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An Empirical Analysis of Price, Quality, and Incumbency in Procurement Auctions

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  • Tunay I. Tunca

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • D. J. Wu

    (Georgia Institute of Technology, Scheller College of Business, Atlanta, Georgia 30308)

  • Fang (Vivian) Zhong

    (Zurich Financial Services Group, 8002 Zurich, Switzerland)

Abstract

The use of multiattribute auctions for procurement of products and services when both price and quality matter is becoming more frequent. Such auctions often employ scoring rules and are open ended in winner determination. Yet there is a significant gap in the literature on the efficiency of these procurement mechanisms. In this paper, providing a theoretical model and utilizing data from legal service procurement auctions, we study how open-ended scoring auctions can be used effectively in procurement and demonstrate the roles supplier quality and incumbency play in this process. We demonstrate that open-ended auctions can generate substantial savings to a buyer without compromising quality. We study the underlying mechanism and show how the auction format can work to achieve such performance. We find that the buyer's revealed preferences significantly differ from her stated preferences. Finally, we contribute to the understanding of the role of incumbency in procurement auctions by providing evidence that what may be perceived as incumbency bias can in fact be a revelation of preference for quality.

Suggested Citation

  • Tunay I. Tunca & D. J. Wu & Fang (Vivian) Zhong, 2014. "An Empirical Analysis of Price, Quality, and Incumbency in Procurement Auctions," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 346-364, July.
    • Handle: RePEc:inm:ormsom:v:16:y:2014:i:3:p:346-364
    DOI: 10.1287/msom.2014.0485
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    References listed on IDEAS

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    3. Alibeiki, Hedayat & Gümüş, Mehmet, 2020. "Supply competition under quality scores: Motivations, information sharing and credibility," International Journal of Production Economics, Elsevier, vol. 226(C).
    4. Basu, Arnab & Jain, Tarun & Hazra, Jishnu, 2018. "Supplier selection under production learning and process improvements," International Journal of Production Economics, Elsevier, vol. 204(C), pages 411-420.
    5. Qian, Cheng & Anderson, Edward, 2020. "Buyer’s optimal information revelation strategy in procurement auctions," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1011-1025.
    6. Ruth Beer & Ignacio Rios & Daniela Saban, 2021. "Increased Transparency in Procurement: The Role of Peer Effects," Management Science, INFORMS, vol. 67(12), pages 7511-7534, December.
    7. Qian, Xiaohu & Fang, Shu-Cherng & Huang, Min & Wang, Xingwei, 2019. "Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement," Energy, Elsevier, vol. 177(C), pages 276-292.
    8. Daniel Montanera & Abhay Nath Mishra & T. S. Raghu, 2022. "Mitigating Risk Selection in Healthcare Entitlement Programs: A Beneficiary-Level Competitive Bidding Approach," Information Systems Research, INFORMS, vol. 33(4), pages 1221-1247, December.
    9. Huang, Min & Qian, Xiaohu & Fang, Shu-Cherng & Wang, Xingwei, 2016. "Winner determination for risk aversion buyers in multi-attribute reverse auction," Omega, Elsevier, vol. 59(PB), pages 184-200.

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