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Show Results For
- All HBS Web
(12,648)
- People (75)
- News (2,932)
- Research (3,671)
- Events (32)
- Multimedia (334)
- Faculty Publications (2,358)
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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.
- October 20, 2020
- Article
Expanding AI's Impact with Organizational Learning
By: Sam Ransbotham, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu and Burt LaFountain
Most companies developing AI capabilities have yet to gain significant financial benefits from their efforts. Only when organizations add the ability to learn with AI do significant benefits become likely. View Details
Ransbotham, Sam, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu, and Burt LaFountain. "Expanding AI's Impact with Organizational Learning." MIT Sloan Management Review, Big Ideas Artificial Intelligence and Business Strategy Initiative (website) (October 20, 2020). (Findings from the 2020 Artificial Intelligence
Global Executive Study and Research Project.)
- 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.
- 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.
- 02 Aug 2017
- Working Paper Summaries
Machine Learning Methods for Strategy Research
Keywords: by Mike Horia Teodorescu
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.)
- 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).
- 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
- 1 Apr 1992
- Conference Presentation
Motivation, Creativity, and Learning
By: R. Conti, Teresa M. Amabile and S. Pollack
- 08 Oct 2018
- Working Paper Summaries
Developing Theory Using Machine Learning Methods
- 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.
- 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).
- July–August 2023
- Article
Demand Learning and Pricing for Varying Assortments
By: Kris Ferreira and Emily Mower
Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to... View Details
Keywords: Experiments; Pricing And Revenue Management; Retailing; Demand Estimation; Pricing Algorithm; Marketing; Price; Demand and Consumers; Mathematical Methods
Ferreira, Kris, and Emily Mower. "Demand Learning and Pricing for Varying Assortments." Manufacturing & Service Operations Management 25, no. 4 (July–August 2023): 1227–1244. (Finalist, Practice-Based Research Competition, MSOM (2021) and Finalist, Revenue Management & Pricing Section Practice Award, INFORMS (2019).)