Research

Generative Artificial Intelligence (AI) technologies have emerged as a transformative force in the content creator economy. This paper examines how the AI stances signaled by specific platform policy decisions shape creator behavior on visual arts platforms. We leverage two independent natural experiments on leading Chinese platforms: Lofter’s launch of an AI image generator, which signaled a pro-AI stance, and Graffiti Kingdom’s prohibition of AI-generated artwork, which signaled an anti-AI stance. Our analysis shows that creators decreased their activity on Lofter following the AI generator launch, while activity increased on Graffiti Kingdom after the AI prohibition.

Multiple lines of evidence show these effects stem from policy-communicated stance signaling: creators respond to what the focal AI policy communicates about AI’s role, the platform’s commitment to human creators, and the future competitive environment for human-created work, rather than merely to specific tool features or enforcement actions. Heterogeneity analysis reveals that higher-popularity, multi-homing, and AI-averse creators show larger activity reductions on Lofter. Through analysis of creator posts, we identify three primary concerns driving resistance: replacement risk, perceived low quality, and copyright infringement.

To our knowledge, this is the first paper to causally identify how the AI stance signaled by a platform policy decision affects creator behavior. Our findings have implications for how platforms communicate AI-related policy decisions, and for policymakers on fostering a constructive relationship between AI and human creators.

Platforms often expand by organizing a common intermediation function into horizontally differentiated communities, such as topics, categories, or domains that users value differently rather than rank by a common quality metric. This strategy resembles product-line design: added communities may attract new participation but also cannibalize incumbents. We show that two-sidedness fundamentally changes this trade-off. When users on at least one side allocate scarce participation resources across communities and the outside option, an initial reallocation changes participation on the opposite side of the affected communities, and that induced response feeds back into the side where the movement began. We call this mechanism the cross-network reallocation effect. The novel implication is that horizontal differentiation on a two-sided platform is not simply a product-line problem with an additional user side: cross-network feedback can change not only the magnitude but also the sign of platform-wide outcomes.

We develop the theory and estimate a dynamic structural model using Stack Exchange, focusing on Stack Overflow and Super User around ChatGPT's release. In the model, askers choose question volume and difficulty composition, while answerers allocate limited weekly capacity across communities and learn about changes in Stack Overflow's audience-feedback environment from realized feedback. Counterfactual simulations show three portfolio-level implications. First, added communities increase aggregate activity when they have strong asker-side demand, answerer-side appeal, and cross-network feedback, but weak additions can reduce aggregate activity even when direct operating costs are absent. Second, after localized shocks, unaffected communities can do more than cushion losses: when they retain and amplify enough displaced activity, they can reverse the shock's aggregate effect. Third, community-targeted policies can raise activity locally while reducing platform-wide activity by drawing effort away from untreated communities that already support substantial activity. These findings introduce a new mechanism linking horizontal differentiation, internal reallocation, and cross-network feedback, and imply that platform expansion, resilience, and community-level governance should be evaluated as portfolio decisions rather than isolated community outcomes.

Opaque products, which conceal key attributes until after purchase, have been widely adopted across various industries. Yet, empirical evidence on how such opacity influences consumer demand remains scarce. This study introduces a reference price framework to estimate demand for opaque products, focusing on blind boxes in the online retailing context. Using a unique dataset from a leading e-commerce platform, we estimate heterogeneous consumer preferences through an aggregate random coefficient logit model that incorporates gains and losses relative to reference prices.

We uncover a novel behavioral pattern of reversed loss aversion: consumers are more sensitive to gains than to losses when purchasing opaque products. Our findings provide the first empirical validation of this pattern in consumer decision-making using field data and demonstrate that this behavioral tendency makes the demand for opaque products fundamentally different from that of traditional products. The policy simulations show that ignoring reversed loss aversion would lead to misestimated own-price elasticity, resulting in suboptimal pricing strategies and substantial profit losses.

Price gouging regulation (PGR) helps ensure that essential goods, e.g., medical supplies or grocery staples, remain affordable and accessible. During the COVID-19 pandemic, PGR was implemented due to widespread reports of price-gouging on such critical items. Extant research offers few insights for policymakers and marketers into how PGR influences consumer spending and sales during crises.

Using Generalized Synthetic Control, we examine the overall and relative effectiveness of different types of PGR legislation, as well as the moderating effects of consumer mobility and the enactment of strict price caps. We find that PGR is generally highly effective, especially when it is not overly comprehensive and for products that are “essential” (e.g., paper towels) but not obviously related to preventing contagion (e.g., hand sanitizer). A nonparametric mediation analysis suggests that pricing alone does not fully explain PGR’s overall effectiveness, accounting for roughly 27–55%, depending on product class; moreover, it is strongly moderated by both specific price caps and consumer mobility.

We further add a mechanism-focused analysis using Numerator product-category data. To determine which categories are legally essential and theoretically informative, we use eight LLMs to code 1,904 product categories against pandemic price-gouging legal materials, aggregate the ratings with a principal-factor essentiality score, and then isolate three pre-specified classes of mechanism-eligible household essentials. In these categories, hierarchical bootstrap models show modestly lower prices, sharply higher online sales, lower offline sales, and a large shift from offline to online purchasing relative to inessential categories. Our results have direct implications for policymakers deciding whether to enact PGR as well as setting specific design features depending on locality.

Generative AI Search is an emerging platform-owned search-interface design that places AI-generated content above conventional ranked search results. For eligible queries, it provides users with a synthesized natural-language answer before they inspect or click the underlying sources, shifting the search experience from a retrieval-first, search-and-click model toward an answer-first format. On content creation platforms, where creator-supplied content is itself the informational product users seek, this design may affect both how often users search and how they subsequently consume the underlying content. Using a large-scale randomized field experiment involving 100,000 users on a leading Chinese content creation platform, we examine these two behavioral margins jointly. Treatment-group users received AI-generated content above the conventional ranked user-generated content (UGC) list when a submitted query satisfied the platform’s triggering rule for objective or sufficiently standardized experience-based answers, whereas control-group users always saw only the conventional ranked list.

Our results reveal a scale–intensity trade-off in content consumption. Generative AI Search expands the extensive margin of information seeking: users search more frequently, on more days, and across a wider range of content categories and semantic topics. This expansion spans all six substantive search intents, including those less aligned with the platform’s triggering rule. On the intensive margin, however, users who search consume less creator-supplied content per session: they browse and click less UGC. Despite this within-session contraction, aggregate UGC browsing and clicking increase, and more search sessions initiate downstream content consumption, which refers to subsequent within-platform content-page clicks after a user opens an initial piece of UGC from the search-results page. An activity–intensity decomposition shows that the expansion in search activity more than offsets the decline in pooled content consumption per session. Thus, Generative AI Search can reduce the consumption of creator-supplied content within individual sessions while increasing its aggregate consumption, demonstrating why lower per-session consumption need not imply lower overall demand for creator-supplied content.

“Host Residency Enforcement” policies have been widely adopted by cities in the US, mandating hosts of short-term rentals to reside in those properties to varying extents. For example, New York City’s “Physical Presence” policy requires hosts to remain present with guests during short-term rentals, while Los Angeles’s “Primary Residence” policy mandates hosts’ residence for at least six months annually. Similar regulations have been widely adopted across other cities. Although these policies aim to address housing issues, they may unintentionally amplify racial disparities due to existing racial discrimination on Airbnb. This paper examines whether and how “Host Residency Enforcement” policies exacerbate these racial disparities on the platform.

Our findings indicate that NYC’s “Physical Presence” policy increased the daily revenue gap between minority hosts (i.e., Hispanic and Black hosts) and White hosts by approximately $12. Similarly, LA’s “Primary Residence” policy widened the revenue disparity specifically between Black and White hosts by about $8, while having no significant effect on the gap between Hispanic and White hosts. Additionally, on the supply side, both policies increased the gap in the likelihood of listings remaining active between minority and White hosts; however, in LA, this effect was statistically significant only among Hispanic hosts. Moderating analysis further indicates that neighborhoods with higher income, higher proportions of White residents, and more native-born residents (i.e., neighborhoods with higher socioeconomic status) mitigate these exacerbated racial disparities. Additionally, listings signaling higher quality through their overall Airbnb ratings experienced reduced disparities. This finding suggests that statistical discrimination, primarily driven by information asymmetry, serves as a key underlying mechanism. Lastly, minority female hosts were disproportionately negatively affected compared to minority male hosts, highlighting intersectional discrimination. Although our analysis specifically focuses on NYC and LA, our findings can be generalized to other cities that have implemented the same or similar policies, as NYC and LA represent opposite ends of the regulatory stringency spectrum.

Our study is the first to demonstrate that the widely adopted “Host Residency Enforcement” policies can unintentionally amplify racial disparities on Airbnb, illustrating an important but understudied interaction between public policy and existing discrimination on online platforms. Drawing from our findings, we offer several practical recommendations for policymakers and online platforms.