Abstract

This paper extends a well-established credence goods model to digital platforms and disintermediation. Users seek the assistance of a digital platform to find the right expert. However, they are not aware of which service type they need; this is why they consult the expert in the first place. Asymmetric information allows experts to prioritize their profit over the users’ needs, as users cannot, even after consumption, detect potential mistreatment. Digital platforms can help users find the right expert and learn about their required service level, mitigating this inefficiency. However, some users with minor quality requirements may then systematically bypass the platform to avoid transaction fees. To adress the problem of disintermediation, the platform could explore a subscription-based revenue model. However, a transaction-based revenue model potentially increases the number of users on the platform under information asymmetry. This could result in an improved matching algorithm and stronger network effects.

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