Competitor referral by platforms

Jianqiang Zhang (), Qingning Cao () and Xiuli He ()
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Jianqiang Zhang: Jiangsu Normal University
Qingning Cao: University of Science and Technology of China
Xiuli He: University of North Carolina at Charlotte

Annals of Operations Research, 2023, vol. 329, issue 1, No 26, 757-780

Abstract: Abstract Online platforms provide sellers’ products on their websites and earn a commission fee for each unit sold. Recently, many platforms have tried to refer customers to their direct competitors. In this paper, we explain this counter-intuitive practice by developing a game-theoretic model where two competing platforms contracting with one common seller or two competing sellers. We first analyze a benchmark case where platforms and sellers are integrated, finding that competitor referral will aggregate competition and thus neither platform is willing to refer its competitor voluntarily. However, when each platform serves as a marketplace for an independent seller, it is possible that a platform voluntarily refers its competitor. The rationale is that there exists a double marginalization problem when platforms set commission fees and sellers set prices, resulting in low efficiency of product selling. By referring visitors to their competitors, platforms can introduce cannibalization to cope with the double marginalization problem. We also investigate the case when the two platforms serve one common seller, finding that as long as the seller does not charge discriminated prices for the same product, the platforms may also apply referral. This is because the benefits of the two platforms are more aligned when the common seller sets a common price. Thus, online referral which helps the competitor may also help the referring platform itself. This paper also makes serval extensions to check the robustness of the model.

Keywords: Competitor referral; Platform; Online competition; Game theory (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-021-04020-4

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