Prediction models of international tender results for formulating an innovative construction bidding strategy

Abstract

The global construction industry is a large, fragmented and competitive industry with relatively low margins. When contractors are bidding for construction projects, where the lowest bidders are awarded the projects, there are several factors, which influence a contractor's chances of winning. What exactly are these factors and to what extent do they affect a contractor's chances of winning? To answer these questions, this research creates an empirical dataset of 858 public tenders in 95 countries in 2013-2019. A series of statistical analyses, including multivariate regressions, robustness checks with control variables and machine learning (Lasso), are performed, and three different empirical models are established each with an accuracy of around 90% for predicting winners. A bidder's busyness in other works, experience in the tender country and project type, level of internationalization and age are found to be the factors influencing the bidder's chances of winning the tenders. Contractors can utilize the results of this research in taking two crucial decisions, bid/no-bid and markup size decisions, to prevent a loss of opportunity, save resources, increase their winning probability and profits.