We use monthly surveys, each based on the same five primary questions, to construct the monthly Consumer Crypto Confidence Index. We also collect demographic data, political leanings, etc., on each survey subject for each monthly survey. So far, we have collected two-years’ worth of the survey data, and also the corresponding bitcoin price data. The primary objective here is to track how consumer confidence in crypto currency changes over time.…Read More
High-income regions like North America and Europe currently generate most of the world’s emissions. However, most high-income countries have in recent years passed national or sub-national legislation to lower emissions, such as carbon taxes, emissions trading schemes, and clean subsidies. Add to this that Africa’s population is expected to triple, and the result is that by 2050 Africa is expected to emit twice as much CO2 per year as North America or Europe.…Read More
Recommendation systems have become integral to our daily lives, with apps suggesting what we might want to watch, eat, read, or invest our time and energy in. The average American consumer uses daily at least 3 apps involving some sort of recommendation system (Medium, 2021) and spend around $200 monthly on subscription systems (Yahoo Finance, 2024), 60% of YouTube searches come from recommendations and 40% of the apps present on Google Play offer in their services a recommendation system (GoogleDevelopers, 2024). However, 75% of users complain that these apps do not actually reflect their taste (The New York Post, 2024), rather leading them to be overwhelmed by the sheer volume of products offered (CNET, 2024). In particular, in online media markets, streaming platforms like Netflix rely on recommendation systems for 75% of its revenue (Medium, 2019) while companies like Amazon attribute approximately 35% of their e-commerce revenue to product recommendations (Quartz, 2018) and yet viewers lament a complete inadequacy of their algorithms (HBR, 2024). How is this misalignment becoming possible and how do we fix it? In our research, we hypothesize that integrating the current algorithms with consumers’ time and energy availability estimations might improve recommendation systems, increasing both consumers’ satisfaction levels and companies’ revenues.
Specifically, we propose a model where effort and time estimation can improve both the choice of content users select to watch – and their satisfaction with it – and the recommendation systems output. By factoring in the effort and time a user is willing to invest, the recommendation system can tailor content that aligns more closely with the user’s current state, thereby improving both the selection process and overall satisfaction.…Read More
Companies often encourage their customers to share their progress toward personal goals, such as their fitness journey, on social media. In this research, we investigate how doing so impacts motivation. While documenting goal pursuit online may increase motivation through immediate social rewards (likes, comments), accountability, and social support, it could also have no effect of even backfire—especially if people focus on social media engagement rather than the underlying goal, or become discouraged by lower-than-expected feedback. We test these possibilities through a preregistered field experiment (N = 500) in which participants are assigned to either document their goal progress by posting on Instagram or by completing a private survey. Over a three-month period, we measure their gym attendance and social media engagement. The findings of this paper would provide theoretical insight into how social media interacts with goal pursuit and potentially offer practical implications for designing scalable, low-cost interventions to promote goal achievement.…Read More
Private equity has become an increasingly active player in technology acquisitions in recent years, yet most prior scholarship has focused on the effects of corporate acquirer ownership on performance and innovation outcomes. As a result, research provides little guidance on how firms should choose between the two acquirer types. To remedy this gap, I construct a panel data set of acquisitions in the chemical, biopharmaceutical, and medical device industries between 1990 and 2019, tracked yearly through 2022. Then, I examine how private equity and corporate acquirers differentially affect product innovation at acquired technology targets using USPTO trademark, FDA orange book, and hand-collected new product introduction data. Our results illuminate the opportunities and tradeoffs facing managers at technology companies in choosing between private equity and corporate acquirers.…Read More
As language model providers develop watermarking techniques to identify AI-generated content, important questions arise about their potential impact on non-native English speakers in academic settings. This study examines how proposed watermarking systems might create “digital accents”—systematic biases that could flag legitimate writing assistance used by non-native English speakers as potential AI-generated content. Through analysis of 1,500 TOEFL essays and three distinct levels of AI assistance, we demonstrate how current watermarking techniques could disproportionately impact international students who use AI tools for language learning and writing improvement. We propose a novel detection framework that reduces potential false positive rates by integrating conformal outlier detection techniques in statistics while maintaining detection accura…Read More
Thinking about a future positive experience can be enjoyable and exciting. However, we suggest that thinking too much about a future positive experience can backfire. In particular, we investigate whether people would become less excited initially when they are reminded more (vs. less often) about a future positive experience. We suggest that people can adapt to the thought of a future experience and thus would become less excited about the experience. We examine how people’s anticipatory enjoyment changes over time and how many (vs. few) reminders affect this trajectory. Further, we will also explore the downstream consequences of many (vs. few) reminders, such as a reduction in enjoyment of the planned activity and a greater likelihood of changing the planned activity.…Read More
From the time remaining for an Uber’s arrival to the number of likes on an Instagram post, new technologies have made it easier to check the status of desired outcomes than ever before. Smartphones and other devices enable consumers to receive updates, such as a delivery driver’s status, in real-time. But is such frequent checking always beneficial? This research explores a potential downside to checking the status of desired outcomes more…Read More
This project investigates the welfare impact of bundling between platforms affects in the video streaming market. Platform bundling has become increasingly common in recent years. For example, comcast offers an ad-supported bundle of Netflix, Apple TV, and Peacock for just $10 per month. Similarly, a bundle of Hulu, Disney Plus, and Max allows consumers to subscribe all three with a nearly 40% discount. However, the effects of such bundling on consumers remain unclear. Existing literature shows that when competing firms offer mixed bundles of their own products, bundling enables more efficient price discrimination, which harms consumers; but also intensifies competition, which benefits consumers. The streaming market presents a unique and intriguing case because these bundles often include platforms with distinct ownership. This separate ownership of bundling platforms creates potential inefficiencies, as platforms may “freeride” own their bundling partners’ content investments while reducing their own. In this project, I will develop a structural model and apply data-driven methods to quantify the impact of mixed bundling between independently owned platforms on competition and consumer welfare.…Read More
This study explores how firms’ search strategies shape innovation outcomes in the context of emerging general-purpose technologies (GPTs), with a focus on artificial intelligence (AI). GPTs, defined by their broad applicability and undefined market needs, challenge traditional search theories by requiring firms to balance advancing technological capabilities (supply-side innovation) with identifying practical use cases (demand-side innovation).…Read More
As organizations seek evidence-based approaches to enhance leadership effectiveness and employee creativity, psychedelic research presents a promising opportunity to understand perspective-changing interventions in professional contexts. The convergence of long-standing scientific interest in psychedelics, growing demand for evidence-based information on the impact of these medicines on leaders, and mounting empirical evidence…Read More
Ensemble models, a class of machine learning algorithms that combine the predictions of multiple algorithms to form more accurate predictions, are widely used in marketing applications. This research explores ways to enhance the adoption and perceived accuracy of these models. Thirteen experimental studies (ten in the main paper and three in the Web Appendix) collectively demonstrate that consumers have an intuitive grasp of the “wisdom of the crowd,” and they perceive machine ensembles as more accurate than single algorithms. Framing algorithms as a collection of models rather than a single algorithm boosts perc…Read More
This research proposes to study how presence on and removal from a digital platform that facilitates virality affects the performance and production of creative goods. We hope to study this question utilizing data on musical artists’ presence and performance across channels such as TikTok, Spotify, and YouTube…Read More
The pharmaceutical market has experienced a massive wave of vertical integration between pharmacy benefit managers (PBMs) and health insurers in recent years. Using a unique dataset on insurer-PBM contracts, we document increasing vertical integration in Medicare Part D–vertically integrated insurers’ market share increased from about 30% to 80% between 2010 and 2018.…Read More
itional two-sided platforms (e.g., Amazon, Uber) rely primarily on commission contracts to generate revenues and fuel growth, whereas their decentralized counterparts (e.g., Uniswap, Filecoin) often forego these in favor of token retention. What economics underpin this choice?…Read More
Listening to the other side is essential for communication and conflict resolution. However, even when a listener listens well, the speaker may still exclaim, “You are not listening to me!” We reason this occurs because speakers think someone who disagrees with them simply has not listened.…Read More
Inter-ethnic tensions have been well documented as one of the main inhibiting factors behind the slow economic progress of developing countries. For example, government leaders have shown to prioritize their ethnic supporter base in providing public goods and services (Franck and Rainer 2012; Kramon and Posner 2016), often resulting in suboptimal distributions. However, relatively little is known about the role of ethnicity in taxation. Specifically, how ethnicity influences the distribution of tax burden and what the costs of such ethnic targeting are, if existent, remain largely unknown. My proposed project will study the role of ethnic targeting in the building of the property tax system in Nigeria.…Read More
Psychological Science, a leading journal in the field of psychology, recently published a paper by Hauenstein et al. (2024) in which the authors reanalyzed data from the Good Judgment Project (e.g., Mellers et al., 2014) and argued that IARPA’s tournaments permitted method variance; forecasters could select their own questions and make their own forecasts whenever they wished while the forecast window was open.…Read More
Prof. Christian Terwiesch, Mack Institute co-director, and Lennart Meincke, Mack Institute predoctoral fellow, have co-authored a new working paper on chatbot design and workflow integration, the latest in Prof. Terwiesch's series of papers on practical applications of Generative AI. The paper devises a framework for chatbot implementation based on varying…Read More
The marginal cost of electricity fluctuates hour-by-hour, yet retail customers typically face flat prices. Using data from all seven US wholesale markets and a new method to evaluate alternative rates set in advance that accounts for equilibrium price effects, we estimate efficiency gains from time-varying price schedules that better align price with cost. We have three main results. First, time-of-use rates and critical-peak pricing, the two most common time-varying rate plans, each correct about 10% of mispricing. Second, complex rate structures based on historical prices often backfire. Third, real-time pricing with price ceilings can capture most potential efficiency gains while limiting customer risk.…Read More
