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Quality Disclosures and Disappointment: Evidence from the Academy Nominations
Michelangelo Rossi & Felix Schleef
Management Science, forthcoming
Abstract:
This study examines the unintended consequences of quality disclosures, focusing on how Academy Award nominations impact consumer satisfaction in the movie industry. Awards and certifications typically signal high quality and increase consumer expectations. Yet, if the experience falls short of the expectation, they may also lead to disappointment. Using a novel data set from MovieLens, we analyze user ratings for movies surrounding Academy Award nominations from 1995 to 2019. We first implement a difference-in-differences strategy comparing nominated and non-nominated films and then introduce a novel recommendation-based matching approach that leverages vector representations of user preferences trained prior to the nominations. Our analysis removes taste-based selection and isolates changes in user experience: users who rate a movie after its nomination assign significantly lower ratings than similar users who rated the same film earlier. This effect accounts for more than 7% of the prenomination rating gap between nominated and non-nominated films and is most pronounced among less experienced users. Our findings are validated with data from IMDb, where the effect is even more pronounced, likely reflecting differences in the composition of the user base across platforms. Additional textual analysis of user-generated content on both platforms provides further evidence that the postnomination decline in ratings is driven by disappointment, rather than disinterest, deteriorating viewing conditions, or snob effects associated with mainstream popularity.
The Moralization of Artificial Intelligence
Victoria Oldemburgo de Mello et al.
University of Toronto Working Paper, March 2026
Abstract:
Resistance to artificial intelligence (AI) is widespread and persists even when known psychological barriers are removed. What explains this persistent aversion? Across four studies, we investigate whether moral reactions to AI -- rooted in deeply held beliefs about right and wrong -- help explain resistance beyond pragmatic concerns. In Study 1, we analyzed all news headlines in a major US media corpus (COCA, 2018-2024) and found that AI is moralized at levels comparable to GMOs and vaccines -- technologies whose moral opposition has received considerable attention -- and that surges in moralization followed the launch of major AI applications such as ChatGPT and DALL-E. In Studies 2a, 2b, and 3, representative samples of Americans reported their attitudes toward several AI applications and other technologies. Although few participants opposed AI outright, most opponents indicated their views would remain unchanged even if AI proved beneficial -- suggesting moral rather than pragmatic roots. Structural equation models revealed that moralization of AI was best captured by a single latent factor, indicating a generalized moral sentiment rather than domain-specific risk-benefit appraisals. Qualitative analyses further uncovered the most common justifications people invoke and how opponents and supporters differ in their reasoning. In Study 4, participants from Studies 2b and 3 completed a subsequent behavioral grading task; moralization scores measured in the earlier surveys predicted greater reluctance to use AI even when doing so would benefit participants (a one standard deviation increase in moralization corresponded to 42% decrease in AI usage). Together, these findings demonstrate that resistance to AI is partly moral in nature, suggesting that reaping the potential benefits of AI tools may require addressing moral concerns rather than relying solely on pragmatic arguments.
Blissful (A)Ignorance: Despite the widespread adoption of AI in communication, people do not suspect AI use in realistic contexts
Jiaqi Zhu & Andras Molnar
Computers in Human Behavior, July 2026
Abstract:
The growing use of AI in written communication has raised important questions about how people perceive and evaluate others based on their written messages. Past studies have documented a substantial "AI penalty": communicators are judged more harshly when their use of AI is disclosed or strongly suspected, compared to when their messages are confirmed as human-written. However, under more realistic conditions, audiences may be uncertain, or even completely unaware, of communicators' potential use of AI. We conducted two online experiments () to systematically examine how both explicit disclosure and uncertainty regarding AI use affect social impressions in realistic communication contexts (e.g., email; social media; texting). In line with previous research, we found that explicit disclosure of AI use resulted in more negative social impressions, compared to confirmed human authorship. However, this AI penalty disappeared or was significantly reduced under more realistic conditions. When potential AI use was not highlighted, recipients exhibited no skepticism and formed impressions that were identical to those of confirmed human writers. Even when potential AI use was highlighted -- but remained uncertain -- recipients' impressions were much closer to those of human writers than to those of AI-generated text. Our findings reveal a striking gap between the rapid adoption of AI and social awareness: despite the widespread use of AI in communication, people remain largely unaware of others' potential AI use, unless this is explicitly disclosed. We discuss how these findings inform ongoing debates about authenticity, trust, and fairness in an era of AI-mediated communication.
The OnlyFans Economy: Intellectual Property's Pivot from Scarcity to Authenticity
Vincent Joralemon
Virginia Law Review, forthcoming
Abstract:
Generative AI is destabilizing the foundational assumption of intellectual property law: that creation is difficult, expensive, and requires legal inducement. When machines produce text, images, and code at near- zero marginal cost, the utilitarian justification for copyright and patent protection begins to collapse. This Essay argues that what emerges in its place is a regime organized not around the scarcity of creation, but around the scarcity of verification -- a shift from an incentive paradigm to a source identification paradigm dominated by trademark, rights of publicity, and platform-controlled authentication infrastructure. OnlyFans provides a revealing case study. Despite AI-generated pornography flooding the market, the platform paid over $5.8 billion to human creators in 2024. Consumers pay not for content, which AI can approximate, but for provenance -- the verified knowledge that they are interacting with a specific, authenticated person. This Essay identifies a "triple-lock" structure underlying this economy: verified identity draws consumers in, proprietary infrastructure ensures access requires staying inside the walled garden, and aggressive copyright enforcement destroys unauthorized copies that would otherwise undermine authenticity's value. Applying a Law and Political Economy lens, the Essay argues that this emerging regime systematically advantages those who already possess recognized brands, legal departments, and capital for proprietary infrastructure -- while offering little to individual creators who lack pre-existing fame. It concludes by proposing that the Library of Congress serve as a public "digital notary," preventing the infrastructure of authenticity from becoming a private toll road.
Is there a premium for legacy artists? The death effect in exhibition shows and auction transactions
Matthias Florian Sahli & Alexander Cuntz
Journal of Cultural Economics, March 2026, Pages 1-34
Abstract:
This article examines how an artist's death influences exhibition activity and auction prices, providing new insights into artistic legacy and postmortem market dynamics in the visual arts. Using a panel dataset of exhibition histories and auction transactions for a sample of prominent artists born after the twentieth century, we employ regression discontinuity and event-study designs to identify causal effects. We find that an artist's death leads to a significant temporary decline in exhibitions, which we attribute to increased transaction frictions, including search costs and legacy management challenges. In contrast, a significant postmortem price premium emerges in auction markets, consistent with idea of scarcity and finite supply. The magnitude of this premium varies substantially by legacy artist characteristics: young, low-reputation artists experience the highest price increases, suggesting buyers anticipate future appreciation in reputation. These findings contribute to the empirical literature on auction pricing, reputation effects, and transaction costs in cultural markets, offering broader implications for asset markets where supply constraints and information asymmetries shape long-term valuation.