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Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms

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  • Karthik Kannan

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

  • Rajib L. Saha

    (Indian School of Business, Hyderabad, Telangana 500111, India)

  • Warut Khern-am-nuai

    (Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 1G5, Canada)

Abstract

Consumer profiling has become one of the most common practices on online trading platforms. Many platforms strive to obtain and implement technological innovations that allow them to understand and identify consumers’ needs, and, thereafter, monetize this capability by charging sellers to present and/or sell their products or services based on consumers’ interests. However, an interesting and relevant question arises in this context: Does the platform have an incentive to profile its buyers as accurately as possible? This paper develops and analyzes a parsimonious game-theoretic model to answer this research question. We find that, surprisingly, platforms that charge sellers for discoveries have a perverse incentive to deviate from accurate buyer profiling. However, such a perverse incentive does not exist for platforms that charge sellers for transactions. As a result, with such a perverse incentive, social welfare under discovery-based pricing is lower than that under transaction-based pricing.

Suggested Citation

  • Karthik Kannan & Rajib L. Saha & Warut Khern-am-nuai, 2022. "Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms," Information Systems Research, INFORMS, vol. 33(2), pages 464-475, June.
    • Handle: RePEc:inm:orisre:v:33:y:2022:i:2:p:464-475
    DOI: 10.1287/isre.2021.1077
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