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- Multimedia (333)
- Faculty Publications (2,349)
Show Results For
- All HBS Web
(12,546)
- People (75)
- News (2,861)
- Research (3,663)
- Events (32)
- Multimedia (333)
- Faculty Publications (2,349)
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- July–August 1993
- Article
Building a Learning Organization
By: David A. Garvin
Garvin, David A. "Building a Learning Organization." Harvard Business Review 71, no. 4 (July–August 1993): 78–91.
- July 2022
- Supplement
Key Learnings about Turnarounds
By: Ranjay Gulati
Gulati, Ranjay. "Key Learnings about Turnarounds." Harvard Business School Multimedia/Video Supplement 423-703, July 2022.
- 2024
- Working Paper
Personalization and Targeting: How to Experiment, Learn & Optimize
By: Aurelie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela and Oded Netzer
Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic... View Details
Keywords: Personalization; Targeting; Experiments; Observational Studies; Policy Implementation; Policy Evaluation; Customization and Personalization; Marketing Strategy; AI and Machine Learning
Lemmens, Aurelie, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela, and Oded Netzer. "Personalization and Targeting: How to Experiment, Learn & Optimize." Working Paper, June 2024.
- 27 Aug 2014
- Lessons from the Classroom
Learning From Japan’s Remarkable Disaster Recovery
leadership in mobilizing people and resources in highly dynamic situations.” Each winter, 900 HBS students dispatch around the world to see businesses up close, learn what they can about how they are run,... View Details
- 2023
- Working Paper
Evaluation and Learning in R&D Investment
By: Alexander P. Frankel, Joshua L. Krieger, Danielle Li and Dimitris Papanikolaou
We examine the role of spillover learning in shaping the value of exploratory versus incremental
R&D. Using data from drug development, we show that novel drug candidates generate more
knowledge spillovers than incremental ones. Despite being less likely to reach... View Details
Frankel, Alexander P., Joshua L. Krieger, Danielle Li, and Dimitris Papanikolaou. "Evaluation and Learning in R&D Investment." Harvard Business School Working Paper, No. 23-074, May 2023. (NBER Working Paper Series, No. 31290, May 2023.)
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Learning Under Random Distributional Shifts." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 27th (2024).
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- 2019
- Article
Fair Algorithms for Learning in Allocation Problems
By: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth and Zachary Schutzman
Settings such as lending and policing can be modeled by a centralized agent allocating a scarce resource (e.g. loans or police officers) amongst several groups, in order to maximize some objective (e.g. loans given that are repaid, or criminals that are apprehended).... View Details
Elzayn, Hadi, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth, and Zachary Schutzman. "Fair Algorithms for Learning in Allocation Problems." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 170–179.
- 2006
- Article
The Long-Term Value of M&A Activity to Enhance Learning Organizations
Viewing the automobile industry as being made up of independent learning-organisations may reveal some tie-ups that can generate value not easily revealed by traditional financial metrics. The key question to be answered when considering M&A activity between automakers... View Details
Heller, Daniel A., Glenn Mercer, and Takahiro Fujimoto. "The Long-Term Value of M&A Activity to Enhance Learning Organizations." International Journal of Automotive Technology and Management 6, no. 2 (2006): 157 – 176.
- October 2021
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Programs; Consumer Behavior; Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
- 03 Oct 2023
- What Do You Think?
Do Leaders Learn More From Success or Failure?
(Jay Yuno/iStock) Harvard Business School Professor Amy Edmondson’s recent thought-provoking book, Right Kind of Wrong, makes a strong case for the notion that we often learn a lot from failure—and in some cases, perhaps even more than we... View Details
Keywords: by James Heskett
- Article
Active World Model Learning with Progress Curiosity
By: Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber and Daniel Yamins
World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal... View Details
Kim, Kuno, Megumi Sano, Julian De Freitas, Nick Haber, and Daniel Yamins. "Active World Model Learning with Progress Curiosity." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- 08 Oct 2018
- Working Paper Summaries
Developing Theory Using Machine Learning Methods
- October 2001
- Article
Speeding Up Team Learning
By: Amy C. Edmondson, Richard Bohmer and Gary P. Pisano
Keywords: Learning
Edmondson, Amy C., Richard Bohmer, and Gary P. Pisano. "Speeding Up Team Learning." Harvard Business Review 79, no. 9 (October 2001): 125–134.
- 02 Aug 2017
- Working Paper Summaries
Machine Learning Methods for Strategy Research
Keywords: by Mike Horia Teodorescu
- Article
Selective Attention and Learning
Schwartzstein, Joshua. "Selective Attention and Learning." Journal of the European Economic Association 12, no. 6 (December 2014): 1423–1452. (Online Appendix.)
- April 2011
- Article
Why Leaders Don't Learn from Success
By: Francesca Gino and Gary P. Pisano
We argue that for a variety of psychological reasons, it is often much harder for leaders and organizations to learn from success than to learn from failure. Success creates three kinds of traps that often impede deep learning. The first is attribution error or the... View Details
Keywords: Learning; Innovation and Management; Leadership; Failure; Success; Performance Evaluation; Prejudice and Bias
Gino, Francesca, and Gary P. Pisano. "Why Leaders Don't Learn from Success." Harvard Business Review 89, no. 4 (April 2011): 68–74.
- February 2013
- Article
Learning from Roger Fisher
Roger Fisher's career and writings not only offer lessons about negotiation but also about how an academic, especially in a professional school such as law or business, can make an important, positive difference in the world. By his relentless engagement in vexing... View Details
Sebenius, James K. "Learning from Roger Fisher." Harvard Law Review 126, no. 4 (February 2013): 893–898.
- 1 Apr 1992
- Conference Presentation
Motivation, Creativity, and Learning
By: R. Conti, Teresa M. Amabile and S. Pollack
- February 26, 2024
- Article
Making Workplaces Safer Through Machine Learning
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
Machine learning algorithms can dramatically improve regulatory effectiveness. This short article describes the authors' scholarly work that shows how the U.S. Occupational Safety and Health Administration (OSHA) could have reduced nearly twice as many occupational... View Details
Keywords: Government Experimentation; Auditing; Inspection; Evaluation; Process Improvement; Government Administration; AI and Machine Learning; Safety; Governing Rules, Regulations, and Reforms
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Making Workplaces Safer Through Machine Learning." Regulatory Review (February 26, 2024).