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- 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
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
- 2025
- Working Paper
Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India
By: Shawn Cole, Grady Killeen, Tomoko Harigaya and Aparna Krishna
This paper evaluates a low-cost, customized soil nutrient management advisory service in India. As a methodological contribution, we examine whether and in which settings satellite measurements may be effective at estimating both agricultural yields and treatment... View Details
- January 2025
- Module Note
Understanding and Addressing Gender Gaps
This module provides a framework for students to analyze how gender stereotypes, through their impact on beliefs about others and beliefs about ourselves, contribute to gender gaps in the workplace. The module proceeds in three parts. First, through a case and an... View Details
- January–February 2025
- Article
Why People Resist Embracing AI
The success of AI depends not only on its capabilities, which are becoming more advanced each day, but on people’s willingness to harness them. Unfortunately, many people view AI negatively, fearing it will cause job losses, increase the likelihood that their personal... View Details
- 2024
- Working Paper
Bounded Solidarity: The Role of Migrants in Shaping Entrepreneurial Ventures
By: Astrid Marinoni and Prithwiraj Choudhury
We explore a previously unexamined aspect of migrants’ contributions to local entrepreneurial
ecosystems: the value created by cooperative interactions between migrants and locals in entrepreneurial
ventures. Specifically, we analyze whether mixed teams composed of... View Details
- 2024
- Working Paper
The New Digital Divide
By: Mayana Pereira, Shane Greenstein, Raffaella Sadun, Prasanna Tambe, Lucia Ronchi Darre, Tammy Glazer, Allen Kim, Rahul Dodhia and Juan Lavista Ferres
We build and analyze new metrics of digital usage that leverage telemetry data collected by Microsoft during operating system updates across forty million Windows devices in U.S. households. These measures of US household digital usage are much more comprehensive than... View Details
- July 2024
- Article
Demographic 'Stickiness': The Demographic Identity of Departing Group Members Influences Who Is Chosen to Replace Them
By: Edward H. Chang and Erika Kirgios
People tasked with replacing a departing group member are disproportionately likely to choose a replacement with the same demographic identity, leading to demographic “stickiness” in group composition. We examine this effect in 2,163 U.S. federal judge appointments... View Details
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
- 2024
- Working Paper
Webmunk: A New Tool for Studying Online Behavior and Digital Platforms
By: Chiara Farronato, Andrey Fradkin and Chris Karr
Understanding the behavior of users online is important for researchers, policymakers, and private companies alike. But observing online behavior and conducting experiments is difficult without direct access to the user base and software of technology companies. We... 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
Don’t Expect Juniors to Teach Senior Professionals to Use Generative AI: Emerging Technology Risks and Novice AI Risk Mitigation Tactics
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
- 2024
- Working Paper
Winner Take All: Exploiting Asymmetry in Factorial Designs
By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti
Researchers and practitioners have embraced factorial experiments to simultaneously test multiple treatments, each with different levels. With the rise of technologies like Generative AI, factorial experimentation has become even more accessible: it is easier than ever... View Details
- 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
- May–June 2024
- Article
Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs
By: Jacqueline N. Lane, Karim R. Lakhani and Roberto Fernandez
Competence development in digital technologies, analytics, and artificial intelligence is increasingly important to all types of organizations and their workforce. Universities and corporations are investing heavily in developing training programs, at all tenure... View Details
- May 2024
- Article
True Costs of Uterine Artery Embolization: Time-Driven Activity-Based Costing in Interventional Radiology Over a 3-Year Period
By: Julia C. Bulman, Nicole H. Kim, Robert S. Kaplan, Sarah Schroeppel DeBacker, Olga R. Brook and Ammar Sarwar
The study used time-driven activity-based costing (TDABC) to estimate the costs to perform uterine artery embolization (UAE). Utilization times for patients undergoing outpatient UAE for fibroids or adenomyosis were captured from electronic health record timestamps and... View Details
- April 2024
- Article
An Integrative Model of Hybrid Governance: The Role of Boards in Helping Sustain Organizational Hybridity
By: Anne-Claire Pache, Julie Battilana and Channing Spencer
Hybrid organizations must sustainably attend to multiple goals embedded in different institutional spheres. Past research has highlighted the value for hybrids in recruiting board members representing different logics to avoid attentional drifts; yet, diverse boards... View Details
- March 2024 (Revised December 2024)
- Case
TELEXISTENCE Inc.
By: Paul A. Gompers and Akiko Saito
A case about a Japanese robotics startup aiming to enter the U.S. market with its robots that combine AI and human intervention to complete restocking tasks in retail stores. View Details
- February 9, 2024
- Article
Addressing Climate Change with Behavioral Science: A Global Intervention Tournament in 63 Countries
By: Madalina Vlasceanu, Kimberly C. Doell, Joseph B. Bak-Coleman, Boryana Todorova, Michael M. Berkebile-Weinberg, Amit Goldenberg, Eric Shuman and et al.
Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate... View Details
- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,... View Details