Author List: Goes, Paulo B.; Karuga, Gilbert G.; Tripathi, Arvind K.;
MIS Quarterly, 2012, Volume 36, Issue 4, Page 1021-1042.
Retailers are increasingly exploiting sequential online auctions as an effective and low cost distribution channel for disposing large quantities of inventory. In such auction environments, bidders have the opportunity of participating in many auctions to learn and choose the bidding strategy that best fits their preferences. Previous studies have mostly focused on identifying bidding strategies in single, isolated online auctions. Using a large data set collected from sequential online auctions, we first characterize bidding strategies in this interesting online environment and then develop an empirical model to explain bidders' adoption of different strategies. We also examine how bidders change their strategies over time. Our findings challenge the general belief that bidders employ their strategies regardless of experience or their specific demand. We find that bidders' demand, participation experience, and auction design parameters affect their choice of bidding strategies. Bidders with unit demand are likely to choose early bidding strategies, while those with multiple unit demand adopt late bidding strategies. Auction design parameters that affect bidders' perception of demand and supply trends affect bidders' choice of bidding strategies. As bidders gain experience within a sequence of auctions, they start choosing late bidding strategies. Our findings help auctioneers to design auction sequences that maximize their objectives.
Keywords: auction design; bidding behavior; bidding strategies; sequential online auctions
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#91 0.462 auctions auction bidding bidders bid combinatorial bids online bidder strategies sequential prices design price using outcomes behavior theoretical computational efficiency
#10 0.267 strategies strategy based effort paper different findings approach suggest useful choice specific attributes explain effective affect employ particular online control
#220 0.069 research study different context findings types prior results focused studies empirical examine work previous little knowledge sources implications specifically provide
#23 0.054 channel distribution demand channels sales products long travel tail new multichannel available product implications strategy allows internet revenue technologies times
#128 0.051 dynamic time dynamics model change study data process different changes using longitudinal understanding decisions develop temporal reveal associated state identifies