organizing in the context of new venture teams, research and development projects, and open-source software communities.
Yash Raj Shrestha is a senior researcher and lecturer at the Chair of Strategic Management and Innovation at ETH Zurich. He received a Ph.D. in Strategy from ETH Zurich. His research interests include new forms of organizing, organization design and algorithms.