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Search algorithm,repetitive information,and sales on online platforms
Institution:1. Hong Kong University of Science and Technology, Hong Kong;2. University of Hong Kong, Hong Kong;1. Compass Lexecon, Spain;2. Graduate Institute of International and Development Studies, Geneva, CEPR and Compass Lexecon, Belgium;3. Bergamo University and Compass Lexecon, Italy;1. Dipartimento di Economia e Finanza, Catholic University of Milan, Via Necchi 5, 20123 Milano, Italy;2. Dipartimento di Matematica per le Scienze Economiche, Finanziarie ed Attuariali, Catholic University of Milan, Largo Gemelli 1, Milano 20123, Italy;1. Stockholm School of Economics, P.O. Box 6501, Stockholm 113 83, Sweden;2. MAG Interactive, Drottninggatan 95A, Stockholm 113 60, Sweden;3. Norwegian School of Economics, Norway;4. CEPR;1. Department of Quantitative Finance, National Tsing Hua University, Taiwan;2. Department of Economics, National Tsing Hua University, Taiwan;1. School of Management, Fudan University, 670 Guoshun Road, Shanghai 200433, China;2. Department of Economics, University of Southern California, 3620 South Vermont Ave., Los Angeles, CA 90089, United States;3. Uber Technologies Inc., United States
Abstract:A prominent feature of online sales is that buyers rely on the search tools offered by platforms to process information when searching for products. We develop a model that captures how the search algorithm affects buyers’ search processes, which further influences market equilibrium and welfare. If a platform adopts a highly unequal search algorithm, buyers are likely to obtain repetitive information about a small group of sellers, which causes buyers to consider fewer options and suppresses competition. By using data from food delivery platforms, we provide empirical evidence that markets with less equal distributions of store rankings in search results have higher average prices and more concentrated sales. We suggest that regulators should restrict search algorithms from showing repetitive information.
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