Buyer's optimal information revelation strategy in procurement auctions
File(s)Buyer’s Optimal Information Revelation EJOR.pdf (401.76 KB)
Accepted version
Author(s)
Qian, Cheng
Anderson, Edward
Type
Journal Article
Abstract
We consider a procurement auction where the buying firm can manipulate the distribution of the uncertainty facing competing suppliers via reducing subjectivity in the scoring rule announced before the auction, and we examine the optimal choice of information revelation for the buyer. Specifically, we model a multi-attribute scoring auction in which the suppliers submit bids involving both price and non-price attributes and the buyer selects one supplier according to a weighted scoring system. Although the scoring rule is preannounced and the buyer commits to it during the bid evaluation, it contains elements that are subjective in nature and not precisely defined, so the suppliers still do not have full information about the exact score that will be awarded. It may be possible for the buyer to reduce the subjective component in the scoring rule by giving unusually detailed descriptions of what corresponds to specific scores. We demonstrate that it is beneficial for the buyer to limit the information revealed by retaining some subjective or imprecisely defined components in the announced scoring rule, so that the suppliers continue to be uncertain about their final scores. It is also shown that the buyer can gain more from this type of imprecision (i.e., releasing less information) if the suppliers are more different in terms of their costs to achieve a given quality level or other aspects of utility for the buyer. We consider both sealed bid and open auction formats.
Date Issued
2020-06-16
Date Acceptance
2019-11-26
Citation
European Journal of Operational Research, 2020, 283 (3), pp.1011-1025
ISSN
0377-2217
Publisher
Elsevier
Start Page
1011
End Page
1025
Journal / Book Title
European Journal of Operational Research
Volume
283
Issue
3
Copyright Statement
© 2019 Elsevier B.V. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000518704400015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Social Sciences
Science & Technology
Technology
Management
Operations Research & Management Science
Business & Economics
Auction/bidding
Scoring auction
Multi-attribute bids
Information revelation
QUALITY
PROBABILITY
COMPETITION
MECHANISM
PRICE
Publication Status
Published
Date Publish Online
2019-11-30