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Show Results For
- All HBS Web
(3,555)
- People (13)
- News (886)
- Research (2,019)
- Events (33)
- Multimedia (3)
- Faculty Publications (804)
- 01 Jun 2024
- News
Decoding the Promise and Perils of Generative AI
ChatGPT burst into the public consciousness on November 30, 2022. Within two months, the generative artificial intelligence (GenAI) software, which leverages a large language model (LLM) to produce... View Details
Keywords: April White
- 2024
- Working Paper
Old Moats for New Models: Openness, Control, and Competition in Generative AI
By: Pierre Azoulay, Joshua L. Krieger and Abhishek Nagaraj
Drawing insights from the field of innovation economics, we discuss the likely competitive environment shaping generative AI advances. Central to our analysis are the concepts of appropriability—whether firms in the industry are able to control the knowledge generated... View Details
Azoulay, Pierre, Joshua L. Krieger, and Abhishek Nagaraj. "Old Moats for New Models: Openness, Control, and Competition in Generative AI." NBER Working Paper Series, No. 7442, May 2024.
- Research Summary
A Model of Delegation in Contests (with Stefan Brandauer), 2004
We analyze a contest between two groups where group members have differing valuations for the contested rent. Generically the pivotal group member with the median valuation of the rent will not act himself but will want to send a group member that has preferences... View Details
- 02 Oct 2013
- News
General Motors Takes a Look Under Tesla's Hood
- September 2003 (Revised May 2006)
- Case
Eyeblaster: Enabling the Next Generation of Online Advertising
By: Elie Ofek
Eyeblaster management has to decide on the best course of action to sustain its momentum from enabling online rich media advertising. Pressure from competitors is forcing the company to re-evaluate its previous marketing strategy that focused primarily on getting... View Details
Keywords: Business Model; Marketing Strategy; Market Entry and Exit; Performance Evaluation; Digital Marketing; Growth and Development Strategy
Ofek, Elie. "Eyeblaster: Enabling the Next Generation of Online Advertising." Harvard Business School Case 504-005, September 2003. (Revised May 2006.)
- Teaching Interest
Data Science and Artificial Intelligence for Leaders
By: Chiara Farronato
With artificial intelligence (AI)... View Details
- Web
General Management Awards & Honors - Faculty & Research
General Management Overview Faculty Curriculum Awards & Honors Doctoral Students 2024 Arthur C. Brooks : Elected as a Member of the American Academy of Sciences & Letters in 2024. Leemore S. Dafny : Elected to the National Academy of... View Details
- 1994
- Article
Three-dimensional Finite Element Modeling of a Cervical Vertebra: An Investigation of Burst Fracture Mechanism
By: Kevin J. Bozic, J H Keyak, H B Skinner, H U Bueff and David Bradford
Finite element modeling was used to study the mechanical behavior of a cervical vertebra under axial compressive loading. A three-dimensional (3-D) finite element (FE) model of a mid-cervical vertebra using inhomogeneous material properties was generated from... View Details
- Article
Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects
By: Juan Alcácer, Wilbur Chung, Ashton Hawk and Gonçalo Pacheco-de-Almeida
Strategy aims at understanding the differential effects of firms’ actions on performance. However, standard regression models estimate only the average effects of these actions across firms. Our paper discusses how random coefficient models (RCMs) may generate new... View Details
Alcácer, Juan, Wilbur Chung, Ashton Hawk, and Gonçalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects." Strategy Science 3, no. 3 (September 2018): 481–553.
- 2025
- 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 43, no. 1 (2025): 256–268.
- December 2023
- Article
When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments
By: Christian Kaps, Simone Marinesi and Serguei Netessine
Globally, 1.5 billion people live off the grid, their only access to electricity often limited to operationally-expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and... View Details
Kaps, Christian, Simone Marinesi, and Serguei Netessine. "When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments." Management Science 69, no. 12 (December 2023): 7633–7650.
- February 21, 2025
- Article
How a Company’s Ownership Model Shapes the Mistakes It Makes
By: Josh Baron
Why do some companies continue to thrive for decades and others die after an initial run of success? Like many kinds of accidents, company failure is generally the consequence of cascading effects that combine to overwhelm a previously effective strategy. But the... View Details
Baron, Josh. "How a Company’s Ownership Model Shapes the Mistakes It Makes." Harvard Business Review Digital Articles (February 21, 2025).
- 2021
- Working Paper
Real Credit Cycles
By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S.... View Details
Bordalo, Pedro, Nicola Gennaioli, Andrei Shleifer, and Stephen J. Terry. "Real Credit Cycles." NBER Working Paper Series, No. 28416, January 2021.
- 01 Jun 2020
- What Do You Think?
Will Challenged Amazon Tweak Its Retail Model Post-Pandemic?
Amazon. Customer service is relative, a product of customer expectations compared to actual experiences. Amazon has created very high customer expectations for in-stock merchandise and rapid delivery and, in the past, generally met them.... View Details
- 2024
- Working Paper
Using LLMs for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Large language models (LLMs) have rapidly gained popularity as labor-augmenting
tools for programming, writing, and many other processes that benefit from quick text
generation. In this paper we explore the uses and benefits of LLMs for researchers and
practitioners... View Details
Keywords: Large Language Model; Research; AI and Machine Learning; Analysis; Customers; Consumer Behavior; Technology Industry; Information Technology Industry
Brand, James, Ayelet Israeli, and Donald Ngwe. "Using LLMs for Market Research." Harvard Business School Working Paper, No. 23-062, April 2023. (Revised July 2024.)
- March 2002
- Article
The Potential Role of Economic Cost Models in the Regulation of Telecommunications in Developing Countries
What is the efficient cost of providing telecommunications services to a certain area or type of customer? As developing countries build up their capacity to regulate infrastructure monopolies, cost models are likely to prove increasingly important in answering... View Details
Keywords: Information; Cost; Mathematical Methods; Developing Countries and Economies; Telecommunications Industry
Ruzzier, Christian Alejandro, D. Benitez, A. Estache, and D. M. Kennet. "The Potential Role of Economic Cost Models in the Regulation of Telecommunications in Developing Countries." Information Economics and Policy 14, no. 1 (March 2002): 21–38.
- July 16, 2016
- Article
A Game Theoretic Model for Resource Allocation Among Countermeasures with Multiple Attributes
By: Elisabeth C. Paulson, Igor Linkov and Jeffrey Keisler
We study a strategic, two-player, sequential game between an attacker and defender. The defender must allocate resources amongst possible countermeasures and across possible targets. The attacker then chooses a type of threat and a target to attack. This paper proposes... View Details
Paulson, Elisabeth C., Igor Linkov, and Jeffrey Keisler. "A Game Theoretic Model for Resource Allocation Among Countermeasures with Multiple Attributes." European Journal of Operational Research 252, no. 2 (July 16, 2016): 610–622.
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details
Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 2023
- Working Paper
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
Keywords: Performance Evaluation; Research and Development; Analytics and Data Science; Consumer Behavior
Ham, Dae Woong, Michael Lindon, Martin Tingley, and Iavor Bojinov. "Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation." Harvard Business School Working Paper, No. 23-070, May 2023.