Software Characteristics of B2B Electronic Intermediaries: A Novel Design Science Approach

Long being seen as commercially unsuccessful after the dot-com era, web-based B2B electronic intermediaries are currentlyundergoing a renaissance driven by globalization and an ever increasing cost-pressure on procurement departments ofenterprises. These systems are getting more sophisticated almost by the day, which is also reflected by numerous relatedscientific articles. This development raises the question of the latest characteristics of such systems scientifically described.In order to answer this question, the work at hand depicts the results of a novel design science approach based on a structuredliterature review. The outcomes of this research are i) a state-of-the-art overview of scientifically described softwarecharacteristics of B2B electronic intermediaries, and ii) a taxonomy for structuring software characteristics of this type ofsystems. The results may help practitioners to further develop B2B electronic intermediaries and e-procurement systems, andwill serve as a basis for future research endeavors in the field

Bidder support in iterative, multiple-unit combinatorial auctions

This thesis is about supporting the bidders' decision making in iterative combinatorial auctions. A combinatorial auction refers to an auction with multiple (heterogeneous) items, in which bidders can submit bids on packages. Combinatorial auctions are challenging decision making environments for bidders, which hinders the adoption of combinatorial mechanisms into practice. Bidding is especially challenging in sealed-bid auctions. Bidders do not know the contents of other bidders' bids and hence cannot place bids that would team up with existing bids to become winners. The objective of this study is to develop and test support tools for bidders in semi-sealed-bid, iterative combinatorial auctions. The tools are designed for reverse auctions, but can easily be applied to a forward setting. The Quantity Support Mechanism (QSM) is a support tool, which provides the bidders with a list of bid suggestions. The bid suggestions are such that if submitted, they would become provisional winners. The QSM benefits both bidders and the buyer, because it chooses suggestions that are most profitable for the bidders while decreasing the total cost to the buyer. The QSM is based on a mixed integer programming problem. The QSM was tested in two simulation studies. The results of the studies indicated that the QSM works well - it is much better to use the QSM than no support - but that it does not necessarily guide the auctions to the efficient allocation. The QSM was also integrated into an online auctions system, and tested with human subjects. The results of the laboratory experiment showed that the performance of the QSM is dependent on the bidders' behavior and the kind of bids they place in the auction. The user interface of the auction was good. I also observed bidders' strategies, and could identify different bidder types corresponding to those reported in earlier studies. The experiment also showed the importance of experience in complex bidding environments. The simulation studies and the laboratory experiment showed that the QSM is too dependent on the existing bids in the bid stream, which causes the auctions to end in inefficient allocations. In order to overcome this problem we designed another support tool, the Group Support Mechanism (GSM). The main logic in the GSM is similar to the QSM. The main difference is that instead of solving for one bid that complements existing bids to become a winner, the GSM can suggest several bids for different bidders. Together this set of bids would then become provisionally winning. The preliminary tests show significant improvement in the efficiency of the auction outcomes when the GSM was used instead of the QSM. Future research includes the further development of the GSM and its testing with simulations and human subjects. Also, bidder behavior, bidder strategies and the effect of learning and experience in combinatorial auctions should be further studied. This is important because bidders' behavior in the auctions affects the auction design and the requirements for the user interface