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Welcome to the reputation-systems wiki!
Online reputation systems constitute an important component of many electronic commerce platforms. Reviews and ratings posted on these platforms strongly influence the purchase and browsing behavior of customers, and play an important role in revenue generation. Existing literature in marketing, computer science, and economics highlight the statistical foundations of such impacts and address some of the behavioral and structural patterns that govern them (such as self-selection and reporting bias). These studies, however, fall short of a comprehensive framework to capture all aspects of online reputation dynamics that include the public perception of reviews, as well as the purchase and rating decisions. In this work, we propose a simulation-based dynamic model for the evolution of reputation on an online e-commerce platform. We use the proposed generative model to make inferences about individuals’ rating behavior using approximate Bayesian computation (ABC). Finally, we apply the ABC to real time-series data of product reviews submitted on a popular e-commerce platform. The results reveal circumstances under which these reviews are posted and their implications for the evolution of product ratings. Gaining a better understanding of the dynamics of reputation systems, namely, the conditions under which ratings are submitted, and how they are eventually interpreted, can be crucial for marketers and designers of digital platforms, who can leverage this information to stimulate further reviews and better manage user generated content.
