Publications
Publications
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- All HBS Web
(4,160)
- Faculty Publications (1,263)
- April 2023 (Revised February 2024)
- Case
AI Wars
By: Andy Wu, Matt Higgins, Miaomiao Zhang and Hang Jiang
In February 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI. Over a year ago, OpenAI released ChatGPT, a... View Details
- 2023
- Working Paper
Corporate Website-based Measures of Firms' Value Drivers
By: Wei Cai, Dennis Campbell and Patrick Ferguson
We develop and validate new text-based measures of firms’ financial and non-financial value drivers. Using the Wayback Machine to access public US firms’ archived websites from 1995-2020, we scrape text from corporate homepages. We use Kaplan and Norton’s (1992)... View Details
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
- April 2023
- Article
Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below
By: Ting Zhang, Dan Wang and Adam D. Galinsky
Although mentorship is vital for individual success, potential mentors often view it as a costly burden. To understand what motivates mentors to overcome this barrier and more fully engage with their mentees, we introduce a new construct, learning direction, which... View Details
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
- 2023
- Working Paper
Organizational Responses to Product Cycles
By: Achyuta Adhvaryu, Vittorio Bassi, Anant Nyshadham, Jorge Tamayo and Nicolas Torres
Product cycles entail the mass production of new—and often increasingly complex—products on a regular basis. How do firms manage these changes? We use granular daily data from a leading automobile manufacturer to study the organizational impacts of introducing new... View Details
- March 2023
- Teaching Note
VideaHealth: Building the AI Factory
By: Karim R. Lakhani
Teaching Note for HBS Case No. 621-021. The case “VideaHealth: Building the AI Factory” examines the creation of dental startup VideaHealth (Videa) and the development of its artificial intelligence (AI)-led business strategy through the eyes of founder and CEO Florian... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
- March 2023 (Revised March 2025)
- Case
Accelerating AI Adoption in the U.S. Air Force
By: Maria P. Roche and Alexander Farrow
In August 2022, the Pentagon tasked U.S. Air Force Captain Victor Lopez to launch a new office for AFWERX, an Air Force innovation unit that leveraged commercial developers and military talent to acquire advanced technologies. This task was particularly arduous because... View Details
- March 2023
- Article
Not from Concentrate: Collusion in Collaborative Industries
By: Jordan M. Barry, John William Hatfield, Scott Duke Kominers and Richard Lowery
The chief principle of antitrust law and theory is that reducing market concentration—having more, smaller firms instead of fewer, bigger ones—reduces anticompetitive behavior. We demonstrate that this principle is fundamentally incomplete.
In many... View Details
In many... View Details
Keywords: Antitrust; Antitrust Law; Antitrust Theory; Law And Economics; Collusion; Collaboration; Collaborative Industries; Regulation; ＂Repeated Games＂; IPOs; Initial Public Offerings; Underwriters; Real Estate; Real Estate Agents; Realtors; Syndicated Markets; Syndication; Brokers; Market Concentration; Competition; Law; Economics; Collaborative Innovation and Invention; Governing Rules, Regulations, and Reforms; Game Theory; Initial Public Offering
- 2023
- Working Paper
Translating Information into Action: A Public Health Experiment in Bangladesh
By: Reshmaan Hussam, Kailash Pandey, Abu Shonchoy and Chikako Yamauchi
While models of technology adoption posit learning as the basis of behavior change, information campaigns in public health frequently fail to change behavior. We design an information campaign embedding hand-hygiene edutainment within popular dramas using mobile... View Details
- 2023
- Working Paper
Senior Team Emotional Dynamics and Strategic Decision Making at a Platform Transition
By: Timo O. Vuori and Michael L. Tushman
Based on an inductive case study, we develop an emotional-temporal process model of an incumbent’s
strategic decision making at a platform transition. We describe the senior team’s emotional response to
this transition and the impact of these emotions on their... View Details
- 2023
- Working Paper
Sending Signals: Strategic Displays of Warmth and Competence
By: Bushra S. Guenoun and Julian J. Zlatev
Using a combination of exploratory and confirmatory approaches, this research examines how
people signal important information about themselves to others. We first train machine learning
models to assess the use of warmth and competence impression management... View Details
- February 2023 (Revised March 2025)
- Case
Graphic Packaging: Project Cowboy (A)
By: Benjamin C. Esty and E. Scott Mayfield
In July 2019, Graphic Packaging CEO Michael Doss was proposing a $600 million investment in a new machine to produce coated recycled board (CRB), a type of paper packaging used for consumer products (cups, cereal boxes, beverage boxes, etc.) that utilized recycled... View Details
- February 2023
- Supplement
Graphic Packaging: Project Cowboy (A) Courseware
By: Benjamin C. Esty and Scott Mayfield
In July 2019, Graphic Packaging CEO Michael Doss was proposing a $600 million investment in a new machine to produce coated recycled board (CRB), a type of paper packaging used for consumer products (cups, cereal boxes, beverage boxes, etc.) that utilized recycled... View Details
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
- Other Article
Introduction
By: Stefano Brusoni, Joachim Henkel, Michael G Jacobides, Samina Karim, Alan MacCormack, Phanish Puranam and Melissa Schilling
In 2000, Carliss Baldwin and Kim Clark published Design Rules: The Power of Modularity, a book that introduced new ways of understanding and explaining the architecture of complex systems. This Special Issue of Industrial and Corporate Change celebrates... View Details