Effect of Information Revelation Policies on a Web - Services Marketplace

Competitive information revealed in a marketplace will have significant impact on the learning ability of the participating sellers across market sessions and therefore, on performance of the marketplace. The marketplace that we study in this paper has the following characteristics: a) Products transacted are custom-built – created specifically for a consumer and cannot be resold to other consumers b) Sellers are the bidders who have ex-ante uncertainty about the topography of the marketplace – whether the marketplace is a monopoly, a duopoly or how many sellers participate in a competitive market. c) Sellers have substantial ex-ante uncertainty about the quality of their product because these are custom-built. d) Sellers incur non-negligible marginal cost for building and delivering the product. In this paper, we use a computational test-bed to analyze the effect of information revelation policy on the following metrics: consumer surplus, producer surplus and social welfare. The information settings that we study are: a) Zero-Information Setting in which each seller only knows whether it won its bid at the conclusion of each market session or not b) Quasi-Information setting in which only the winning bid is revealed to all and c) Complete-Information Setting where information about all bids are revealed with dummy identification without revealing the true identity of the bidders. Based on our results, we find the producer surplus monotonically decreases with information revealed while the consumer surplus increases. Also, the social welfare generated in the Complete-Information Setting was not significantly different from that in the Zero-Information Setting. Although one may interpret this as 3 information revelation aiding the transfer of surplus from the producer surplus to the consumer surplus, this is not the case and we provide explanation for this. Finally, we apply our results to a specific instance of web services marketplace – reverse-auction marketplace and provide recommendations.