Filter Results:
(10,177)
Show Results For
- All HBS Web
(10,177)
- People (64)
- News (3,257)
- Research (3,953)
- Events (24)
- Multimedia (60)
- Faculty Publications (1,368)
Show Results For
- All HBS Web
(10,177)
- People (64)
- News (3,257)
- Research (3,953)
- Events (24)
- Multimedia (60)
- Faculty Publications (1,368)
- 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... View Details
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.
- 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... View Details
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.)
- 20 Dec 2022
- Blog Post
Thinking About an MBA? Think About Your Purpose
Why get an MBA? Many of my students are excited to acquire the tools that will help them solve the complex challenges that await them in the business world. That is admirable, but I have found that my most successful students are also guided View Details
- 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... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Working Paper, March 2022.
- 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... View Details
Keywords: by Sean Silverthorne
- 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? View Details
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.
- 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... View Details
Keywords: by Dina Gerdeman
- 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
- 27 Jun 2007
- Lessons from the Classroom
Learning to Make the Move to CEO
bring back what they've learned to their organizations? "Graduates walk a fine line," Simons remarks. "On the one hand, it's not wise to come back with the attitude that they know it all and are ready to save the company.... View Details
- 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... View Details
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.
- 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.)
- 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... View Details
Keywords: by Martha Lagace
- 09 Jun 2023
- Blog Post
Learning Curve
career in the field but instead found herself in quasi-retirement at age 35. “Life has a way of getting in the way,” she notes. Melcher’s first child, Katie, struggled in preschool with learning disabilities, and Melcher made the decision... View Details
- 01 Mar 2013
- News
Learning from Lincoln
- 07 Sep 2022
- News
Bored at Work? Learn to Manage It by Putting It to Work
- 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.
- 01 Jun 2011
- News
Faculty Think Tank
charts and notes on modules aimed at delivering throughout the year small-group learning experiences that are experiential, immersive, and field-based. HBS plans to roll out FIELD this fall. Pictured, clockwise from left, are Joshua... View Details
Keywords: FIELD program
- 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... View Details
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.)