Publication Type

Conference Proceeding Article

Publication Date

8-2012

Abstract

B2B auctions play a key role in a firm's procurement process. Even though it is known that repetition is a key characteristic of procurement auctions, traditional auctioneers typically have not put in place a suitable mechanism that supports repetitive auctions effectively. In this paper, we empirically investigate what has taken place in repeated procurement auctions based on real world data from a major outsourcing company of MRO (Maintenance, Repair and Operations) items in Korea. From this empirical study, we discovered the followings. First, we discovered that the repeated bidders contribute majority of all bids, and that the number of new entrants declined significantly as time passes. Second, repeated bidders become inactive and virtually leave the market, particularly if they fail to win in the auctions even though their bid prices were competitive. This implies that repeated bidders with lower winning rates have a higher possibility of becoming inactive. Third, the number of bidders along with the purchase amount and the bidder's previous winning rates are critical factors in determining both the winning bid price in the auction level and the bid price of each bidder. According to these research findings, we recognize that retaining a sufficient number of repeated bidders is crucial in the repeated procurement auction market. This motivates auctioneers to provide incentives to the repeated bidders to retain them in future auctions.

Keywords

Repeated bidding, repeated bidder, procurement auctions, bid pricing behavior

Discipline

Artificial Intelligence and Robotics | E-Commerce | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

ICEC '12: 14th Annual International Conference on Electronic Commerce: Singapore, 7-8 August, 2012

First Page

145

Last Page

152

ISBN

9781450311977

Identifier

10.1145/2346536.2346563

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2346536.2346563