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Show Results For
- All HBS Web
(12,592)
- People (75)
- News (2,944)
- Research (3,713)
- Events (33)
- Multimedia (340)
- Faculty Publications (2,400)
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- 2017
- Working Paper
Machine Learning Methods for Strategy Research
By: Mike Horia Teodorescu
Numerous applications of machine learning have gained acceptance in the field of strategy and management research only during the last few years. Established uses span such diverse problems as strategic foreign investments, strategic resource allocation, systemic risk... View Details
Keywords: Machine Learning; Natural Language Processing; Classification; Decision Trees; Strategic Decisions; Strategy; Research; Information Technology
Teodorescu, Mike Horia. "Machine Learning Methods for Strategy Research." Harvard Business School Working Paper, No. 18-011, August 2017. (Revised October 2017.)
- August 2020
- Article
Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation
By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic... View Details
Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.
- April 2020
- Article
Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning
By: Ariel Dora Stern and W. Nicholson Price, II
In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging... View Details
Keywords: Machine Learning; Causal Inference; Health Care and Treatment; Safety; Governing Rules, Regulations, and Reforms
Stern, Ariel Dora, and W. Nicholson Price, II. "Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning." Biostatistics 21, no. 2 (April 2020): 363–367.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- Research Summary
Learning Motives
In another research stream, Professor Myers probes the underlying reasons that motivate people to learn. He has confirmed a conceptual framework that identifies four distinct learning motives that vary to the extent that they are intrinsic, extrinsic, and self- or... View Details
Keywords: Learning
- January 2015 (Revised April 2015)
- Case
Zeal: Launching Personalized and Social Learning
By: John J-H Kim and Christine S. An
Set in 2014, this case follows John Danner and his team at Zeal as they consider their product development strategy. In February 2013, serial entrepreneurs John Danner and Sanjay Noronha co-found Zeal, an education technology start up providing a web-based, mobile... View Details
Keywords: Entrepreneurship; Education Technology; MVP; Product Development; Product Market Fit; Monetization Strategy; SaaS Business Models; Education; Personalized Learning
Kim, John J-H, and Christine S. An. "Zeal: Launching Personalized and Social Learning." Harvard Business School Case 315-052, January 2015. (Revised April 2015.)
- Research Summary
Learning Agility
This research in both field and experimental settings is targeted at examining whether and how employees in the 'midst of learning' are aware of the optimal learning behavior as well as determining if this awareness does indeed increase learning agility. Further,... View Details
- 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; AI and Machine Learning
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.
- Research Summary
Social Learning
One major area of my research is social learning: the ways and extent to which people discover what they want and need from the behavior and opinions of others. Social learning takes many forms. Probably most obvious is word of mouth—the advice and... View Details
- December 2023
- Background Note
Organizational Learning
By: Willy Shih
This is a background note that surveys part of the extensive literature on organizational learning. The focus is on learning from experiences, how those learnings get translated into organizational routines and processes, and how that can also lead to getting stuck in... View Details
Shih, Willy. "Organizational Learning." Harvard Business School Background Note 624-058, December 2023.
- Research Summary
Learning Organizations
David A. Garvin is studying how companies pursue improvement and change through efforts to stimulate organizational learning. He has found the following activities to be common in learning organizations: intelligence gathering; experimentation; learning from... View Details
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.
- 18 Apr 2000
- Research & Ideas
Learning in Action
"The most effective learning strategy depends on the situation," writes David A. Garvin. "There is no stock answer, nor is there a single best approach." In Learning in Action, he illustrated the diversity... View Details
Keywords: by David A. Garvin
- February 2014
- Article
Learning by Supplying
By: Juan Alcacer and Joanne Oxley
Learning processes lie at the heart of our understanding of how firms build capabilities to generate and sustain competitive advantage: learning by doing, learning by exporting, learning from competitors, users, and alliance partners. In this paper we focus attention... View Details
Alcacer, Juan, and Joanne Oxley. "Learning by Supplying." Strategic Management Journal 35, no. 2 (February 2014): 204–223.
- 2012
- Working Paper
Learning by Supplying
By: Juan Alcacer and Joanne Oxley
Learning processes lie at the heart of our understanding of how firms build capabilities to generate and sustain competitive advantage: learning by doing, learning by exporting, learning from competitors, users, and alliance partners. In this paper we focus attention... View Details
Keywords: Learning; Supply Chain; Competitive Advantage; Mobile and Wireless Technology; Competency and Skills; Relationships; Telecommunications Industry
Alcacer, Juan, and Joanne Oxley. "Learning by Supplying." Harvard Business School Working Paper, No. 12-093, April 2012.
- July–September 2020
- Article
Innovation Contest: Effect of Perceived Support for Learning on Participation
By: Olivia Jung, Andrea Blasco and Karim R. Lakhani
Background: Frontline staff are well positioned to conceive improvement opportunities based on first-hand knowledge of what works and does not work. The innovation contest may be a relevant and useful vehicle to elicit staff ideas. However, the success of the... View Details
Keywords: Contest; Innovation; Employee Engagement; Organizational Learning; Health Care; Health Care Delivery; Innovation and Invention; Organizations; Learning; Employees; Perception; Health Care and Treatment
Jung, Olivia, Andrea Blasco, and Karim R. Lakhani. "Innovation Contest: Effect of Perceived Support for Learning on Participation." Health Care Management Review 45, no. 3 (July–September 2020): 255–266.
- 2020
- Working Paper
Team Learning and Superior Firm Performance: A Meso-Level Perspective on Dynamic Capabilities
By: Jean-François Harvey, Henrik Bresman, Amy C. Edmondson and Gary P. Pisano
This paper proposes a team-based, meso-level perspective on dynamic capabilities. We argue that team-learning routines constitute a critical link between managerial cognition and organization-level processes of sensing, seizing, and reconfiguring. We draw from the... View Details
Keywords: Dynamic Capabilities; Innovation; Strategic Change; Teams; Team Learning; Groups and Teams; Learning; Innovation and Invention; Change; Performance
Harvey, Jean-François, Henrik Bresman, Amy C. Edmondson, and Gary P. Pisano. "Team Learning and Superior Firm Performance: A Meso-Level Perspective on Dynamic Capabilities." Harvard Business School Working Paper, No. 19-059, December 2018. (Revised January 2020.)
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- February 1989
- Background Note
Learning with Cases
Gives some tips to maximize all learning; offers the pros and cons of experiential learning (cases) as a method; and gives some guidelines for effective case preparation, discussion, and learning. View Details
Bonoma, Thomas V. "Learning with Cases." Harvard Business School Background Note 589-080, February 1989.
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)