Solving the Negative Earnings Dilemma of Multistage Bidding in Public Construction and Infrastructure Projects: A Game Theory–Based Approach

Abstract

With the tremendous increase in spending on public projects, contractors need to employ efficient and effective bidding strategies to cope with the competitive bidding environment. Usually, general contractors carry a portion of the work and subcontract other parts to eventually submit a holistic joint bid. This bidding setting is referred to as multistage bidding where subcontractors submit their quotations/bids to the general contractor, after which the general contractor submits a final joint bid for the whole project. In a multistage bidding environment, general contractors may be faced with an increase in the probability of negative or below normal profits. Despite previous research efforts for developing bidding models, there is a need for the extension of existing literature to tackle the multistage bidding environment, referred to hereinafter as multistage game (MSG). As such, the goal of this paper is to develop a bidding model for the MSG. The authors followed a multistep research methodology comprised of: (1) defining MSG in terms of game theory; (2) deriving a game-theoretic bid function for general contractors to determine the final joint bid to submit in MSG; and (3) developing a simulation model for MSG, using a data from 2,235 US public infrastructure projects. Results demonstrate that the new bid function gives general contractors a competitive advantage by avoiding the occurrence of negative profits in their part of the project. Also, results show a reduction in the occurrence and magnitude of the negative profits in relation to the final joint bids. This research significantly contributes to the body of knowledge by providing an innovative bid function for MSG. In addition, it offers substantial practical benefits for general contractors by providing a tool that facilitates dealing with the inherent complexity and uncertainties related to actual cost estimation within the MSG decision-making process.