Identifying the critical success factors for target cost contracts in the construction industry

Abstract

Purpose – The paper aims to present a succinct review of guaranteed maximum price (GMP) and target cost contracting (TCC) concepts and features in general, and to identify the critical success factors for procuring GMP/TCC contracts from the Hong Kong perspective in particular. Design/methodology/approach – By means of an empirical questionnaire survey geared towards industrial practitioners with direct hands-on GMP/TCC experience, the opinions of various contracting parties including clients, consultants and contractors were solicited, analysed and compared in relation to GMP/TCC success factors. Findings – Experienced practitioners shared the unanimous perception that: reasonable share of cost saving and fair risk allocation; partnering spirit from all contracting parties; right selection of project team; well-defined scope of work in client's project brief and early involvement of contractor in design development, are the most essential ingredients for the successful implementation of GMP/TCC scheme. Research limitations/implications – Although the research study is based in Hong Kong with a limited sample size, the survey findings and hands-on experience of the relevant industrial practitioners may be cross-referenced to other similar investigations in other parts of the world for international comparisons. Originality/value – The research study has provided some useful insights into assisting key project stakeholders in determining important successful ingredients when launching GMP/TCC scheme. Such an identification of critical success factors would be valuable in formulating effective practical strategies to improve overall project performance, create win-win opportunities for contracting parties and mitigate the occurrence of construction disputes/claims. It also attempts to seek more research evidence to capture the levels of success and lessons learned from previous GMP/TCC construction projects for generating best practice recommendations for future implementation.