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Show Results For
- All HBS Web
(11,186)
- People (74)
- News (2,817)
- Research (3,693)
- Events (36)
- Multimedia (229)
- Faculty Publications (2,254)
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- 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
- 2019
- Working Paper
Status Pivoting: Coping with Status Threats through Motivated Trade-off Beliefs and Consumption across Domains
By: Dafna Goor, Anat Keinan and Nailya Ordabayeva
Prior research established that status threat leads consumers to display status-related products such as luxury brands. While compensatory consumption in the domain of the status threat (e.g., products associated with financial and professional success) is the most... View Details
- March 2015
- Case
Pearson Affordable Learning Fund
By: Michael Chu, Vincent Dessain and Kristina Maslauskaite
An in-house venture capital fund for affordable private schools at the base of the pyramid established by Pearson, the world's largest education company, PALF sought to invest in business models providing superior educational outcomes in emerging markets on a... View Details
Keywords: Impact Investment; Low Cost Private Schools; Investment Fund; Business At The Base Of The Pyramid; Transition; Investment; Development Economics; Business Growth and Maturation; Social Entrepreneurship; Emerging Markets; Private Sector; Education; Education Industry; Asia; Africa
Chu, Michael, Vincent Dessain, and Kristina Maslauskaite. "Pearson Affordable Learning Fund." Harvard Business School Case 315-109, March 2015.
- 01 Jan 1977
- Conference Presentation
Short Term Natural Gas Consumption Forecasts: Optimal Use of National Weather Service Data
By: James K. Sebenius and Richard Lehman
- 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.
- 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
- April 2000
- Teaching Note
Types of Learning Processes TN
By: David A. Garvin and Jeffrey Berger
Teaching Note for (9-300-124). Not listed on product. View Details
Keywords: Learning
- 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.
- 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 2022
- Article
A Strategic View of Team Learning in Organizations
By: Jean-François Harvey, Henrik Bresman, Amy C. Edmondson and Gary P. Pisano
Research in strategic management and organizational behavior has increasingly focused on understanding how organizations achieve and sustain performance in fast-changing environments. Strategy research suggests that senior managers, through their decisions, influence... View Details
Keywords: Strategic Management; Organization Behavior; Teams; Organizational Capabilities; Groups and Teams; Learning; Management; Decision Making; Performance; Organizational Design
Harvey, Jean-François, Henrik Bresman, Amy C. Edmondson, and Gary P. Pisano. "A Strategic View of Team Learning in Organizations." Academy of Management Annals 16, no. 2 (July 2022): 476–507.
- Article
Inviting Consumers to Downsize Fast-Food Portions Significantly Reduces Calorie Consumption
By: Janet Schwartz, Jason Riis, Brian Elbel and Dan Ariely
Policies that mandate calorie labeling in fast-food and chain restaurants have had little or no observable impact on calorie consumption to date. In three field experiments, we tested an alternative approach: activating consumers' self-control by having servers ask... View Details
Keywords: Food; Labels; Consumer Behavior; Interpersonal Communication; Motivation and Incentives; Health Industry; Food and Beverage Industry
Schwartz, Janet, Jason Riis, Brian Elbel, and Dan Ariely. "Inviting Consumers to Downsize Fast-Food Portions Significantly Reduces Calorie Consumption." Health Affairs 31, no. 2 (February 2012): 2399–2407.
- 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.)
- 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.)
- October 2004 (Revised March 2006)
- Background Note
Learning from Scandals: Responsibility of Professional Organizations
By: Ashish Nanda
This case comments on the responsibility of professional organizations to respond openly to public accusations of wrongdoing by its members. It briefly relates the circumstances of the sexual abuse scandal in the Boston archdiocese of the Roman Catholic Church and the... View Details
Nanda, Ashish. "Learning from Scandals: Responsibility of Professional Organizations." Harvard Business School Background Note 905-037, October 2004. (Revised March 2006.)
- 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.
- 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.
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords: Economics Of AI; Machine Learning; Non-stationarity; Perishability; Value Depreciation; Analytics and Data Science; Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)