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- All HBS Web
(12,407)
- Faculty Publications (1,591)
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- Working Paper
The Returns to Skills During the Pandemic: Experimental Evidence from Uganda
By: Livia Alfonsi, Vittorio Bassi, Imran Rasul and Elena Spadini
The Covid-19 pandemic represents one of the most significant labor market shocks to the world economy in recent times. We present evidence from a field experiment to understand whether and why skilled and unskilled workers were differentially impacted by the shock, in... View Details
Keywords: COVID-19 Pandemic; System Shocks; Labor; Competency and Skills; Development Economics; Uganda
Alfonsi, Livia, Vittorio Bassi, Imran Rasul, and Elena Spadini. "The Returns to Skills During the Pandemic: Experimental Evidence from Uganda." Harvard Business School Working Paper, No. 25-003, August 2024. (NBER Working Paper Series, No. 32785, August 2024.)
- 2024
- Working Paper
The Rise of Alternatives
By: Juliane Begenau, Pauline Liang and Emil Siriwardane
Since the 2000s, U.S. public pensions have shifted their risky investments towards alternative assets like private equity and hedge funds, some more aggressively than others. We explore several explanations for these cross-sectional trends, focusing on those implied by... View Details
Begenau, Juliane, Pauline Liang, and Emil Siriwardane. "The Rise of Alternatives." Harvard Business School Working Paper, No. 25-016, August 2024.
- July 2024
- Case
Knowledge-Enabled Financial Advice: Digital Transformation at Edward Jones
By: Lauren Cohen, Richard Ryffel, Grace Headinger and Sophia Pan
Edward Jones, a wealth management advisory firm that prided itself on its interpersonal connections and face-to-face interactions, was eager to augment their services with AI capabilities. Built on 1-to-1 close-knit relationships, the firm had more than 15,000 offices... View Details
Keywords: Fintech; Innovation And Strategy; Financial Advisors; Big Data; AI; Artificial Intelligence; Digital Strategy; Digitization; Financial Institutions; Business Strategy; Competitive Advantage; Technology Adoption; Business Plan; Technological Innovation; Interpersonal Communication; Communication Intention and Meaning; Communication Strategy; Transformation; Employee Stock Ownership Plan; Disruptive Innovation; Innovation Strategy; Innovation and Management; Innovation Leadership; Knowledge Acquisition; Knowledge Use and Leverage; Customer Relationship Management; AI and Machine Learning; Financial Services Industry; St. Louis; Missouri; United States; Canada
Cohen, Lauren, Richard Ryffel, Grace Headinger, and Sophia Pan. "Knowledge-Enabled Financial Advice: Digital Transformation at Edward Jones." Harvard Business School Case 225-009, July 2024.
- July 2024
- Article
Acceptance of Automated Vehicles Is Lower for Self than Others
By: Stuti Agarwal, Julian De Freitas, Anya Ragnhildstveit and Carey K. Morewedge
Road traffic accidents are the leading cause of death worldwide for people aged 2–59. Nearly all deaths are due to human error. Automated vehicles could reduce mortality risks, traffic congestion, and air pollution of human-driven vehicles. However, their adoption... View Details
Agarwal, Stuti, Julian De Freitas, Anya Ragnhildstveit, and Carey K. Morewedge. "Acceptance of Automated Vehicles Is Lower for Self than Others." Journal of the Association for Consumer Research 9, no. 3 (July 2024): 269–281.
- July 2024
- Article
Chatbots and Mental Health: Insights into the Safety of Generative AI
By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp and Stefano Puntoni
Chatbots are now able to engage in sophisticated conversations with consumers. Due to the ‘black box’ nature of the algorithms, it is impossible to predict in advance how these conversations will unfold. Behavioral research provides little insight into potential safety... View Details
Keywords: Autonomy; Chatbots; New Technology; Brand Crises; Mental Health; Large Language Model; AI and Machine Learning; Behavior; Well-being; Technological Innovation; Ethics
De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, and Stefano Puntoni. "Chatbots and Mental Health: Insights into the Safety of Generative AI." Journal of Consumer Psychology 34, no. 3 (July 2024): 481–491.
- 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
Keywords: Occupational Licensing; Consumer Protection; Perception; Experience and Expertise; Public Opinion; Governing Rules, Regulations, and Reforms; Demand and Consumers
Farronato, Chiara, Andrey Fradkin, Bradley Larsen, and Erik Brynjolfsson. "Consumer Protection in an Online World: An Analysis of Occupational Licensing." American Economic Journal: Applied Economics 16, no. 3 (July, 2024): 549–579.
- 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
Chang, Edward H., and Erika Kirgios. "Demographic 'Stickiness': The Demographic Identity of Departing Group Members Influences Who Is Chosen to Replace Them." Management Science 70, no. 7 (July 2024): 4236–4259.
- 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
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- July 2024
- Article
How Artificial Intelligence Constrains Human Experience
By: A. Valenzuela, S. Puntoni, D. Hoffman, N. Castelo, J. De Freitas, B. Dietvorst, C. Hildebrand, Y.E. Huh, R. Meyer, M. Sweeney, S. Talaifar, G. Tomaino and K. Wertenbroch
Many consumption decisions and experiences are digitally mediated. As a consequence, consumer behavior is increasingly the joint product of human psychology and ubiquitous algorithms (Braun et al. 2024; cf. Melumad et al. 2020). The coming of age of Large Language... View Details
Keywords: Large Language Model; User Experience; AI and Machine Learning; Consumer Behavior; Technology Adoption; Risk and Uncertainty; Cost vs Benefits
Valenzuela, A., S. Puntoni, D. Hoffman, N. Castelo, J. De Freitas, B. Dietvorst, C. Hildebrand, Y.E. Huh, R. Meyer, M. Sweeney, S. Talaifar, G. Tomaino, and K. Wertenbroch. "How Artificial Intelligence Constrains Human Experience." Journal of the Association for Consumer Research 9, no. 3 (July 2024): 241–256.
- 2024
- Working Paper
How Inflation Expectations De-Anchor: The Role of Selective Memory Cues
By: Nicola Gennaioli, Marta Leva, Raphael Schoenle and Andrei Shleifer
In a model of memory and selective recall, household inflation expectations remain rigid when inflation is anchored but exhibit sharp instability during inflation surges, as similarity prompts retrieval of forgotten high-inflation experiences. Using data from the New... View Details
Gennaioli, Nicola, Marta Leva, Raphael Schoenle, and Andrei Shleifer. "How Inflation Expectations De-Anchor: The Role of Selective Memory Cues." NBER Working Paper Series, No. 32633, June 2024.
- 2024
- Working Paper
The Golden Revolving Door
By: Ling Cen, Lauren Cohen, Jing Wu and Fan Zhang
Using both the onset of the US-China trade war in 2018 and the most recent Russia-Ukraine war and associated trade tensions, we show a counterintuitive pattern in global trade. Namely, while the average firm trading with these nations significantly decreases their... View Details
Cen, Ling, Lauren Cohen, Jing Wu, and Fan Zhang. "The Golden Revolving Door." NBER Working Paper Series, No. 32621, June 2024.
- 2024
- Working Paper
Webmunk: A New Tool for Studying Online Behavior and Digital Platforms
By: Chiara Farronato, Audrey 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
Farronato, Chiara, Audrey Fradkin, and Chris Karr. "Webmunk: A New Tool for Studying Online Behavior and Digital Platforms." NBER Working Paper Series, No. 32694, July 2024.
- July 2024
- Article
Whether to Apply
By: Katherine B. Coffman, Manuela Collis and Leena Kulkarni
Labor market outcomes depend, in part, upon an individual’s willingness to put herself forward for different opportunities. We use a series of experiments to explore gender differences in willingness to apply for higher return, more challenging work. We find that, in... View Details
Coffman, Katherine B., Manuela Collis, and Leena Kulkarni. "Whether to Apply." Management Science 70, no. 7 (July 2024): 4649–4669.
- June 2024
- Teaching Note
Skills-First Hiring at IBM
By: Boris Groysberg and Sarah Mehta
Teaching note for “Skills-First Hiring at IBM,” case no. 422-013. View Details
Keywords: Competency and Skills; Experience and Expertise; Talent and Talent Management; Human Resources; Human Capital; Employees; Recruitment; Retention; Selection and Staffing; Jobs and Positions; Job Design and Levels; Job Interviews; Society; Societal Protocols; Technology Industry; United States; New York (state, US)
- July 2024
- Article
Mass General Brigham’s Patient-Reported Outcomes Measurement System: A Decade of Learnings
By: Jason B. Liu, Robert S. Kaplan, David W. Bates, Mario O. Edelen, Rachel C. Sisodia and Andrea L. Pusic
This article describes the strategies that leaders at the Mass General Brigham (MGB) health system have used in launching a standardized patient-reported outcome measure (PROM) collection program in 2012, a major step in the value-based transformation of health care.... View Details
Keywords: Patient-reported Outcomes; Value Based Health Care; Health Care and Treatment; Transformation; Outcome or Result; Organizational Change and Adaptation; Performance Improvement; Health Industry
Liu, Jason B., Robert S. Kaplan, David W. Bates, Mario O. Edelen, Rachel C. Sisodia, and Andrea L. Pusic. "Mass General Brigham’s Patient-Reported Outcomes Measurement System: A Decade of Learnings." NEJM Catalyst Innovations in Care Delivery 5, no. 7 (July 2024).
- 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
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- 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
Kellogg, Katherine C., Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon, and Karim R. Lakhani. "Don’t Expect Juniors to Teach Senior Professionals to Use Generative AI: Emerging Technology Risks and Novice AI Risk Mitigation Tactics." Harvard Business School Working Paper, No. 24-074, June 2024.
- 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
Keywords: Factorial Designs; Fisher Randomizations; Rank Estimators; Employer Interventions; Causal Inference; Mathematical Methods; Performance Improvement
DosSantos DiSorbo, Matthew, Iavor I. Bojinov, and Fiammetta Menchetti. "Winner Take All: Exploiting Asymmetry in Factorial Designs." Harvard Business School Working Paper, No. 24-075, June 2024.
- June 2024 (Revised August 2024)
- Case
Hospital for Special Surgery: Returning to a New Normal? (A)
By: Robert S. Huckman, Michael Lingzhi Li and Camille Gregory
Early on the morning of April 27, 2020, Justin Oppenheimer stood outside the entrance to the lobby of the Hospital for Special Surgery (HSS) Pavilion Building with mixed emotions. On one hand, Oppenheimer, HSS’ Enterprise Chief Operating Officer and Chief Strategy... View Details
Keywords: Operations Management; Scheduling; Optimization; COVID-19; Health Care and Treatment; Operations; Customer Focus and Relationships; Disruption; Health Industry; United States
Huckman, Robert S., Michael Lingzhi Li, and Camille Gregory. "Hospital for Special Surgery: Returning to a New Normal? (A)." Harvard Business School Case 624-092, June 2024. (Revised August 2024.)