Believe it when you see it: dyadic embeddedness and reputation effects on trust in cryptomarkets for illegal drugs
Lukas Norbutas, Rense Corten, Stijn RuiterReputation is key for establishing trust between individuals in social interactions and market exchanges in which information about partner’s trustworthiness or true quality of goods and services is hard to obtain. When uncertainty is high, people can rely on their social networks to exchange with someone they already know. Social ties can also be used to get information about trustworthiness of potential partners, or sanction them after opportunistic behavior. When economic exchanges take place between many anonymous actors, such as in large online marketplaces, social network effects are often substituted by reputation systems.
As such, 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.
Taken together, our findings show that reputation effects play a crucial role in establishing trust in an uncertain market environment. However, not all reputation information is equally important when making trust decisions – individual history of exchanges plays a much stronger role than third-party information, and subsequently translates into strong dyadic embeddedness of actors in the exchange network.