【公开学术报告】Data Preferences in Firm Learning: Evidence from an Online Auction Platform

Data Preferences in Firm Learning: Evidence from an Online Auction Platform

Guest Speaker: LI Xiaojie (University of Rochester)

Date & Time:       14:00-15:30 (Beijing Time), Wed. 16^th, Oct. 2024

Classroom:  Room 2101, Tongji Building A

ABSTRACT

Governmental organizations have promoted data sharing across firms to expedite firms’ 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 such biased data preferences among firms, focusing on used-car auction sellers on Ali Auction, the largest online auction platform in China. These sellers primarily decide on auction timing, which is crucial on this platform as payoffs vary by hour. Despite being experienced local sellers before joining the platform, these sellers face national demand and competition in the online environment, creating the scope for learning. 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 relatively weighing their own data at 90% compared to 10% for others’ data at the average level of experience. The counterfactual results show that data preferences are the main reason that prevents the sellers from achieving full potential profit. 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.