Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
  • Research
    • Research
    • Publications
    • Global Research Centers
    • Case Development
    • Initiatives & Projects
    • Research Services
    • Seminars & Conferences
    →
  • Publications→

Publications

Publications

Filter Results : (7) Arrow Down
Filter Results : (7) Arrow Down Arrow Up

Show Results For

  • All HBS Web  (270)
    • Faculty Publications  (7)

    Show Results For

    • All HBS Web  (270)
      • Faculty Publications  (7)

      Partial Dependence Plots Remove Partial Dependence Plots →

      Page 1 of 7 Results

      Are you looking for?

      Machine Learning for Pattern Discovery in Management Research
      Ilva <span class="highlight">Steel</span> <span class="highlight">Taranto</span>: <span class="highlight">Providing</span> and <span class="highlight">Polluting</span> (A)
      → Search All HBS Web
      • January 2021
      • Article

      Machine Learning for Pattern Discovery in Management Research

      By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
      Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect...  View Details
      Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; Roc Curve; Confusion Matrix; Partial Dependence Plots
      Citation
      Find at Harvard
      Read Now
      Related
      Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
      • 2020
      • Working Paper

      Business Reopening Decisions and Demand Forecasts During the COVID-19 Pandemic

      By: Dylan Balla-Elliott, Zoë B. Cullen, Edward L. Glaeser, Michael Luca and Christopher Stanton
      How quickly will American businesses reopen after COVID-19 lockdowns end? We use a nationwide survey of small businesses to measure firms’ expectations about their re-opening and future demand. A plurality of firms in our sample expect to reopen within days of the end...  View Details
      Keywords: Covid-19; Demand Forecasting; Reopening; Health Pandemics; Demand and Consumers; Forecasting and Prediction
      Citation
      SSRN
      Read Now
      Related
      Balla-Elliott, Dylan, Zoë B. Cullen, Edward L. Glaeser, Michael Luca, and Christopher Stanton. "Business Reopening Decisions and Demand Forecasts During the COVID-19 Pandemic." NBER Working Paper Series, No. 27362, June 2020. (Harvard Business School Working Paper, No. 20-132, June 2020.)
      • 2016
      • Article

      The Mirroring Hypothesis: Theory, Evidence, and Exceptions

      By: Lyra J. Colfer and Carliss Y. Baldwin
      The mirroring hypothesis predicts that organizational ties within a project, firm, or group of firms (e.g., communication, collocation, employment) will correspond to the technical dependencies in the work being performed. This article presents a unified picture of...  View Details
      Keywords: Modularity; Mirroring Hypothesis; Organization Design; Conway's Law; Knowledge Boundaries; Relational Contracts; Open Source Software; Organizational Design; Organizational Structure; Boundaries; Knowledge Management
      Citation
      Find at Harvard
      Read Now
      Related
      Colfer, Lyra J., and Carliss Y. Baldwin. "The Mirroring Hypothesis: Theory, Evidence, and Exceptions." Industrial and Corporate Change 25, no. 5 (2016): 709–738. (Lead Article.)
      • 2016
      • Working Paper

      The Mirroring Hypothesis: Theory, Evidence and Exceptions

      By: Lyra J. Colfer and Carliss Y. Baldwin
      The mirroring hypothesis predicts that organizational ties within a project, firm, or group of firms (e.g., communication, collocation, employment) will correspond to the technical patterns of dependency in the work being performed. A thorough understanding of the...  View Details
      Keywords: Modularity; Innovation; Product And Process Development; Organization Design; Design Structure; Organizational Ties; Mirroring Hypothesis; Industry Architecture; Product Architecture; Complex Technical Systems; Technology; Organizational Design; Organizational Structure; Relationships; Innovation and Invention; Product Development
      Citation
      SSRN
      Read Now
      Related
      Colfer, Lyra J., and Carliss Y. Baldwin. "The Mirroring Hypothesis: Theory, Evidence and Exceptions." Harvard Business School Working Paper, No. 16-124, April 2016. (Revised May 2016.)
      • March 2016
      • Article

      Dividends as Reference Points: A Behavioral Signaling Approach

      By: Malcolm Baker, Brock Mendel and Jeffrey Wurgler
      We outline a dividend signaling model that features investors who are averse to dividend cuts. Managers with strong unobservable cash earnings separate by paying high dividends but retain enough to be likely not to fall short next period. The model is consistent with a...  View Details
      Keywords: Investment
      Citation
      Find at Harvard
      Read Now
      Related
      Baker, Malcolm, Brock Mendel, and Jeffrey Wurgler. "Dividends as Reference Points: A Behavioral Signaling Approach." Review of Financial Studies 29, no. 3 (March 2016): 697–738.
      • October 2013 (Revised May 2016)
      • Case

      Ilva Steel Taranto: Providing and Polluting (A)

      By: Lena G. Goldberg, Vincent Dessain, Ottavia Pesce and Karol Misztal
      Nearly 27,000 people depended on Ilva Steel Taranto, the largest steel-making plant in Europe, for their livelihoods, but the plant's pollution fouled the environment and increased the incidence of tumors, respiratory illnesses, and deaths. In July 2012, faced with a...  View Details
      Keywords: Safety; Pollutants; Business Exit or Shutdown; Health; Decision Making; Steel Industry; Europe
      Citation
      Educators
      Purchase
      Related
      Goldberg, Lena G., Vincent Dessain, Ottavia Pesce, and Karol Misztal. "Ilva Steel Taranto: Providing and Polluting (A)." Harvard Business School Case 314-045, October 2013. (Revised May 2016.)
      • February 2010 (Revised June 2012)
      • Case

      "Plugging In" the Consumer: The Adoption of Electrically Powered Vehicles in the U.S.

      By: Elie Ofek and Polly Ribatt
      How will U.S. consumers respond to the proliferation of alternative-fuel vehicles, such as cars powered partially or completely by electricity, in the coming decade? After a century in which fossil fuel-powered vehicles dominated the market, it appeared consumers would...  View Details
      Keywords: Energy Sources; Policy; Marketing; Demand and Consumers; Business and Government Relations; Natural Environment; Pollutants; Adoption; Auto Industry; United States
      Citation
      Educators
      Purchase
      Related
      Ofek, Elie, and Polly Ribatt. "Plugging In" the Consumer: The Adoption of Electrically Powered Vehicles in the U.S. Harvard Business School Case 510-076, February 2010. (Revised June 2012.)
      • 1

      Are you looking for?

      Machine Learning for Pattern Discovery in Management Research
      Ilva <span class="highlight">Steel</span> <span class="highlight">Taranto</span>: <span class="highlight">Providing</span> and <span class="highlight">Polluting</span> (A)
      → Search All HBS Web
      ǁ
      Campus Map
      Harvard Business School
      Soldiers Field
      Boston, MA 02163
      →Map & Directions
      →More Contact Information
      • Make a Gift
      • Site Map
      • Jobs
      • Harvard University
      • Trademarks
      • Policies
      • Digital Accessibility
      Copyright © President & Fellows of Harvard College