Competitive Bidding Strategy in Buyer-determined Online Reverse Auctions

Many industries utilize competitive, online reverse auctions for industrial procurement. In many of these auctions, the lowest price bidder is not guaranteed to be the winner and the buyer has full latitude to select the winner from any of the qualified bidders. What are the competitive bidding strategies of bidders in these online auctions? Using an adaptive hierarchical bounded rationality mixture model framework and point-by-point bid data from ten lots of industrial reverse auctions, we find that some bidders incorporate quality information from competitive bids over the course of the auction; that is, suppliers not only adjust their bids for their own quality-advantage or disadvantage – they also account for opponents’ perceived quality differences (on the basis of anonymous price bids). This behavior has implications for the optimal choice of auction format. We estimate bidder rationality from the data and use the estimated parameters to then examine predictions for artificial adaptive agents under different assumptions. We find that the choice of a full price visibility auction design versus a partial price visibility format critically depends on the relationship between cost and quality as well the number of competing sellers.