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Multi‐Attribute Procurement Auctions in the Presence of Satisfaction Risk

He Huang¹; Liming Liu²; Geoffrey Parker³; Yinliang (Ricky) Tan⁴; Hongyan Xu¹

¹ School of Economics and Business Administration, Chongqing University, Chongqing 400044, China · ² Faculty of Business, Lingnan University, Hong Kong, China · ³ Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, 03755, USA · ⁴ A. B. Freeman School of Business, Tulane University, New Orleans, Louisiana, 70118, USA

Procurement auctions are widely used by governments and corporations to solicit bids for services and projects. Such auctions involve significant risk for the buyer, because the delivered quality is highly uncertain. We examine a multi‐attribute procurement auction combined with a performance‐based contract. In this setting, suppliers submit bids which include both price and promised quality. After the buyer awards the contract to the winning bidder with the highest score, the supplier exerts efforts to accomplish the project, and buyer satisfaction is randomly affected by both promised quality and effort. A performance‐contingent reward or penalty occurs upon project delivery. We show that bidders jointly optimize promised quality and effort before submitting a bid price. Depending upon the relative impacts from promised quality and effort on buyer’s satisfaction, the promised quality and execution effort can be complements or substitutes. Our analysis reveals that the information rent that the supplier gains depends on the relationship between promised quality and buyer satisfaction. Further, the optimal scoring rule distorts promised quality downwardly. We find that either reserve quality or price alone is insufficient to exclude undesirable bidders. Compared with efficient mechanism, the effort under optimal mechanism is distorted upwardly (downwardly) when it substitutes (complements) promised quality. We also find that the risk uncertainty can benefit both buyer and supplier, under certain conditions of an additive relationship between supplier’s behaviors and randomness, resulting in a Pareto improvement.

DOI

10.1111/poms.12979

Volume

28 (5)

Pages

1206-1221

Language

en

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