Construction procurement: Modelling bidders' learning in recurrent bidding

Construction remains a significant area of public expenditure. An understanding of the process of changes in construction pricing, and how the process can be manipulated through the release of bidding feedback information is vital, in order to best design clients' procurement policies. This paper aims to statistically model inexperienced individual bidders' learning in recurrent bidding under partial and full information feedback conditions. Using an experimental dataset, the developed linear mixed model contains three predictor variables, namely: time factor, information feedback conditions, and bidding success rate in the preceding round. The results show nonlinearity and curvature in the bidders' learning curves. They are generally less competitive in time periods after a winning bid with lower average bids submitted by those subjected to full information feedback condition. In addition, the model has captured the existence of heterogeneity across bidders with individual-specific parameter estimates that demonstrate the uniqueness of individual bidders' learning curves in recurrent bidding. The findings advocate for adequate bidding feedback information in clients' procurement design to facilitate learning among contractors, which may in turn lead to increased competitiveness in their bids.