The following is a list of current working papers.
Communicating Attribute Importance under Competition
(with Jae-Yun Lee and Jungju Yu), revise and resubmit at Marketing Science
When consumers encounter unfamiliar products, they often face difficulty in understanding which attributes are crucial, leading to challenges in product comparison and potential diminished interest in the category. This study examines how firms strategically communicate the importance of product attributes in a competitive environment. Despite consumer awareness of attributes and their levels, ambiguity regarding their relative importance remains. We analyze a situation where two firms each receive a noisy signal about the true attribute importance and convey this information to consumers through cheap-talk messages. Following these communications, consumers decide whether to incur a cost to further explore the category by visiting stores. Our findings reveal a truthful equilibrium where firms honestly report their received signals. In this equilibrium, when both firms’ messages align, their collective messages can credibly convey information about the more important attribute, thereby encouraging store visits and purchase. Interestingly, firms may still find it advantageous to truthfully highlight an attribute, even if it doesn’t align with their competitive advantage. Moreover, we show that without competition (i.e., a single firm communicating), this truthful equilibrium does not exist. Thus, the presence of the competition enables the credible communication of information about attribute importance, benefiting both firms by enhancing consumer engagement with the product category. More…
Technical Appendix: Communicating Attribute Importance under Competition
Searching for Rewards
(with T. Tony Ke and Xu Zhu), revise and resubmit at Management Science
Loyalty programs are pervasive across numerous markets, offering members rewards based on their past purchases for future benefits. This study explores the dynamics of loyalty programs within a repeated ordered search framework, where consumers sequentially search for the optimal product across multiple firms over two periods. Our findings reveal that firms strategically use price discounts and rewards to influence consumer behaviors. Price discounts discourage further search in the current shopping period, while rewards encourage consumer loyalty by inducing prominence in subsequent visits. As search costs increase, firms tend to offer lower price discounts but higher rewards. This strategy increases industry profit but reduces consumer surplus. Compared with its absence, loyalty programs decrease both industry profit and consumer welfare, leading to a lose-lose outcome. Moreover, we demonstrate that when the market is heterogeneous, high-type firms, with larger networks, offer lower rewards but achieve higher second-period prices and greater consumer loyalty, contrasting with low-type firms that compensate with higher rewards for their smaller networks. This study offers new insights into the strategic use of loyalty programs and their impact on market competition. More…
The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry
(with Minkyu Shin and Jin Kim), revise and resubmit at Nature Human Behaviour
Large Language Models (LLMs) are reshaping consumer decision-making, particularly in communication with firms, yet our understanding of their impact remains limited. This research explores the effect of LLMs on consumer complaints submitted to the Consumer Financial Protection Bureau from 2015 to 2024, documenting the adoption of LLMs for drafting complaints and evaluating the likelihood of obtaining relief from financial firms. Utilizing a leading AI detection tool, we analyzed over 1 million complaints and identified a significant increase in LLM usage following the release of ChatGPT. We establish a causal relationship between LLM usage and an increased likelihood of obtaining relief by employing instrumental variables to address endogeneity in LLM adoption. Experimental data further support this link, demonstrating that LLMs enhance the clarity and persuasiveness of consumer narratives. Our findings suggest that facilitating access to LLMs can help firms better understand consumer concerns and level the playing field among consumers. This underscores the importance of policies promoting technological accessibility, enabling all consumers to effectively voice their concerns. More…
Personalized Discounts and Consumer Search
(with Zikun Liu and Jidong Zhou), under review at Management Science
The growing availability of big data enables firms to predict consumer search outcomes and outside options more accurately than consumers themselves. This paper examines how a firm can utilize such superior information to offer personalized buy-now discounts intended to deter consumer search. However, discounts can also serve as signals of attractive outside options, potentially encouraging rather than discouraging consumer search. We show that, despite the firm’s ability to tailor discounts across a continuum of consumer valuations, the firm-optimal equilibrium features a simple two-tier discount scheme, comprising a uniform positive discount when the consumer outside option is intermediate and no discount when the outside option is low or high. Furthermore, compared to a scenario where the firm lacks superior information, we find that the firm earns lower profits, consumers search more while their welfare remains unchanged, and total welfare declines. More…
Designing Detection Algorithms for AI-Generated Content: Consumer Inference, Creator Incentives, and Platform Strategy
(with Jieteng Chen and Tony Ke), under review at Marketing Science
Generative AI has transformed content creation, enhancing efficiency and scalability across media platforms. However, it also introduces substantial risks, particularly the spread of misinformation that can undermine consumer trust and platform credibility. In response, platforms deploy detection algorithms to distinguish AI-generated from human-created content, but these systems face inherent trade-offs: aggressive detection lowers false negatives (failing to detect AI-generated content) but raises false positives (misclassifying human content), discouraging good creators. Conversely, conservative detection protects creators but weakens the informational value of labels, eroding consumer trust. We develop a model in which a platform sets the detection threshold, consumers form beliefs from content labels and decide whether to engage, and creators choose whether to adopt AI and how much effort to exert to create content. A central insight is that detection does not affect outcomes continuously: instead, equilibrium structure shifts discontinuously as the threshold changes. At low thresholds, consumers trust human labels and partially engage with AI-labeled content, disciplining AI misuse and boosting engagement. But when detection threshold becomes higher, this inference breaks down, AI adoption rises, and both trust and engagement collapse. Thus, the platform’s optimal detection strategy balances these risks, influencing content creation incentives, consumer beliefs, and overall welfare. More…
Demand Externalities from Co-Location
(with B. Sen, K. Sudhir and N. Yang), revise and resubmit for 2nd round review at QME
We illustrate an approach to measure demand externalities from co-location by estimating household-level changes in grocery spending at a supermarket among households that also buy gas at a co-located gas station, relative to those who do not. Controlling for observable and unobserved selection in the use of the gas stations, we find significant demand externalities; on average a household that buys gas has a 7.7% to 9.3% increase in spending on groceries. Accounting for differences in gross margins, the profit from the grocery spillovers is 130% to 150% the profit from gasoline sales. The spillovers are moderated by store loyalty, with the gas station serving to cement the loyalty of store-loyal households. The grocery spillover effects are significant for traditional grocery products, but 23% larger for convenience stores. Thus co-location of a new category impacts both inter-format competitions with respect to convenience stores (selling the new category) and intra-format competition with respect to other supermarkets (selling the existing categories). More…