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(1,156)
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- Faculty Publications (570)
Show Results For
- All HBS Web
(1,156)
- News (153)
- Research (843)
- Events (17)
- Multimedia (9)
- Faculty Publications (570)
- June 2008
- Article
'Thar' She Blows: Can Bubbles Be Rekindled with Experienced Subjects?
By: Reshmaan Hussam, David Porter and Vernon Smith
We report 28 new experiment sessions consisting of up to three experience levels to examine the robustness of learning and “error” elimination among participants in a laboratory asset market and its effect on price bubbles. Our answer to the title question is: “yes.”... View Details
Hussam, Reshmaan, David Porter, and Vernon Smith. "'Thar' She Blows: Can Bubbles Be Rekindled with Experienced Subjects?" American Economic Review 98, no. 3 (June 2008): 924–937.
- 2000
- Book
Learning in Action: A Guide to Putting the Learning Organization to Work
By: David A. Garvin
Keywords: Market Intelligence; Learning Organizations; After-Action Reviews; Experimentation; Learning
Garvin, David A. Learning in Action: A Guide to Putting the Learning Organization to Work. Boston: Harvard Business School Press, 2000.
- Research Summary
The Chopstick Auction - An Experimental Study of the Exposure Problem in Auctions (with P. Guillen, L. Llorente, S. Onderstal, R. Sausgruber), 2002
Multi-unit auctions are sometimes plagued by the so-called exposure problem. In this paper, we analyze a simple game called the "chopstick auction" in which bidders are confronted with the exposure problem. We analyze the chopstick auction with incomplete information... View Details
- Article
The Magic That Makes Customer Experiences Stick
By: Stefan Thomke
Why do some customer experiences have that magical "wow" factor, making them all destined for success, while others get few, if any, enthusiastic customer responses? How would we "design" a great customer experience? These are some of the questions that the article... View Details
Keywords: Customer Experience; Emotion; Innovation; Experimentation; Storytelling; Customer Satisfaction; Emotions; Design; Innovation and Invention
Thomke, Stefan. "The Magic That Makes Customer Experiences Stick." MIT Sloan Management Review 61, no. 1 (Fall 2019).
- February 2021
- Tutorial
T-tests: Theory and Practice
This video provides an introduction to hypothesis testing, sampling, t-tests, and p-values. It provides examples of A/B testing and t-testing to assess whether difference between two groups are statistically significant. This video can be assigned in conjunction with... View Details
- April 2020
- Article
Field Comparisons of Incentive-Compatible Preference Elicitation Techniques
By: Shawn A. Cole, A. Nilesh Fernando, Daniel Stein and Jeremy Tobacman
Knowledge of consumer demand is important for firms, policy makers, and economists. One common tool for incentive-compatible demand elicitation, the Becker-DeGroot-Marschak (BDM) mechanism, has been widely used in laboratory settings but rarely evaluated for... View Details
Keywords: Incentive-compatible Elicitation; Experimental Methods; Weather Insurance; Rainfall Insurance; Agricultural Extension; Demand and Consumers
Cole, Shawn A., A. Nilesh Fernando, Daniel Stein, and Jeremy Tobacman. "Field Comparisons of Incentive-Compatible Preference Elicitation Techniques." Journal of Economic Behavior & Organization 172 (April 2020): 33–56.
- Research Summary
Overview
My work assists small business entrepreneurs, and high-growth technology firms that serve them, to grow with technology and AI in emerging economies. View Details
- 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).
- April 2015
- Article
Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers
By: Shawn Cole, Martin Kanz and Leora Klapper
This paper uses a series of experiments with commercial bank loan officers to test the effect of performance incentives on risk assessment and lending decisions. We first show that while high-powered incentives lead to greater screening effort and more profitable... View Details
Keywords: Banking; Management Processes; Credit Products; Experimental Economics; Risk Management; Motivation and Incentives; Management Practices and Processes; Financing and Loans; Banking Industry
Cole, Shawn, Martin Kanz, and Leora Klapper. "Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers." Journal of Finance 70, no. 2 (April 2015): 537–575.
- February 2021
- Tutorial
Getting Started in RStudio Cloud
By: Chiara Farronato and Caleb Kwon
This video provides an introduction to the free programming language R using an online cloud version of RStudio, which is the most popular editor and interface for writing and executing R code. The video begins by providing a brief background of R and RStudio and... View Details
- April 2020
- Article
Designs for Estimating the Treatment Effect in Networks with Interference
By: Ravi Jagadeesan, Natesh S. Pillai and Alexander Volfovsky
In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment... View Details
Keywords: Experimental Design; Network Inference; Neyman Estimator; Symmetric Interference Model; Homophily
Jagadeesan, Ravi, Natesh S. Pillai, and Alexander Volfovsky. "Designs for Estimating the Treatment Effect in Networks with Interference." Annals of Statistics 48, no. 2 (April 2020): 679–712.
- September 2016 (Revised October 2018)
- Case
LabCDMX: Experiment 50
By: Mitchell Weiss and Maria Fernanda Miguel
There were probably 30,000 public buses, minibuses, and vans in Mexico City. Though, in 2015, no one knew for certain since no comprehensive schedule existed. This was why el Laboratorio para la Ciudad (or LabCDMX) had spawned an effort to generate a map of the... View Details
Keywords: Public Entrepreneurship; Experimentation; Lean Startup; Government; Innovation; Crowdsourcing; Open Data; Entrepreneurship; Social Entrepreneurship; Innovation and Invention; Innovation Leadership; Government Administration; Transportation; Transportation Industry; Mexico City; Mexico
Weiss, Mitchell, and Maria Fernanda Miguel. "LabCDMX: Experiment 50." Harvard Business School Case 817-031, September 2016. (Revised October 2018.)
- June 2022 (Revised January 2025)
- Technical Note
Causal Inference
This note provides an overview of causal inference for an introductory data science course. First, the note discusses observational studies and confounding variables. Next the note describes how randomized experiments can be used to account for the effect of... View Details
Keywords: Causal Inference; Causality; Experiment; Experimental Design; Data Science; Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Causal Inference." Harvard Business School Technical Note 622-111, June 2022. (Revised January 2025.)
- May 2012
- Background Note
Innovation Magic
By: Stefan Thomke and Jason Randal
Why do certain product and service experiences seem like magic, making them all but destined for success, while other items languish on store shelves? For a better understanding of that, perhaps there's no better place to turn to than the world of magic. Consider that... View Details
Keywords: Innovation; Product Differentiation; Experimentation; Personal Strategy ＆ Style; Innovation and Invention; Creativity; Service Operations; Product; Customer Satisfaction
Thomke, Stefan, and Jason Randal. "Innovation Magic." Harvard Business School Background Note 612-099, May 2012.
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
Bojinov, Iavor I., Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih, and Michael W. Toffel. "Comparing Two Groups: Sampling and t-Testing." Harvard Business School Technical Note 621-044, August 2020.
- 2024
- Working Paper
Consumer Choice and Corporate Bankruptcy
By: Samuel Antill and Megan Hunter
We estimate the indirect costs of corporate bankruptcy associated with lost
customers. In incentivized experiments, randomly informing consumers about a firm’s Chapter 11 reorganization lowers their willingness to pay for the firm’s products by 18-35%. Up
to 48% of... View Details
Keywords: Consumer Choice; Bankruptcy; Financial Distress; Structural Estimation; Experimental Economics; Hertz; Insolvency and Bankruptcy; Consumer Behavior
Antill, Samuel, and Megan Hunter. "Consumer Choice and Corporate Bankruptcy." Working Paper, January 2024. (Revise & Resubmit, Journal of Finance.)
- 2021
- Working Paper
The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes
By: Arlen Guarin, Christian Posso, Estefania Saravia and Jorge Tamayo
Identifying the effect of physicians’ skills on health outcomes is a challenging task due to the nonrandom sorting between physicians and hospitals. We overcome this challenge by exploiting a Colombian government program that randomly assigned 2,126 physicians to 618... View Details
Keywords: Physicians' Health Skills; Health Birth Outcomes; Birthing Outcomes; Experimental Evidence; Health Care and Treatment; Competency and Skills; Outcome or Result; Health Industry; Colombia
Guarin, Arlen, Christian Posso, Estefania Saravia, and Jorge Tamayo. "The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes." Harvard Business School Working Paper, No. 22-015, February 2021. (R&R American Economic Journal.)
- September 2013
- Teaching Note
Gary Hirshberg and Stonyfield Farm
By: Nancy F. Koehn and Nora N. Khan
Gary Hirshberg and Stonyfield Farm is the story of one entrepreneur's vision and journey to create a market-leading, environmentally responsible business founded on the principles of product quality, organizational alignment and sustainability. A former... View Details
- March 2012 (Revised October 2012)
- Case
Gary Hirshberg and Stonyfield Farm
By: Nancy F. Koehn, Nora N. Khan and Elizabeth W. Legris
Gary Hirshberg and Stonyfield Farm is the story of one entrepreneur's vision and journey to create a market-leading, environmentally responsible business founded on the principles of product quality, organizational alignment, and sustainability. A former environmental... View Details
Keywords: Entrepreneurs; Values; Development Stage Enterprises; Innovation; Management By Objective; Experimentation; Emerging Technologies; Mission and Purpose; Management Style; Values and Beliefs; Social Issues; Organizational Culture; Environmental Sustainability; Business Growth and Maturation; Entrepreneurship; Business Startups; Innovation and Invention; Food and Beverage Industry; New Hampshire
Koehn, Nancy F., Nora N. Khan, and Elizabeth W. Legris. "Gary Hirshberg and Stonyfield Farm." Harvard Business School Case 312-122, March 2012. (Revised October 2012.)
- 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.)