摘要： Nowadays, online auctions have become the most successful business model in the electronic marketplace. To the best of the authors’ knowledge, no other work has been devoted to the prediction of closing price and duration of Business-to-Business (B2B) English reverse online auctions in which goods or service providers compete with each other to win contracts by lowering offering prices with each bid, which is conducted on a virtual platform hosted on the Internet. This research designs and proposes a new methodology to predict closing prices and duration within the first few bids of the corresponding auctions based on real time bidding information rather than static auction information. In this article, we employ real time information and prediction rules to forecast the behavior of live auctions. This is in contrast to the static prediction approach that takes into consideration only information available at the beginning of an auction such as products, item features, or the seller’s reputation. This simulation is based on discretized auction data derived from a B2B online auction marketplace over a two-year period. Three measurements including accuracy, coverage, and benefit are used to evaluate the methodology. Results show that after observing the first 4 bids, this methodology can predict closing prices and duration with 84.6 and 71.9% accuracy, respectively. 其他題名： Knowl Inf Syst 出版者： London: Springer-Verlag 出版日期： 2012-09-01 出處： Knowledge and information systems, 2012-09, Vol.32 (3), p.697-716 資源來源： ABI/INFORM Collection (ProQuest Business/Economics) (LUT) 版權： Springer-Verlag London Limited 2011 版權： 2014 INIST-CNRS 版權： Springer-Verlag London Limited 2012 識別號： ISSN: 0219-1377 識別號： EISSN: 0219-3116 識別號： DOI: 10.1007/s10115-011-0449-6 識別號： CODEN: KISNCR
