Contractor management performance evaluation model at prequalification stage

Pooria Rashvand, Muhd Zaimi Abd Majid, Jeffrey K. Pinto

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

The client's choice of contractor at the project prequalification stage is critical as the selected contractor is responsible for the overall coordination of a project. Although a wide variety of criteria have been employed to develop models for contractor selection, one critical component of their capability - management performance - has historically been among the least addressed but, we argue, most critical elements in a useful selection model. Even in the cases when management performance is considered, it has typically focused only on objective outcome criteria of limited predictive power, such as contractor time and cost performance. This paper reports on the results of a study to develop a comprehensive contractor evaluation model that directly addresses the contractor management capabilities and practices as a critical additional element at the prequalification stage. Using a multi-step research process, including extensive literature review, analysis and synthesis of critical contractor management performance factors, development of an analytic network project (ANP) model, and subsequent case study and field validation, we identified a robust model that enhances the decision support tools for the client organization by creating databases for comparing multiple contractors with respect to management practices. In addition, the evaluation of this prequalification criterion permits the contractors to focus on their efforts to address the critical management practices to improve their project performance.

Original languageEnglish (US)
Pages (from-to)5087-5101
Number of pages15
JournalExpert Systems With Applications
Volume42
Issue number12
DOIs
StatePublished - Jul 15 2015

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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