Title: Platform Disintermediation: Information Effects and Pricing Remedies
Abstract: Two-sided platforms, such as labor marketplaces for hiring freelancers, typically generate revenue by matching prospective buyers and sellers and extracting commissions from completed transactions. Disintermediation, where sellers transact off-platform with buyers to bypass commission fees, can undermine the viability of these marketplaces. Although circumventing the platform allows sellers to avoid commission fees, it also leaves them fully exposed to risky buyers (given the absence of the platform's payment protections) and incurs switching costs (given the absence of the platform's transaction infrastructure). In this paper, we consider interventions for addressing disintermediation, focusing on the pricing and informational levers available to the platform, where the latter refers to the accuracy of the signal sellers receive about buyers' riskiness. First, while intuition suggests platforms should counter disintermediation by lowering commission rates, in a high-information environment a platform may be better off raising them. Further, a platform may strictly benefit from sellers receiving a partially-informative buyer signal (i.e., not perfectly revealing nor concealing a buyer's riskiness), particularly when switching costs are low. Finally, while charging sellers platform-access fees can immunize the platform from disintermediation, it can fall short of the optimal revenue under commission-based pricing. We also examine the efficacy of banning sellers that are caught disintermediating, and extend our findings to a setting with repeated transactions. Overall, our results shed light on how disintermediation disrupts platform operations and offers prescriptions for platforms seeking to counteract it.
Bio: Auyon Siddiq is an Assistant Professor of Decisions, Operations and Technology Management at the UCLA Anderson School of Management. His research draws from optimization, machine learning, econometrics, and game theory, with a common thread of fitting structural models to data. Auyon is also interested in models of strategic behavior as it relates to incentives and competition. Contextual areas of his work include urban mobility, healthcare, and labor platforms.
At Anderson, he taught the core analytics course in the full-time MBA program and the core optimization course in the Master of Science in Business Analytics (MSBA) program. In 2020, he was named to Poets&Quants' list of the Best 40-Under-40 Business School Professors.
Before joining UCLA, Auyon received a PhD in Operations Research from the University of California, Berkeley, an MASc in Industrial Engineering from the University of Toronto, and a BEng in Electrical Engineering from Dalhousie University, which is in his hometown of Halifax, Canada.
