My major research interests lie in building theoretical models about the use of technology (e.g., artificial intelligence) in online platforms to study its impact on customers and firms. In my research, I focus on topics such as content moderation, influencer marketing, gig economy, etc., and try to find a novel aspect to illustrate some unexpected and understudied impact of adopting technology. 

Publications/Accepted papers:

Abstract: Artificial intelligence (AI) has been increasingly integrated into the process of matching between workers and employers requesting job tasks on a gig-economy platform. Unlike the conventional wisdom that adopting AI in the matching process always benefits the platform by assigning better-matched jobs (employers) to workers, we discover unintended but possible revenue-decreasing consequences for the AI-adopting platform. We build a stylized game-theoretical model that considers gig workers' strategic participation behavior. We find that while the matching enhancement effect of AI can increase the platform's revenue by improving matching quality, AI-assigned jobs can also reveal information about the uncertain labor demand to workers and thus unfavorably change workers' participation decisions, resulting in revenue loss for the platform. We extend our model to the cases where (1) the share of revenue between workers and platform is endogenous and (2) the workers compete for the job tasks, and find consistent results. Furthermore, we examine two approaches to mitigate the potential negative effect of AI-enabled matching for the platform and find that under certain conditions, the AI-adopting platform can be better off by revealing the labor demand or competition information directly to workers. Our results shed light on both the intended positive and unintended negative roles of utilizing AI to facilitate matching, and highlight the importance of thoughtful development, management, and application of AI in the gig economy.

Marketing Science, 2022

Abstract: This paper develops a theoretical model to study the economic incentives for a social media platform to moderate user-generated content. We show that a self-interested platform can use content moderation as an effective marketing tool to expand its installed user base, to increase the utility of its users, and to achieve its positioning as a moderate or extreme content platform. For the purpose of maximizing its own profit, a platform will balance pruning some extreme content, thus losing some users, with gaining new users because of a more moderate content on the platform. This balancing act will play out differently depending on whether users will have to pay to join (subscription vs advertising revenue models) and on whether the technology for content moderation is perfect.

We show that when conducting content moderation optimally, a platform under advertising is more likely to moderate its content than one under subscription, but does it less aggressively compared to the latter when it does. This is because a platform under advertising is more concerned about expanding its user base, while a platform under subscription is also concerned with users' willingness-to-pay. We also show a platform's optimal content moderation strategy depends on its technical sophistication. Because of imperfect technology, a platform may optimally throw away the moderate content more than the extreme content. Therefore, one cannot judge how extreme a platform is by just looking at its content moderation strategy. Furthermore, we show that a platform under advertising does not necessarily benefit from a better technology for content moderation, but one under subscription does, as the latter can always internalize the benefits of a better technology. This means that platforms under different revenue models can have different incentives to improve their content moderation technology. Finally, we draw managerial and policy implications from our insights.

Other working papers:

Consumer Preferences and Firm Technology Choice (with Pinar Yildirim and John Zhang)

Abstract: Advances in intelligent technologies change the way consumers search and shop for products. Emerging is the trend of home-shopping devices such as Amazon's Alexa and Google Home, which allow consumers to search or order products using voice commands. We study the impact of such artificial intelligence (AI) enabled devices on a brand's channel strategy and its price discrimination across these channels. After making a theoretical breakdown of the functionalities of the AI-enabled shopping devices into (1) adding convenience in ordering procedure (“OC”) and (2) providing support in purchase decision making (“DS”), we document via a set of experiments that consumers who have strong (weak) shopping preferences are less-inclined to shop through AI-enabled devices with the functionality of DS (OC) compared to their existing shopping heuristics. The hesitation of the group to adopt AI-enabled shopping devices makes it efficient for a brand operating in a competitive environment to price discriminate across distribution channels. In the second part of the paper, we build an analytical model and derive the equilibrium distribution and pricing strategies for competing brands conditional on the heterogeneity of consumers with respect to their willingness to adopt AI-enabled devices. We also analyze the welfare impact of the introduction of AI technology as a new possible distribution channel.

When Does a Brand-Influencer Matching AI Backfire? (with Jessie Liu)

Abstract: We consider a social media platform that offers a matching service to match marketers with influencers through Artificial Intelligence (AI) technology. We find that, even if the implementation cost is negligible, it is not always in a platform’s best interest to adopt such AI technology or to perfect its AI accuracy. The results arise from two countervailing effects on the participation incentives of influencers, which in turn affects the platform's profit. On the one hand, influencer marketing generates higher sales from a better influencer-marketer match, which benefits both influencers and the platform as they share a commission proportional to sales. On the other hand, if sales is high via an influencer's recommendation channel, more users may stop following the influencer if they bought a low-quality product. The former "sales effect" induces the influencer to participate in a marketing campaign, whereas the latter "quality concern effect" deters one from doing so. Furthermore, the proportional size of loyal followers moderates the trade-off between these two effects. We derive conditions under which adopting such AI technology is profitable for a platform. We also extend our baseline model to study when and how a platform should integrate its AI strategy with its communication strategy for product quality assurance.

On the Role of Trailer as a Marketing Research Tool: The Economic Value of the Comments it Generates (with Josh Eliashberg)

Abstract: Trailers are commonly employed in the pre-launch campaigns of new products as an advertising tool to generate awareness and interest among the potential audience. In this paper, we argue that such trailers, whose costs range and are rising, should also be considered as a marketing research tool having additional economic value. The incremental value is driven by the audience comments data that the trailer of a new product generates. The data, once analyzed, can signal the post-release electronic word-of-mouth (eWOM) that will prevail and can affect the new product success, but is uncertain to the product manager a priori. That is, trailers, if released early, have the potential to assist in making more informed pre-launch marketing decisions (specifically, advertising spending), and thus have additional economic values in guiding risk-mitigation strategies. This incremental value should thus be considered in the cost/benefit analysis of the trailer production. This paper quantifies such value by formulating various economic value of information (information value) metrics based on a Bayesian decision-theoretic framework, which are applicable to many product categories. We investigate the metrics both analytically and empirically, employing a dataset of 363 movies released in 2014-2018. The information value is analyzed with respect to the two key dimensions of the uncertain post-release eWOM: volume and valence. We carry out various sensitivity analyses, comparing the magnitudes of information value under different scenarios. Based on our dataset, we demonstrate that the volume of comments generated by early trailers is more critical than its valence and consequently has a higher incremental economic information value, which may be worth $240,000. We also find that the trailer comments of different genres have different economic values, with action movies having the highest.