JOURNAL ARTICLE

How Search Engine Impacts Market Structure: Empirical Evidence from a Multivendor Darknet Market.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 5. P. 4319 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Lu, Ying; Qiao, Dandan; He, Shu; Tan, Bernard C. Y. 3 of 3

Abstract

This article empirically examines the impact of the cross-website search engine GRAMS on the market structure of illegal online Darknet markets, focusing on vendor and product category levels. Using a difference-in-differences approach with data from multiple Darknet marketplaces before and after GRAMS’ introduction, the study finds that while the search engine boosts overall market transactions, it disproportionately benefits leading vendors and popular drug categories, resulting in increased market concentration. Two key mechanisms explain this concentration: consumers’ preference for trustworthy leading vendors—especially for high-risk drugs—and these vendors’ superior ability to scale up by expanding product assortments to offer a convenient “one-stop” shopping experience, rather than competing on price. The findings contribute to understanding how search technologies affect multivendor online markets, particularly in trust-sensitive illicit contexts, and offer insights relevant for law enforcement strategies and the regulation of search technologies in various market environments.

Additional Information

  • Source:Management Science (INFORMS). 2026/05, Vol. 72, Issue 5, p4319
  • Document Type:Article
  • Subject Area:Library and Information Science
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.04133
  • Accession Number:193596719
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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