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- March–April 2025
- Article
Getting Value from Digital Technologies
By: Frank Cespedes and Georg Krentzel
Companies need digital technologies in an omni-channel buying world where online and in-person interactions are complements, not either/or substitutes. Multi-channel hybrid sales solutions are required, but what are the key requirements for using the available... 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 Working Multiple Jobs: Implications for Spending Behavior and Wellbeing
By: Paige Tsai and Ryan W. Buell
Problem definition: Amidst inflation, rising costs of living, an explosion in remote and gig working opportunities, and an increase in the part-time labor mix in economies around the world, it is becoming evermore commonplace for
people to earn labor income... 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; Decision Making; Automation; Benefits; Compensation; Cost Reduction; Digital Transformation; Employment; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Information Technology; Insurance; United States
- October 2024
- Article
Canary Categories
By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
Past customer spending in a category is generally a positive signal of future customer spending. We show that there exist “canary categories” for which the reverse is true. Purchases in these categories are a signal that customers are less likely to return to that... View Details
- 2025
- Working Paper
Global Evidence on Gender Gaps and Generative AI
By: Nicholas G. Otis, Solène Delecourt, Katelynn Cranney and Rembrand Koning
Generative AI has the potential to transform productivity and reduce inequality, but only if adopted broadly. In this paper, we show that recently identified gender gaps in generative AI use are nearly universal. Synthesizing data from 18 studies covering more than... View Details
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
- September–October 2024
- Article
Where Data-Driven Decision-Making Can Go Wrong
By: Michael Luca and Amy C. Edmondson
When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any... View Details
- September–October 2024
- Article
Working Around the Clock: Temporal Distance, Intrafirm Communication, and Time Shifting of the Employee Workday
By: Jasmina Chauvin, Prithwiraj Choudhury and Tommy Pan Fang
This paper examines the effects of temporal distance generated by time zone separation on communication in geographically distributed organizations. We build on prior research, which highlights time zone separation as a significant challenge, but argue that employees... View Details
- August 2024
- Case
The Walt Disney Company: Management Guidance
By: Joseph Pacelli and James Weber
In November 2023, financial analyst Aurora Fee was forecasting The Walt Disney Company’s earnings and stock price, with the goal of providing an investment recommendation to her clients. Disney, one of the world’s largest media and entertainment companies, had just... View Details
- 2024
- Working Paper
The Narrative AI Advantage? A Field Experiment on Generative AI-Augmented Evaluations of Early-Stage Innovations
By: Jacqueline N. Lane, Léonard Boussioux, Charles Ayoubi, Ying Hao Chen, Camila Lin, Rebecca Spens, Pooja Wagh and Pei-Hsin Wang
The rise of generative artificial intelligence (AI) is transforming creative problem-solving, necessitating new approaches for evaluating innovative solutions. This study explores how human-AI collaboration can enhance early-stage evaluations, focusing on the interplay... View Details
- August 2024
- Article
How Do Copayment Coupons Affect Branded Drug Prices and Quantities Purchased?
By: Leemore S. Dafny, Kate Ho and Edward Kong
Drug copayment coupons to reduce patient cost-sharing have become nearly ubiquitous for high-priced brand-name prescription drugs. Medicare bans such coupons on the grounds that they are kickbacks that induce utilization, but they are commonly used by... View Details
- July, 2024
- Article
Consumer Protection in an Online World: An Analysis of Occupational Licensing
By: Chiara Farronato, Andrey Fradkin, Bradley Larsen and Erik Brynjolfsson
We study the demand and supply implications of occupational licensing using transaction-level data from a large online platform for home improvement services. We find that demand is more responsive to a professional's reviews than to the professional's... View Details
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
- 2024
- Working Paper
Does the Case for Private Equity Still Hold?
By: Nori Gerardo Lietz and Philipp Chvanov
Private Equity (“PE”) received a 10-fold increase in capital flows since the Great Financial Crisis (“GFC”) Investors sought higher nominal returns relative to those they could obtain in the public capital markets. This paper questions the fundamental assumptions... View Details
- March–April 2024
- Article
Retailers and Health Systems Can Improve Care Together
By: Robert S. Huckman, Vivian S. Lee and Bradley R Staats
Health systems are struggling to address the many shortcomings of health care delivery: rapidly growing costs, inconsistent quality, and inadequate and unequal access to primary and other types of care. However, if retailers and health systems were to form strong... View Details