Publications
Publications
Show Results For
- All HBS Web
(2,162)
- Faculty Publications (248)
Page 1 of 248
Results →
- 2025
- Article
Difference-in-Differences Subset Scan
By: Will Stamey, Sriram Somanchi and Edward McFowland III
Difference-in-differences (DiD) has been extensively applied in the literature to elicit the average causal effect of an intervention or policy. Though researchers explore heterogeneity in the treatment effect with respect to time or some observed covariate (usually... View Details
- Article
Using Gen AI for Early-Stage Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Generative AI, particularly large language models (LLMs), offers a promising new tool for early-stage market research by simulating customer responses to product concepts. This can allow companies to draw conclusions similar to those they’d obtain by surveying... View Details
- June 2025
- Case
Scale AI Scales Up
By: Boris Groysberg and Sarah L. Abbott
Scale AI, the data labeling and AI infrastructure company, had grown rapidly since it was founded in 2016; however, as Scale’s generative AI business was taking off, Alexandr Wang, Scale’s founder and CEO, became concerned that Scale was slowing down. Wang and the... View Details
Keywords: Technology And Innovation Management; Entrepreneur; Managing Growth; Hiring; Generative Ai; Data Labeling; Scale; AI and Machine Learning; Entrepreneurship; Talent and Talent Management; Growth Management; Leadership; Organizational Culture; Business Startups; Selection and Staffing; Technology Industry; United States
- June 2025
- Article
Ideation with Generative AI—In Consumer Research and Beyond
By: Julian De Freitas, G. Nave and Stefano Puntoni
The use of large language models (LLMs) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing... View Details
- 2025
- Working Paper
Trade and Industrial Policy in Supply Chains: Directed Technological Change in Rare Earths
By: Laura Alfaro, Harald Fadinger, Jay Schymik and Gede Virananda
Trade and industrial policies, while primarily intended to support domestic industries, may unintentionally stimulate technological progress abroad. We document this mechanism in the case of rare earth elements (REEs)—critical inputs for manufacturing at the knowledge... View Details
- Working Paper
Shifting Work Patterns with Generative AI
By: Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica and Christopher T. Stanton
We present evidence on how generative AI changes the work patterns of knowledge workers using
data from a 6-month-long, cross-industry, randomized field experiment. Half of the 7,137 workers
in the study received access to a generative AI tool integrated into the... View Details
- 2024
- Case
EPCorp: Convincing the C-Suite
By: Jacob M. Cook
In EPCorp: Convincing the C-Suite, Shivani Bahl is attempting to sell EPCorp's CEO, Debbie Sullivan, on her ideas for not only a new website upgrade but also a more expansive vision on how data and Generative AI can be used to grow the company. Debbie is understandably... View Details
- 2025
- Working Paper
Race, Rental Yields, and Housing Decay in Manhattan
By: Tom Nicholas and Christophe Spaenjers
We develop a new dataset on real estate transactions in Manhattan (1912–1939), linked to federal Census records (1930 and 1940) and property images used for tax assessment purposes (around 1940 and 1980). We analyze investor returns and incentives to maintain... View Details
- May–June 2025
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
- April 2025 (Revised May 2025)
- Case
Adobe: GenAI Opportunity or Threat?
By: Sunil Gupta, Rajiv Lal and Allison Ciechanover
In December 2022, Adobe CEO Shantanu Narayen faced a pivotal strategic decision due to the rapid rise of generative AI image models from OpenAI, Midjourney, and StabilityAI. Adobe, a leader in digital media and marketing software with a 40-year legacy of innovation and... View Details
- April 2025 (Revised April 2025)
- Case
JPMorganChase: Leadership in the Age of GenAI
By: Iavor I. Bojinov, Karim R. Lakhani and David Lane
This case study examines JPMorgan Chase's (JPMC) journey in adopting and implementing Generative AI (GenAI) following the release of ChatGPT. It outlines JPMC's initial cautious approach focused on data security, followed by strategic investments in internal platforms... View Details
Keywords: Banks and Banking; Governance Controls; Information Technology; AI and Machine Learning; Analytics and Data Science; Cybersecurity; Digital Platforms; Digital Transformation; Information Management; Information Infrastructure; Technology Adoption; Job Cuts and Outsourcing; Knowledge Management; Knowledge Sharing; Leading Change; Growth and Development Strategy; Marketing; Product Development; Performance Improvement; Customization and Personalization; Financial Services Industry; United States
- March 2025
- Case
Metaphysic AI: Rethinking the Value of Human Expertise
By: Zoë B. Cullen, Shikhar Ghosh and Shweta Bagai
In early 2025, Thomas Graham, CEO of Metaphysic, a leading AI generative video company confronted fundamental questions about who should control digital identity in a world where AI could perfectly recreate human likeness. Founded in 2021, Metaphysic first rose to fame... View Details
- March 2025
- Article
Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures
By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani
The literature on communities of practice demonstrates that a proven way for senior professionals to upskill
themselves in the use of new technologies that undermine existing expertise is to learn from junior
professionals. It notes that juniors may be better able... View Details
- 2025
- Working Paper
The Hidden Costs of Flexible Labor Models: How Working Multiple Jobs Affects Employees
By: Paige Tsai and Ryan W. Buell
As operations increasingly rely upon flexible labor models—such as gig, part-time, and remote work—it has become commonplace for individuals to work multiple jobs. Across three studies, relying on a combination of transaction-level data from 90,548 customers of a... View Details
- 2025
- Working Paper
Combining Complements: Theory and Evidence from Cancer Treatment Innovation
By: Rebekah Dix and Todd A. Lensman
Innovations often combine several components to achieve outcomes greater than the
“sum of the parts.” We argue that such combination innovations can introduce an understudied
inefficiency—a positive market expansion externality that benefits the owners of
the... View Details
- 2025
- Working Paper
Causes and Extent of Increasing Partisan Segregation in the U.S. – Evidence from Migration Patterns of 212 Million Voters
By: Jacob R. Brown, Enrico Cantoni, Vincent Pons and Emilie Sartre
Using data on the residential location and migration for every voter in U.S. states recording partisan registration between 2008–2020, we find that residential segregation between Democrats and Republicans has increased year over year at all geographic levels, from... View Details
- 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
- January–February 2025
- Article
Want Your Company to Get Better at Experimentation?: Learn Fast by Democratizing Testing
By: Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley
For years, online experimentation has fueled the innovations of leading tech companies, enabling them to rapidly test and refine new ideas, optimize product features, personalize user experiences, and maintain a competitive edge. The widespread availability and lower... View Details
- 2024
- Working Paper
Scaling Core Earnings Measurement with Large Language Models
By: Matthew Shaffer and Charles CY Wang
We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details
- November 2024 (Revised January 2025)
- Case
MiDAS: Automating Unemployment Benefits
By: Shikhar Ghosh and Shweta Bagai
In 2015, the state of Michigan considered whether to nominate its Michigan Integrated Data Automated System (MiDAS) for a prestigious state technology award. Launched in 2013 amid severe budget pressures, the $47 million automated fraud detection system was designed to... View Details
Keywords: Artificial Intelligence; AI; Machine Learning Models; Algorithmic Data; Automation; Benefits; Compensation; Cost Reduction; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Insurance; Decision Making; Digital Transformation; Employment; Public Administration Industry; United States; Michigan