Online labor markets are Web-based platforms that enable buyers to identify and contract for information technology (IT) services with service providers using buyer-determined (BD) auctions. BD auctions in online labor markets either follow an open or a sealed bid format. We compare open and sealed bid auctions in online labor markets to identify which format is superior in terms of obtaining more bids and a higher buyer surplus. Our theoretical analysis suggests that the relative advantage of open versus sealed bid auctions hinges on the role of reducing service providers' valuation uncertainty (difficulty in assessing the cost to execute a project) and competition uncertainty (difficulty in assessing the intensity of the competition from other service providers), which largely depend on the relative importance of the common value (versus the private value) component of the auctioned IT services, calling for an empirical investigation to compare open and sealed bid auctions. Based on a unique data set of 71,437 open bid auctions and 7,499 sealed bid auctions posted by 21,799 buyers at a leading online labor market, we find that, on average, although sealed bid auctions attract 18.4% more bids, open bid auctions offer buyers $10.87 higher surplus. Furthermore, open bid auctions are 55.3% more likely to result in a buyer's selection of a certain service provider and 22.1% more likely to reach a contract (conditional on the buyer's making a selection) with a provider, and they generate higher buyer satisfaction. In contrast to conventional wisdom that Òthe more bids the betterÓ and industry practice of treating sealed bid auctions as a premium feature, our results suggest that the buyer surplus gained from the reduction in valuation uncertainty enabled by open bid auctions outweighs the buyer surplus gained from the higher competition uncertainty in sealed bid auctions, which renders open bid auctions a superior auction design in online labor markets.
Product <i>fit</i> uncertainty (defined as the degree to which a consumer cannot assess whether a product's attributes match her preference) is proposed to be a major impediment to online markets with costly product returns and lack of consumer satisfaction. We conceptualize the nature of product fit uncertainty as an information problem and theorize its distinct effect on product returns and consumer satisfaction (versus product quality uncertainty), particularly for experience (versus search) goods without product familiarity. To reduce product fit uncertainty, we propose two Internet-enabled systems—<i>website media</i> (visualization systems) and <i>online product forums</i> (collaborative shopping systems)—that are hypothesized to attenuate the effect of product type (experience versus search goods) on product fit uncertainty.
Online markets pose a difficulty for evaluating products, particularly experience goods, such as used cars, that can not be easily described online. This exacerbates product uncertainty, the buyer's difficulty in evaluating product characteristics, and predicting how a product will perform in the future. However, the IS literature has focused on seller uncertainty and ignored product uncertainty. To address this void, this study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars(eBay Motors).Extending the information asymmetry literature from the seller to the product, we first theorize the nature and dimensions (description and performance) of product uncertainty. Second, we propose product uncertainty to be distinct from, yet shaped by, seller uncertainty. Third, we conjecture product uncertainty to negatively affect price premiums in online markets beyond seller uncertainty. Fourth, based on the information signaling literature, we describe how information signals (diagnostic product descriptions and third-party product assurances) reduce product uncertainty.The structural model is validated by a unique dataset comprised of secondary transaction data from used carson eBay Motors matched with primary data from 331 buyers who bid on these used cars. The results distinguish between product and seller uncertainty, show that product uncertainty has a stronger effect on price premiums than seller uncertainty, and identify the most influential information signals that reduce product uncertainty.The study's implications for the emerging role of product uncertainty in online markets are discussed