In this thesis we present three papers which investigate informative content generated by consumers, aiming to improve the usefulness for matching high quality products at lower prices. Following a general perspective, we explore platform product listing, searchable through a decision making mechanism. In a more specialized perspective, we take into account a dropping price modality service, differentiating the consumer benefit in the case of high or low quality product matching. Chapter 1 Product quality on platform markets. Abstract Many studies have questioned the meaning of “product quality”, hanging between a characteristic interpretation of a product for improving consumer satisfaction, and scientific approach to measure its benefits. Starting from the historical quality setting as mirror image of the price, we investigate the adoption of new signals, developed over the years to adjust the original relationship. Recently, bootstrapping by emperor of e-commerce platforms, the rating system has emerged as a reference contribute for product quality informativeness. We study this tendency, to show its failure in the presence of low price market and new brands. For this purpose, we collect User Generated Contents from a well-known online retailing platform. We capture and distill meaningful features in order to adjust the rating assigned by reviewers, and propose a novel quality formula able to increase the accuracy of the information provided to the consumer. We suggest that our formula better captures product quality, and, when adopted by a platform for sorting the products, it increases the products variety and, consequently the satisfaction of the consumer. Our proposal suggests a way to facilitate the consumer search (as we will show in the second chapter). Moreover, it can be used as a measure of market efficiency in the case of voluntary opacity of the platform in exposing product quality signals.
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