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:

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.

Abstract: Recent years have witnessed significant advancements in matching technologies to improve the matching between workers and employers requesting job tasks on a gig-economy platform. While the conventional wisdom suggests that technologies with higher matching quality benefits the platform by assigning better-matched jobs to workers, we discover a possible unintended revenue-decreasing effect. Our stylized game-theoretic model suggests that while a technology’s matching enhancement effect can increase the platform’s revenue, the jobs assigned by the better matching technology can also unintentionally reveal more information about the uncertain labor demand to workers, especially when the demand is low, and thus unfavorably change workers’ participation decisions, resulting in a revenue loss for the platform. We extend our model to the cases where (1) the share of revenue between workers and platform is endogenous, (2) the matching quality can be improved continuously, (3) the opportunity cost of workers is affected by competition between platforms, and (4) workers compete for job tasks. We find consistent results with additional insights including the optimal matching quality the platform should pursue. Furthermore, we examine two approaches to mitigate the potential negative effect of employing an advanced matching technology for the platform and find that under certain conditions, the 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 effects of improvements in matching quality and highlight the importance of thoughtful development, management, and application of matching technologies in the gig economy.

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

International Journal of Research in Marketing, 2023

Abstract: Advances in technology 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. We investigate how consumer brand and technology preferences may interact with the functionalities of technology-enabled shopping (TES) devices to determine the channel structure and market competition.

In specific, we break the functionalities of the TES devices into two: (1) the shopping support functionality (SSF), and (2) the ordering convenience functionality (OCF). Via a series of experiments, we document that stronger brand preferences are negatively correlated with the willingness to use a TES device that offers SSF. However, there is no association with brand preferences and desire to use a TES device when it offers OCF. 

We build an analytical model integrating the findings from these experiments, and then derive the equilibrium channel and pricing strategies for two competing retailers. Our findings show that the functionality of TES devices results in vastly different distribution and pricing strategies in retail markets. In particular, consumers' heterogeneous valuation of the SSF results in a monopolistic adoption of TES devices by the retailers in equilibrium, and generates Pareto improvements for both. In contrast, when the TES devices offer OCF, in equilibrium, retailers adopt TES channels competitively, resulting in a prisoner's outcome. 

In the extensions,  studying a third-party technology developer's decision to invest in OCF and SSF technologies, we show that the contrast between the channel strategies under the OCF and the SSF also impact the incentives to develop TES. We show that in some cases, in an effort to mitigate downstream retailer competition, the provider may prefer not to offer the best possible OCF technology to consumers. These findings shed light on the future adoption and the functionalities of shopping technologies offered by retailers.

Other working papers:

Asymmetric Impact of AI Matching on Influencer Marketing: Implications for Platform Revenue (with Jessie Liu)

Abstract: This paper explores the impact of using artificial intelligence (AI) to connect marketers with social media influencers. We develop a theoretical model to examine how AI accuracy affects the competition between influencers and the profitability of a social media platform. Our findings show that improving AI accuracy may not always benefit the platform, especially for platforms with intermediate follower density. Two opposing effects of AI improvement affect the prices of influencer marketing campaigns: AI enhances the matching between influencers and marketers, but also intensifies competition between different types of influencers. The overall effect on prices can be negative for some influencers due to the asymmetric nature of such matching technology: the matching outcome for influencers with a narrower audience ("niche" influencers) is more sensitive to AI accuracy than that for those with a broader audience ("general" influencers). As a result, more niche influencers begin to participate in marketing campaigns when AI accuracy improves, which reduces the prices offered by sufficiently general influencers and may lead to a decline in platform revenue. Additionally, we found that adjusting commission rates in response to AI improvements could help mitigate the negative impact, although it may not eliminate it entirely. Our findings offer valuable insights for social media platforms seeking to remain competitive in the influencer marketing landscape.

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.