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- 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
- 2024
- Working Paper
The Effect of a System for Sharing Best Practices Within Pre-existing Peer Networks
By: Shelley Xin Li and Tatiana Sandino
Peer networks, such as enterprise social networks (ESNs), can facilitate knowledge transfer across employees. However, such systems can also lead to information overload or difficulty in finding useful information. We examine data from a natural field experiment where... View Details
- September 2024
- Article
A Potential Pitfall of Passion: Passion Is Associated with Performance Overconfidence
By: Erica R. Bailey, Kai Krautter, Wen Wu, Adam D. Galinsky and Jon M. Jachimowicz
Having passion is almost universally lauded. People strive to follow their passion at work, and organizations increasingly seek out passionate employees. Supporting the benefits of passion, prior research finds a robust relationship between passion and higher levels of... View Details
- 2024
- Working Paper
The Wade Test: Generative AI and CEO Communication
By: Prithwiraj Choudhury, Bart S. Vanneste and Amirhossein Zohrehvand
Can generative artificial intelligence (AI) transform the role of the CEO by effectively automating CEO
communication? This study investigates whether AI can mimic a human CEO and whether employees’
perception of the communication’s source matter. In a field... 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
- 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
- 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
- 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
- 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
- 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
Greenlighting Innovative Projects: How Evaluation Format Shapes the Perceived Feasibility of Novel Ideas
By: Jacqueline N. Lane, Tianxi Cai, Michael Menietti, Griffin Weber and Eva C. Guinan
Evaluation of novel projects is essential for scientific and technological advancement. However,
evaluator bias toward a project’s potential can obscure its limitations. This study investigates
evaluation formats by contrasting combined assessments of novelty and... View Details
- 2024
- Working Paper
Precautionary Debt Capacity
By: Deniz Aydin and Olivia S. Kim
Firms with ample financial slack are unconstrained... or are they? In a field experiment
that randomly expands debt capacity on business credit lines, treated small-and-medium
enterprises (SMEs) draw down 35 cents on the dollar of expanded debt capacity in... View Details
- 2024
- Working Paper
Platform Information Provision and Consumer Search: A Field Experiment
By: Lu Fang, Yanyou Chen, Chiara Farronato, Zhe Yuan and Yitong Wang
Despite substantial efforts to help consumers search in more intuitive ways, text search remains the predominant tool for product discovery online. In this paper, we explore the effects of visual and textual cues for search refinement on consumer search and purchasing... View Details
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
- December 2023
- Article
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work
By: Mijeong Kwon, Julia Lee Cunningham and Jon M. Jachimowicz
Intrinsic motivation has received widespread attention as a predictor of positive work outcomes, including employees’ prosocial behavior. In the current research, we offer a more nuanced view by proposing that intrinsic motivation does not uniformly increase prosocial... View Details
- 2023
- Chapter
Malleability Interventions in Intergroup Relations
By: Smadar Cohen-Chen, Amit Goldenberg, James J. Gross and Eran Halperin
One important characteristic of intergroup relations and conflicts is the fact that toxic or violent intergroup relations are often associated with fixed and stable perceptions of various entities, including the ingroup (stable and positive), the outgroup (stable and... View Details
- December 2023
- Article
Save More Today or Tomorrow: The Role of Urgency in Precommitment Design
By: Joseph Reiff, Hengchen Dai, John Beshears, Katherine L. Milkman and Shlomo Benartzi
To encourage farsighted behaviors, past research suggests that marketers may be wise to invite consumers to pre-commit to adopt them “later.” However, the authors propose that people will draw different inferences from different types of pre-commitment offers, and that... View Details
- 2024
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details