Believe it when you see it

Dyadic embeddedness and reputation effects on trust in cryptomarkets for illegal drugs

Bibliographic Data

ID18936751
AuthorsL Norbutas (0000-0003-4712-5348, Utrecht University, corresponding author), Stijn Ruiter (0000-0003-2872-2710, Netherlands Institute for the Study of Crime and Law Enforcement), R Corten (0000-0001-8154-9393, Utrecht University)
Year2020
Volume63
Pages150-161
Publication date2020-07-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Networks (JOURNAL)
Journal identifiersISSN: 0378-8733 • E-ISSN: 1879-2111
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.socnet.2020.07.003
OpenAlexW3043142180
LanguageEN
Citations received18
References cited46

Large-scale online marketplace data have been repeatedly used to test sociological theories on trust between strangers. Most studies focus on sellers’ aggregate reputation scores, rather than on buyers’ individual decisions to trust. Theoretical predictions on how repeated exchanges affect trust within dyads and how buyers weigh individual experience against reputation feedback from other actors have not been tested directly in detail. What do buyers do when they are warned not to trust someone they have trusted many times before? We analyze reputation effects on trust at the dyadic and network levels using data from an illegal online drug marketplace. We find that buyers’ trust decisions are primarily explained by dyadic embeddedness - cooperative sellers get awarded by repeated exchanges. Although buyers take third-party information into account, this effect is weaker and more important for first-time buyers. Buyers tend to choose market exit instead of retaliation against sellers after negative experiences

Embeddedness · Internet privacy · Political science · Reputation · Sociology · Computer Science · Cybercrime and Law Enforcement Studies · Digital Marketing and Social Media · Law · Psychology · Social Psychology · Spam and Phishing Detection

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