Job Market Paper
- “Data Preferences in Firm Learning: Evidence from an Online Auction Platform”
Abstract
Governmental organizations and industry practitioners have promoted data sharing across firms to expedite learning to improve business decisions. However, current discussions have largely overlooked the possibility that firms may prefer their own data over others' data. This paper investigates the presence of such biased preferences among firms, focusing on used-car auction sellers on China's largest online auction platform. Auction timing is crucial on this platform as payoffs vary by hour, influenced by bidders' valuations and the number of residual bidders at different times. Despite being experienced local sellers before joining Ali Auction, these sellers face national demand and competition in the online environment, creating the scope for learning. Combining state-of-the-art auction literature, I develop a structural model of sellers' learning based on their own and others' data to optimize auction timing. The model estimates suggest that sellers' preferences for different data sources change with experience, with sellers weighing their own data at 90% compared to 10% for others' data at the average level of experience. Moreover, the data preferences account for more than half of the revenue gap between the status quo and the full information scenario. These findings have two implications for the platform. First, data sharing alone may not effectively guide sellers in selecting optimal auction timing. Second, the platform can leverage sellers' data preferences to guide new sellers to optimal timing early in their tenure, ensuring lasting benefits. Overall, the platform should play a coordinating role in helping sellers identify the best timing for their auctions.
Working Paper
- “Shaping the Influencers: The Role of Multi-Channel Networks” [SSRN]
- with Yulin Hao
- Revise & Resubmit at Journal of Marketing Research
- Presentation:
Abstract
Social media influencers are increasingly affiliating with multi-channel networks (MCNs), also known as influencer agencies. These MCNs recruit influencers and help them monetize their content. More importantly, MCNs are rumored to be directly involved in content creation. This paper provides the first empirical examination of the effects of MCN affiliation on influencer content. To this end, we construct a unique dataset tracking influencers' changes in their MCN affiliation on TikTok in China. Using a difference-in-differences strategy, we compare influencers who switched their affiliation status with observably similar non-switchers. The findings reveal that MCN affiliation enhances content engagement and leads to more homogeneous and focused content, steering influencers towards topics with higher advertising prices. However, the content quantity does not change. When influencers affiliate with MCNs, these influencers also have more sponsorships and charge a higher advertising price, which is predominantly driven by changes in content resulting from the affiliation. These results suggest that platforms and influencers can benefit from improved engagement and sponsorships resulting from MCN affiliation. Although advertisers face higher advertising prices charged by MCN-affiliated influencers, these prices are justified by more engaging and focused content, which may also be beneficial to the advertisers.
Work in Progress
- “Consumer as Helping Hands: The Value of Incentives in Mitigating Demand Fluctuations under Capacity Constraints”
- “Optimal Design of Credit Card Discounts: Monetizing Discount Synergy Across Products”
- with Bowen Luo and Ruiqi Wu
- Data access through collaborating with Wharton AI & Analytics for Business (AIAB)
- “Bargaining in New Product Launch (tentative title)”