Dimoka, Angelika and Paul A. Pavlou, "Mitigating Adverse Product and Seller Selection in Online Auction Marketplaces," (2006)
Abstract. To overcome a market of ‘lemons’, online auction marketplaces must differentiate among products and reward high-quality ones with price premiums. However, the literature has only focused on seller quality uncertainty (seller reputation), alas ignoring the role of product quality uncertainty. It is defined as the degree by which the outcome of a transaction cannot be accurately predicted due to fears that the product’s quality may differ from what is expected. Product quality uncertainty is particularly important in used and expensive experience products, such as used automobiles, whose quality cannot be conveyed via the web interface. To address adverse product selection, this study first introduces a set of product-related variables (warranty, inspection, posted price, standard value, year and mileage) that is proposed to influence price premiums by reducing product quality uncertainty. Moreover, it proposes their interaction effects with seller reputation. The proposed econometric model is tested with a multivariate regression model with secondary data from 350,000 auctions of used cars completed on eBay Motors using a custom data mining tool. Implications for mitigating adverse product selection and preventing a market of ‘lemons’ are discussed. Download pdf.
