Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Working Knowledge
Business Research for Business Leaders
  • Browse All Articles
  • Popular Articles
  • Cold Call Podcast
  • Managing the Future of Work Podcast
  • About Us
  • Book
  • Leadership
  • Marketing
  • Finance
  • Management
  • Entrepreneurship
  • All Topics...
  • Topics
    • COVID-19
    • Entrepreneurship
    • Finance
    • Gender
    • Globalization
    • Leadership
    • Management
    • Negotiation
    • Social Enterprise
    • Strategy
  • Sections
    • Book
    • Podcasts
    • HBS Case
    • In Practice
    • Lessons from the Classroom
    • Op-Ed
    • Research & Ideas
    • Research Event
    • Sharpening Your Skills
    • What Do You Think?
    • Working Paper Summaries
  • Browse All
    Developing Theory Using Machine Learning Methods
    08 Oct 2018Working Paper Summaries

    Developing Theory Using Machine Learning Methods

    by Prithwiraj Choudhury, Ryan Allen, and Michael G. Endres
    This paper provides a step-by-step roadmap for using machine learning (ML) techniques to explore novel and robust patterns in data. It introduces management researchers to a new use case for ML tools: building new theory from quantitative observational data.
    LinkedIn
    Email

    Author Abstract

    We describe how to employ machine learning methods in theory development. Compared to traditional causal inference methods, ML methods make far fewer a priori assumptions about the functional form of the underlying model that best represents the data. Given this, researchers could use such methods to explore novel and robust patterns in the data that could lead to inductive theory building. ML strengths include replicable identification of novel patterns in the data. Additionally, ML methods address several concerns (such as “p-hacking” and confounding local effects for global effects) raised by scholars relative to the norms of empirical research in the fields of strategy and management. We develop a step-by-step roadmap that illustrates how to use four ML methods (decision trees, random forests, K-nearest neighbors, and neural networks) to reveal patterns in data that could be used for theory building. We also illustrate how ML methods could better illuminate interactions and non-linear effects, relative to traditional methods. In summary, ML methods could act as a complementary tool to both existing inductive theory-creating methods such as multiple case inductive studies and traditional methods of causal inference.

    Paper Information

    • Full Working Paper Text
    • Working Paper Publication Date: September 2018
    • HBS Working Paper Number: HBS Working Paper #19-032
    • Faculty Unit(s): Technology and Operations Management
    Post A Comment
    In order to be published, comments must be on-topic and civil in tone, with no name calling or personal attacks. Your comment may be edited for clarity and length.
      Trending
        • 27 Jan 2023
        • Op-Ed

        Have We Lost Sight of Integrity?

        • 01 Feb 2023
        • What Do You Think?

        Will Hybrid Work Strategies Pull Down Long-Term Performance?

        • 31 Jan 2023
        • Research & Ideas

        It’s Not All About Pay: College Grads Want Jobs That ‘Change the World’

        • 17 Jan 2023
        • In Practice

        8 Trends to Watch in 2023

        • 28 Feb 2018
        • Sharpening Your Skills

        Master the Team Meeting

    Ryan Allen
    Ryan Allen
    Doctoral Student in Technology and Operations Management
    Contact
    Send an email
    → More Articles
    Prithwiraj Choudhury
    Prithwiraj Choudhury
    Lumry Family Associate Professor of Business Administration
    Contact
    Send an email
    → More Articles
    Find Related Articles
    • Technology Adoption
    • Technological Innovation
    • Technology
    • Management Analysis, Tools, and Techniques
    • Theory

    Sign up for our weekly newsletter

    Interested in improving your business? Learn about fresh research and ideas from Harvard Business School faculty.
    This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
    ǁ
    Campus Map
    Harvard Business School Working Knowledge
    Baker Library | Bloomberg Center
    Soldiers Field
    Boston, MA 02163
    Email: Editor-in-Chief
    →Map & Directions
    →More Contact Information
    • Make a Gift
    • Site Map
    • Jobs
    • Harvard University
    • Trademarks
    • Policies
    • Accessibility
    • Digital Accessibility
    Copyright © President & Fellows of Harvard College