Decision Support in Multi-Attribute Reverse Auctions

ABSTRACT This study seeks to better understand bidding decision support in multi-attribute reverse auctions. It views bidding as a decision making process and attempts to determine how tools that aid different stages of the decision making process affect the quality of the bids submitted. Two experiments reveal that decision support tools that impact all the stages of a bidder's decision making process can generate high quality bids. However, the findings also reveal that even with such tools, variations in auction structural elements such as the number of bidders and the number of auction attributes can impact the quality of the bids submitted. Keywords: bid-quality, decision-making process, decision support, multi-attribute auctions (ProQuest: ... denotes formulae omitted.) 1. Background Reverse auctions are market mechanisms that enable sellers rather than buyers to compete via a bidding process in order to supply goods or services [Dans, 2002]. While the importance of providing bidders with adequate decision support in reverse auctions has been stressed [Leskela, Teich, Wallenius, Wallenius, 2007], it has not been a mainstream concern in the literature [Teich, Wallenius, Wallenius, Koppius, 2004]. Recently some scholars have called for more research on ways to better support bidder decision making. For instance Leskela et al. [2007] state that it is imperative for auctioneers to provide decision support for the bidders in combinatory reverse auctions. Teich, Wallenius, Wallenius, and Zaitsev [2006] suggest that well designed auctions should not only provide support for bid-takers, but they should also enable bidders to make good bids. Rothkopf and Whinston [2007] express that studies which consider the decision support potential of feedback information in reverse auctions would be an important extension to auction literature. In response to this call, this study focuses on decision support in the context of multi-attribute reverse auctions. Drawing on Simon's [1960] decision making model, this study views bidding as a decision making process, and investigates the effects of various decision support tools on the different stages of a bidder's decision making process and ultimately their impact on the quality of bids submitted. Bid quality refers to the desirability of a bid to a bid-taker; the more desirable the bid the better its quality. This study is motivated by both a basic need and a fundamental desire to develop a systematic approach which ensures that decision support tools developed for a complex auction can yield high-quality bids. Currently, this approach is lacking. The few existing studies on decision support in reverse auctions have concentrated on developing different types of tools to assist bidders during the bidding process. Some have focused on creating mechanisms that minimize the time and effort involved in bidding by automating the bidding process through the development of intelligent software agents [Yung, Yang, Lau, Yen, 2000]. For instance, in "price only" business-to-consumer (B2C) and consumer-to-consumer (C2C) auctions, artificial sniper agents have been designed to place bids seconds before the auction closes on behalf of bidders. In some cases sniping agents have been found to be as effective as humans at placing bids [Bapna, 2003] and their use is becoming more common in practice [Ku and Malhotra, 2001]. Examples of sniping agents used in B2C and C2C auctions include EZ Sniper, JustSnipe, JBidwatcher just to name a few. Software agents have also been employed in reverse auction mechanisms. For example, to automate bidding in procurement reverse auctions, Sikora and Sachdev [2008] create a tool that facilitates the use of artificial agents to learn competitors' strategies and then propose effective responses during the bidding process. Other studies have focused on reducing bidder's cognitive strain through creating tools that provide computational support or feedback information. …