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Show Results For
- All HBS Web
(11,305)
- People (74)
- News (2,859)
- Research (3,809)
- Events (45)
- Multimedia (240)
- Faculty Publications (2,372)
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- April 2011
- Article
What Can We Learn from 'Great Negotiations'?
What can one legitimately learn-analytically and/or prescriptively-from detailed historical case studies of "great negotiations," chosen more for their salience than their analytic characteristics or comparability? Taking a number of such cases compiled by Stanton... View Details
Keywords: Learning; International Relations; History; Agreements and Arrangements; Negotiation Process; Conflict and Resolution
Sebenius, James K. "What Can We Learn from 'Great Negotiations'?" Negotiation Journal 27, no. 2 (April 2011).
- September 1992
- Case
Star Cablevision Group (F): Lessons Learned
Last case in a series of six cases. This case describes the company as it reflects back to lessons learned. View Details
Keywords: Learning
Sahlman, William A. "Star Cablevision Group (F): Lessons Learned." Harvard Business School Case 293-041, September 1992.
- 25 Apr 2005
- Research & Ideas
New Learning at American Home Products
1931 of John Wyeth & Brothers. In prescription drugs, the company's initial learning base emerged with the purchase in 1931 of John Wyeth & Brothers, a respected... View Details
- 21 Jan 2011
- Working Paper Summaries
Learning from Customers in Outsourcing: Individual and Organizational Effects
- 2008
- Chapter
When Learning and Performance Are at Odds: Confronting the Tension
By: Sara Jean Singer and A. C. Edmondson
This chapter explores complexities of the relationship between learning and performance. We start with the general proposition that learning promotes performance and then describe several challenges for researchers and managers who wish to study or promote learning in... View Details
- June 1996 (Revised January 2000)
- Case
McKinsey & Co.: Managing Knowledge and Learning
Describes the development of McKinsey & Co. as a worldwide management consulting firm from 1926 to 1996. In particular, it focuses on the way in which McKinsey has developed structures, systems, processes, and practices to help it develop, transfer, and disseminate... View Details
Keywords: Management; Managerial Roles; Management Practices and Processes; Competitive Advantage; Global Range; Knowledge Dissemination; Business Processes; Consulting Industry
Bartlett, Christopher A. "McKinsey & Co.: Managing Knowledge and Learning." Harvard Business School Case 396-357, June 1996. (Revised January 2000.)
- January–February 2022
- Article
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
How does a knowledge worker’s level of domain experience affect their algorithm-augmented work performance? We propose and test theoretical predictions that domain experience has countervailing effects on algorithm-augmented performance: on one hand, domain experience... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Organization Science 33, no. 1 (January–February 2022): 149–169. ("Best PhD Student Paper" at SMS conference 2020.)
- 19 Oct 2010
- Working Paper Summaries
The Impact of Supply Learning on Customer Demand: Model and Estimation Methodology
- March 2008
- Article
What Have We Learned from Market Design?
By: Alvin E. Roth
This essay discusses some things we have learned about markets, in the process of designing marketplaces to fix market failures. To work well, marketplaces have to provide thickness, i.e. they need to attract a large enough proportion of the potential participants in... View Details
Keywords: Risk Management; Market Design; Market Participation; Market Transactions; Failure; Safety
Roth, Alvin E. "What Have We Learned from Market Design?" Economic Journal 118, no. 527 (March 2008): 285–310. (Hahn Lecture.)
- March – April 2002
- Article
The Local and Variegated Nature of Learning in Organizations: A Group-Level Perspective
By: Amy C. Edmondson
Edmondson, Amy C. "The Local and Variegated Nature of Learning in Organizations: A Group-Level Perspective." Organization Science 13, no. 2 (March–April 2002): 128–146.
- 14 Mar 2023
- Cold Call Podcast
Can AI and Machine Learning Help Park Rangers Prevent Poaching?
- Article
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
- June, 2021
- Article
Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19
By: Edward L. Glaeser, Ginger Zhe Jin, Benjamin T. Leyden and Michael Luca
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant... View Details
Keywords: COVID-19; Lockdown; Reopening; Impact; Coronavirus; Public Health Measures; Mobility; Health Pandemics; Governing Rules, Regulations, and Reforms; Consumer Behavior
Glaeser, Edward L., Ginger Zhe Jin, Benjamin T. Leyden, and Michael Luca. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Journal of Regional Science 61, no. 4 (June, 2021): 696–709.
- March 2023
- Article
Learning to Successfully Hire in Online Labor Markets
By: Marios Kokkodis and Sam Ransbotham
Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of... View Details
Kokkodis, Marios, and Sam Ransbotham. "Learning to Successfully Hire in Online Labor Markets." Management Science 69, no. 3 (March 2023): 1597–1614.
- September 1999
- Background Note
Learning from Projects: Note on Conducting a Postmortem Analysis
By: Stefan H. Thomke and Steven Sinofsky
Describes how firms can learn from projects through postmortem analysis. Focuses on the step-by-step process of preparing and running a postmortem meeting as it is done at Microsoft and other software developers. View Details
Keywords: Conferences; Management Analysis, Tools, and Techniques; Projects; Software; Information Technology Industry
Thomke, Stefan H., and Steven Sinofsky. "Learning from Projects: Note on Conducting a Postmortem Analysis." Harvard Business School Background Note 600-021, September 1999.
- Article
Overcoming the Winner's Curse: An Adaptive Learning Perspective
By: Yoella Bereby-Meyer and Brit Grosskopf
The winner's curse phenomenon refers to the fact that the winner in a common value auction, in order to actually win the auction, is likely to have overestimated the item's value and consequently is likely to gain less than expected and may even lose (i.e., it is said... View Details
Bereby-Meyer, Yoella, and Brit Grosskopf. "Overcoming the Winner's Curse: An Adaptive Learning Perspective." Journal of Behavioral Decision Making 21, no. 1 (January 2008): 15–27.
- 2020
- Working Paper
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented work performance. To reconcile these perspectives, we theorize that domain experience affects algorithm-augmented performance via two distinct countervailing... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Decision-making; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Harvard Business School Working Paper, No. 21-073, October 2020. (Revised September 2021.)
- Research Summary
Organisational Learning in Software Requirements Engineering and Management
The current research project addresses the continuing low success rate of software development projects, which has been frequently reported in empirical studies. For example, the 2004 Chaos Report by the Standish Group found that only 29% of 9,236 application... View Details
- 17 Jun 2019
- Research & Ideas
What Hospitals Must Learn to Compete
Harvard Business School professors Raffaella Sadun and Leemore Dafny are both economists who have studied hospitals extensively—Sadun’s research has looked at the economics of management, while Dafny’s examines interactions between health... View Details
- 2005
- Working Paper
Team Learning Trade-Offs: When Improving One Critical Dimension of Performance Inhibits Another
By: Richard M.J. Bohmer, Ann B. Winslow, Amy C. Edmondson and Gary P. Pisano
Bohmer, Richard M.J., Ann B. Winslow, Amy C. Edmondson, and Gary P. Pisano. "Team Learning Trade-Offs: When Improving One Critical Dimension of Performance Inhibits Another." Harvard Business School Working Paper, No. 05-047, January 2005.