Essays on online product ratings

This dissertation focuses on the drivers of online product ratings. Existing literature shows that self-selection, social influence, and rating systems influence product ratings and reviews. By documenting the impact of multi-dimensional rating systems on product ratings and exploring strategic rating behavior on online platforms in two separate essays, I aim to implement and extend the literature on the influencers of online ratings and reviews. In the first study, I explore the effects of multi-dimensional rating systems on product ratings and review content in online platforms. Using empirical data, I demonstrate that the introduction of multi-dimensional rating systems leads to lower overall ratings compared to traditional single-dimensional systems. Additionally, I find that while users write fewer reviews in multi-dimensional systems, those reviews exhibit higher quality, with a greater focus on product-specific attributes rather than seller-specific attributes. These findings suggest that multi-dimensional rating systems not only provide a more granular understanding of product performance but also encourage more detailed and informative feedback. This study has significant implications for the design of rating systems, highlighting how structural changes can influence user behavior and enhance the usefulness of online reviews for both consumers and platform managers. By uncovering these dynamics, the study contributes to the growing literature on online consumer behavior and the optimization of rating systems. In the second study, I examine whether strategic rating behavior exists when rating products online, focusing on how users' evaluations are influenced by the number and valence of prior ratings. I present evidence that users tend to provide lower ratings to products with a higher number of previous ratings, which suggests strategic efforts to affect overall ratings. This behavior is not explained by either herding or differentiation effects. Additionally, I find that while ratings are subject to strategic behavior, review sentiment and length remain unaffected, offering a more authentic reflection of user experiences. These findings have important implications for the design of rating systems on e-commerce platforms, emphasizing the need to mitigate biases and enhance the reliability of ratings. The study contributes to the literature on the drivers of online ratings by uncovering a novel form of social influence —strategic rating behavior— that adds a new dimension to the understanding of rating dynamics and highlights the value of written reviews as a source of more genuine consumer feedback. Overall, this dissertation deepens our understanding of the drivers of online product ratings by examining both the structural influences of rating systems and the behavioral dynamics of users. It emphasizes the critical role of system design in shaping ratings and review quality, as well as the strategic behavior that can affect user evaluations. These insights not only contribute to the theoretical literature on online ratings but also provide practical guidance for improving the reliability and authenticity of user feedback on e-commerce platforms, benefiting both researchers and industry professionals.