  The emergence of Consumer-to-Consumer (C2C) platforms has allowed consumers
to buy and sell goods directly, but it has also created problems, such as
commodity fraud and fake reviews. Trust Management Algorithms (TMAs) are
expected to be a countermeasure to detect fraudulent users. However, it is
unknown whether TMAs are as effective as reported as they are designed for
Peer-to-Peer (P2P) communications between devices on a network. Here we examine
the applicability of `EigenTrust', a representative TMA, for the use case of
C2C services using an agent-based model. First, we defined the transaction
process in C2C services, assumed six types of fraudulent transactions, and then
analysed the dynamics of EigenTrust in C2C systems through simulations. We
found that EigenTrust could correctly estimate low trust scores for two types
of simple frauds. Furthermore, we found the oscillation of trust scores for two
types of advanced frauds, which previous research did not address. This
suggests that by detecting such oscillations, EigenTrust may be able to detect
some (but not all) advanced frauds. Our study helps increase the
trustworthiness of transactions in C2C services and provides insights into
further technological development for consumer services.
