Start-up and steady-state learning in recurrent bidding

Abstract

Is contractors' bidding competitiveness under the governance of the adaptive learning mode? The adaptive learning model suggests contractor organizations would regulate the use of a bidding strategy, which is deemed optimal in response to recurring and similar bidding situations. A data set on open tendering by a select group of contractors was gathered over six years by the Hong Kong government and used to test this premise. The behaviour of the eight newly listed contractors indicates that an upward trend of bidding competitiveness in initial bidding attempts is not a generic phenomenon. The theoretical construct of bidding behaviour in the start-up phase is therefore dubious. Data lend only partial support to the existence of rapid learning during the start-up phase. However, the five more experienced contractors (indicated by the largest number of bidding attempts) show high and consistent bidding competitiveness. This provides evidence that contractors display behavioural regularity when the optimal bidding strategy has been reached. Construction organizations are, therefore, urged to treat organizational learning strategically in their attempts to maintain high competitiveness in the bidding process.