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- 2025
- Working Paper
Is Love Blind? AI-Powered Trading with Emotional Dividends
By: Valeria Fedyk, Daniel Rabetti and Stella Kong
We leverage the non-fungible tokens (NFTs) setting to assess the valuation of emotional dividends (LOVE), a long-standing empirical challenge in private-value markets such as art, antiques, and collectibles. Having created and validated our proxy, we use deep learning... View Details
Keywords: AI and Machine Learning
- 2025
- Article
Emotion Regulation Contagion Drives Reduction in Negative Intergroup Emotions
By: Michael Pinus, Yajun Cao, Eran Halperin, Alin Coman, James J. Gross and Amit Goldenberg
When emotions occur in groups, they sometimes impact group behavior in undesired ways. Reducing group’s emotions with emotion regulation interventions can be helpful, but may also be a challenge, because treating every person in the group is often infeasible. One... View Details
- January 2025
- Case
A Tiger in the Tank: Exxon Sues Investors
By: Clayton S. Rose, Sarah Sasso and James Weber
In June 2024, investors were trying to make sense of ExxonMobil’s (Exxon) lawsuit against two impact investors, Arjuna Capital (Arjuna) and Follow This, that had just been dismissed by the U.S. District Court of Northern Texas. Exxon’s suit challenged the rights of two... View Details
Keywords: Disruption; Talent and Talent Management; Customer Satisfaction; Decision Making; Demographics; Ethics; Corporate Accountability; Employees; Recruitment; Retention; Leadership; Crisis Management; Risk Management; Corporate Social Responsibility and Impact; Mission and Purpose; Organizational Change and Adaptation; Civil Society or Community; Social Issues; Adaptation; Investment Activism; Lawsuits and Litigation; Business and Shareholder Relations; Medical Devices and Supplies Industry; Health Industry; Energy Industry; United States; Netherlands; Norway
- 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
- 2024
- Working Paper
The Real Effects of Bankruptcy Forum Shopping
By: Samuel Antill and Aymeric Bellon
Many non-Delaware firms strategically file for bankruptcy in Delaware. Should this "forum shopping" be allowed? This question has motivated six congressional bill proposals over decades of policy debate. Using a novel natural experiment and Census-Bureau microdata, we... 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
- Article
Preference Externality Estimators: A Comparison of Border Approaches and IVs
By: Xi Ling, Wesley R. Hartmann and Tomomichi Amano
This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in... 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
- 2024
- Working Paper
People, Practices, and Productivity: A Review of New Advances in Personnel Economics
By: Mitchell Hoffman and Christopher T. Stanton
This chapter surveys recent advances in personnel economics. We begin by presenting evidence showing substantial and persistent productivity variation among workers in the same roles. We discuss new research on incentives and compensation; hiring practices; the... View Details
- July 2024
- Case
Roja Garimella: Developing a Founder's Judgment
By: Reza Satchu and Patrick Sanguineti
Roja Garimella’s path to becoming a founder was anything but straight. Setting her sights on a career in medicine since childhood, she committed to medical school with her acceptance to college. And yet, throughout her studies, she continually explored alternative... View Details
- 2024
- Working Paper
The Value of Silence: The Effect of UMG’s Licensing Dispute with TikTok on Music Demand
By: Mengjie (Magie) Cheng, Elie Ofek and Hema Yoganarasimhan
Social media platforms like TikTok have transformed how music is discovered, consumed, and
monetized. This study examines the implications of the dispute between TikTok and Universal Music
Group (UMG), which resulted in UMG excluding its music from TikTok from... 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
- June 2024
- Article
The Monitoring Role of Social Media
By: Jonas Heese and Joseph Pacelli
In this study, we examine whether social media activity can reduce corporate misconduct. We use the staggered introduction of 3G mobile broadband access across the United States to identify exogenous increases in social media activity and test whether access to 3G... View Details
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
- March 2024
- Article
Investigation of Divergent Thinking among Surgeons and Surgeon Trainees in Canada (IDEAS): A Mixed-methods Study
By: Alex Thabane, Tyler McKechnie, Vikram Arora, Goran Calic, Jason W Busse, Ranil Sonnadara and Mohit Bhandari
Objective: To assess the creative potential of surgeons and surgeon trainees, as measured by divergent thinking. The secondary objectives were to identify factors associated with divergent thinking, assess confidence in creative problem-solving and the perceived effect... View Details
- 2024
- Chapter
Managing for Organisational Integrity: My Take After Three Decades
By: Lynn S. Paine
This chapter revisits core ideas from my 1994 article “Managing for Organizational Integrity” and explores a critical issue not discussed in the article: the role of corporate boards. In the chapter, I first re-examine the article’s ideas about the origins of... View Details
- Working Paper
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
By: Flora Feng, Charis Li and Shunyuan Zhang
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely... View Details
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which... View Details