Research

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.

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.

Abstract: This paper explores the impact of using advanced technology such as artificial intelligence (AI) to match marketers with social media influencers. We develop a theoretical model to examine how matching accuracy affects the competition between influencers and the profitability of a social media platform. Our findings show that improving matching accuracy may not always benefit the platform, especially for platforms with intermediate follower density. Two opposing effects of technology improvement affect the prices of influencer marketing campaigns: advanced technology such as 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 matching accuracy than that for those with a broader audience ("general" influencers). As a result, more niche influencers begin to participate in marketing campaigns when matching 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 technology 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.

Abstract: The data boom in e-commerce has spurred AI-powered marketplace analytics, but platforms hold the data reins. Some adopt open data-access policies with third-party analytics providers (e.g., permitting data-scraping or API-sharing) while others are restrictive. We ask why and when an e-commerce platform—capable of designing its own analytics to control sellers’ actions—may benefit from open data-access policies to accommodate competing third-party analytics services, despite the potential drawbacks of weakening its data advantages and control. We analyze two intertwined decisions an e-commerce platform can make when designing analytics to predict market competitiveness and assist sellers’ pricing decisions involving (1) data-access policy and (2) algorithm design. We find that platforms may use over-optimistic algorithms (by increasing the likelihood of generating low-competition signals) in their own analytics to boost commissions. Since sellers do not trust the platform to act in their best interest, they may be reluctant to adopt a platform’s analytics, resulting in a lose-lose situation and prompting the platform to allow data access to third-party providers. Overall, platforms gain from open data-access strategies in markets with moderately strong or weak competition. Finally, privacy legislation aimed at curtailing platforms' data-sharing practices may inadvertently hurt consumers.

Abstract: With the massive growth of social media and other informational platforms that businesses and individuals may use to share their opinions, a society's perspectives of certain contentious issues may be influenced significantly. At the same time, it has been noted that various parties have used paid trolls (i.e., fake comments made by bot accounts) to sway public opinions concerning some hotly debated issues or legal proceedings. This article aims to investigate how public opinions and paid trolls may affect the outcome of legal disputes between opposing parties. Our research indicates that litigating parties involved in lawsuits with social media engagement may purchase paid trolls to influence public opinions, and they do so more significantly when the truth does not match the prior expectation of the public. We also discover that social media users' investigation may be a pitfall for the involved parties. Additionally, the court may be either constrained or aided to identify the truth by having a tendency to render decisions that align with prevailing public opinions, depending on the stake of the lawsuit. Finally, treating public opinions on social media as additional information that aids court decision may backfire.

Abstract: In a professional service market (e.g., legal, medical, and investment advising), diversity in providers’ opinion generation processes enables consumers to benefit from aggregating opinions from multiple providers. Our study examines how providers’ GenAI adoption impacts such a market by (1) modeling vertical competition between a high-expertise provider and two low-expertise providers and (2) capturing diverse opinion generation processes as well as horizontal differentiation between low-expertise providers. We show that before GenAI adoption, consumers have an incentive to buy opinions from both low-expertise providers. A higher opinion diversity leads to an increased profit for each low-expertise provider, but reduces the profit of the high-expertise provider. Providers’ adoption of GenAI research tools narrows the quality gap between the high- and low-expertise providers, and homogenizes providers’ opinion formation processes so that consumers may lose interest in buying from multiple providers. This opinion homogenizing effect brings a set of interesting implications. First, if low-expertise providers have high opinion diversity prior to GenAI adoption, the high-expertise provider becomes better off even when GenAI adoption has no impact on its opinion quality. Second, the two low-expertise providers become better off if they are sufficiently differentiated in horizontal attributes. Otherwise, both are worse off, leading to an equilibrium situation of “the prisoners’ dilemma.” Third, consumers as a whole are better off if and only if the low-expertise providers’ opinion diversity is relatively low. Taken together, a surprising “all-lose” outcome of GenAI adoption may arise under low-expertise providers’ moderate opinion diversity, where all market players are worse off. In case of total consumer surplus loss, individual consumers with weak quality preferences still gain from improved affordability of low-expertise providers (“newcomers”), but those with moderate quality preferences may lose due to the diminished benefit from opinion aggregation (“diversity-losers”), and those with high quality preferences suffer from reduced affordability of high-expertise providers (“downgraders” or “doomed loyal”).

Abstract: The rise of foundation models in generative AI (GenAI), driven by the development of large language models (LLMs), is reshaping firms’ product development strategies through open-source technologies. Unlike traditional open-source environments characterized by collaboration and standardization, the LLM-based open-source paradigm introduces new complexities, notably fine-tuning uncertainty on performance and limited collaboration between firms, that influence innovation efforts of firms. This paper develops an analytical model to examine how these dynamics shape firms’ product development strategies, contrasting settings involving traditional open-source technologies with those built on LLMs. Our results show that fine-tuning uncertainty acts as a double-edged sword: whereas it destabilizes predictable product development outcomes for firms, it can also create artificial vertical differentiation that softens competition and boosts firm profitability. In contrast, limited collaboration imposes structural barriers that prevent cooperative vertical improvement, curtailing shared progress and reducing profitability. Taken together, however, these dynamics can still yield positive outcomes for firms. From a welfare perspective, we find that as LLMs become easier to adapt to product needs, total welfare increases in both settings, but interestingly, it grows more slowly under LLM-based open source, due to distortions introduced by the artificial vertical differentiation that weaken firm-user matching. At the same time, limited collaboration can improve welfare by compelling firms to undertake stronger vertical development individually, raising product quality and alleviating the underinvestment problem that arises in traditional open-source collaboration. Overall, our study sheds lights on the strategic trade-offs and opportunities in leveraging open-source LLMs for corporate product development. It also informs ongoing policy discussions surrounding the governance of open-source LLMs.

Abstract: Platform fragmentation, where users are dispersed across incompatible segments, poses a fundamental challenge to digital ecosystems. While conventional wisdom views fragmentation as a barrier to innovation, this paper demonstrates that its effects are more nuanced. We develop a stylized game-theoretical model comparing developer innovation under a unified versus a fragmented platform. Two heterogeneous developers with different innovation efficiencies choose innovation efforts and entry into platform segments. We find that moderate fragmentation can stimulate innovation by the less efficient developer: when entry into an additional segment incurs extra development costs, the dominance of the more efficient developer is weakened, creating competitive opportunities for the less efficient developer. Therefore, fragmentation may reduce "winner-take-all" outcomes, foster a more diverse and balanced ecosystem, and increase user welfare when entry into a user segment is moderately costly. Using data from the Firefox add-on marketplace, we provide anecdotal evidence consistent with our model's prediction: compared to more efficient developers, less efficient developers exhibit greater innovation under higher fragmentation, which in turn translates into gains in their user base and market share. These findings suggest that fragmentation, while costly, can enhance competition and developer diversity. The research contributes to the literature on platform governance and innovation by identifying conditions under which platform fragmentation can be strategically beneficial.

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.