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Show Results For
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All HBS Web
(10,596)
- People (64)
- News (3,231)
- Research (3,880)
- Events (24)
- Multimedia (60)
- Faculty Publications (1,353)
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- 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...
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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.)
- 08 Dec 2008
- Research & Ideas
Thinking Twice About Supply-Chain Layoffs
one-standard-deviation increase in store labor brought about a 10 percent increase in profit margin. These findings ran contrary to the thinking of store managers that Ton interviewed. They consistently identified service quality as a top...
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- 01 Oct 2012
- Research & Ideas
Better by the Bundle?
scenario where such a bundle was not offered. Total hardware sales were higher by approximately 100,000 units when bundles were offered. Much more surprising, the sales of software video games jumped by over...
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Keywords:
by Dina Gerdeman
- 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...
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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.)
- 2016
- Article
Learning By Contributing: Gaining Competitive Advantage Through Contributing to Public Goods
By: Frank Nagle
Nagle, Frank. "Learning By Contributing: Gaining Competitive Advantage Through Contributing to Public Goods." Academy of Management Best Paper Proceedings (2016).
- October 8, 2017
- Article
The Watergate Trap: Seeing So Much of the Present through Watergate Makes It Harder to See the Future
By: J. Peter Scoblic
Scoblic, J. Peter. "The Watergate Trap: Seeing So Much of the Present through Watergate Makes It Harder to See the Future." Washington Post (October 8, 2017), B1.
- 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, and share their own knowledge...
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- Forthcoming
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with...
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Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics (forthcoming). (Pre-published online July 8, 2024.)
- Article
From Thinking Too Little to Thinking Too Much: A Continuum of Decision Making.
By: Dan Ariely and Michael I. Norton
Due to the sheer number and variety of decisions that people make in their everyday lives-from choosing yogurts to choosing religions to choosing spouses-research in judgment and decision making has taken many forms. We suggest, however, that much of this research has...
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Ariely, Dan, and Michael I. Norton. "From Thinking Too Little to Thinking Too Much: A Continuum of Decision Making." Wiley Interdisciplinary Reviews: Cognitive Science 2, no. 1 (January–February 2011): 39–46.
- 2022
- Working Paper
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with...
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Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Working Paper, March 2022.
- Research Summary
Relative Thinking and Consumer Choice
Fixed differences appear smaller when compared to large differences. Professor Schwartzstein has proposed a model of relative thinking, in which a person weighs a given change by less when he compares it to a larger range. Relative thinking implies that a person is... 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...
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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.
- September 16, 2022
- Article
Bored at Work? Learn to Manage It by Putting It to Work
By: Katherine Connolly Baden, Boris Groysberg and Heather Poco
Do you often feel bored at work or in life? Do you want to feel less bored? If so, what can you do to make that happen? Boredom has a bad rap, but is it really so bad?
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Baden, Katherine Connolly, Boris Groysberg, and Heather Poco. "Bored at Work? Learn to Manage It by Putting It to Work." Newsweek (September 16, 2022), 18–19.
- 28 Jan 2020
- Book
Advanced Leadership Requires More Than Outside-The-Box Thinking
University, where senior and retired executives learn to develop leadership skills in the service of solving large social problems. Many of the examples in the book come from her experience leading that program. Sean Silverthorne: We’re...
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by Sean Silverthorne
- 20 Apr 2011
- Research & Ideas
Blind Spots: We’re Not as Ethical as We Think
Think back to recent events when people making unethical decisions grabbed the headlines. How did auditors approve the books of Enron and Lehman Brothers? How did feeder funds sell Bernard Madoff's invesments? We would never act as they...
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by Sean Silverthorne
- 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...
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by James Heskett
- 04 Jan 2011
- Working Paper Summaries
The Learning Effects of Monitoring
- 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...
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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.)
- 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...
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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.
- 18 Jan 2021
- Book
How Thinking Like a Startup Helps Governments Solve More Problems
Author Martha Lagace is a writer based in the Boston area. [Image: damircudic] Click to watch. Book Excerpt Chapter 1: Problems as Opportunities By Mitchell Weiss There is a particular and much loved kind of...
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Keywords:
by Martha Lagace