“Sherlocking” and Platform Information Policy

Platform-run marketplaces may exploit third-party sellers’ data to develop competing products, but the threat of future competition can deter sellers’ entry. We explore how this trade-off affects the platform’s entry on the marketplace and the referral fee it charges to the third-party sellers. We first characterize the platform’s optimal fee under different degrees of commitment on its own entry policy. We show that full commitment maximizes not only the platform’s payoff but also consumer surplus, as the associated fee induces maximal entry by the sellers. Next, we show how and when the platform’s policy on information sharing between its marketplace and product divisions can substitute for its commitment to entry. We characterize the platform’s optimal information policy and examine how it interacts with the platform’s fee structure. We show that an outright ban on information sharing between the platform’s divisions can reduce consumer welfare as it may lead to a higher referral fee that thwarts sellers’ entry. Our findings highlight the importance of considering the platform’s fee structure as a strategic response in the policy debates on marketplace regulation. This paper was accepted by Alfonso Gambardella, business strategy. Funding: J. P. Choi is supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [Grant NRF-2023S1A5A2A01074511]. K. Kim is supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [Grant NRF-2020S1A5A2A03043516].

Authors

Institutions

Publication Details

Journal
Management Science
Published
2025-04-07
DOI
https://doi.org/10.1287/mnsc.2024.05215
Primary Topic
Digital Platforms and Economics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

“Sherlocking” and Platform Information Policy

Jay Pil Choi, Kyungmin Kim, A.K. Mukherjee
Management Science
Digital Platforms and Economics
article

“Sherlocking” and Platform Information Policy

Jay Pil Choi, Kyungmin Kim, A.K. Mukherjee
article en

Abstract

Platform-run marketplaces may exploit third-party sellers’ data to develop competing products, but the threat of future competition can deter sellers’ entry. We explore how this trade-off affects the platform’s entry on the marketplace and the referral fee it charges to the third-party sellers. We first characterize the platform’s optimal fee under different degrees of commitment on its own entry policy. We show that full commitment maximizes not only the platform’s payoff but also consumer surplus, as the associated fee induces maximal entry by the sellers. Next, we show how and when the platform’s policy on information sharing between its marketplace and product divisions can substitute for its commitment to entry. We characterize the platform’s optimal information policy and examine how it interacts with the platform’s fee structure. We show that an outright ban on information sharing between the platform’s divisions can reduce consumer welfare as it may lead to a higher referral fee that thwarts sellers’ entry. Our findings highlight the importance of considering the platform’s fee structure as a strategic response in the policy debates on marketplace regulation. This paper was accepted by Alfonso Gambardella, business strategy. Funding: J. P. Choi is supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [Grant NRF-2023S1A5A2A01074511]. K. Kim is supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [Grant NRF-2020S1A5A2A03043516].

Management ScienceVol. 71(11)
Emory University (US), Yonsei University (KR), Michigan State University (US)
Openalex Percentile: Top 91%
Digital Platforms and Economics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.