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The Invisible Value of Information Systems: Reputation Building in an Online P2P Lending System

Technological advancements in web-based information systems in the last decade has given rise to Web 2.0 market mechanisms, where individuals take up active roles and their participation is key to survival of these markets. Many of these markets lean heavily towards C2C transactions where consumers act as buyers, sellers and even independent experts. Participants in these markets act based on their perception of trustworthiness or reputation of other parties and the risks involved in the associated transactions. We use data from a peer-to-peer lending community to empirically investigate the role of information systems in alleviating adverse selection through information availability in a market where information asymmetry poses the greatest threat. We focus on borrowers and posit that due to the repeated nature of transactions in the online P2P system, borrowers develop a form of identity, the borrower’s reputation. A borrower’s reputation is the collective perception of the lenders about the borrower and a signal of the trustworthiness of the borrower. We set up a simultaneous equation model of outcomes of interest and a latent class model of reputation that affects the outcomes and is affected by them. Reputation is modeled as a discrete variable with three states, and we use the novel approach of latent instrumental variable modeling to deal with endogeneity. We estimate the model using Bayesian MCMC, compare alternative models, and discuss the impact of the state of reputation on the outcomes through the different reputation-specific coefficients of the models. We show that accounting for reputation improves the explanatory power of the model, and therefore provide a way to empirically model the evolution and impact of reputation in online platforms where repeated transactions are performed. (Peer-to-Peer Lending, Reputation, Latent Instrumental Variable Modeling, Latent Class Model, Hidden Markov Model, Bayesian Analysis)