Unveiling relationships between contractor inputs and performance outputs
Purpose ‐ The purpose of this paper is to unveil any underlying relationships between contractor inputs and performance outputs. The outcome of the reported study is intended to help identify the inputs, which have more significant impacts on contractor performance
outputs and therefore, help formulate more reliable "upfront" (ex ante ) performance assessment criteria, hence improving approaches to the contractor-selection process. Design/methodology/approach ‐ A case study was conducted on the Performance
Assessment Scoring System (PASS) of a large public client in Hong Kong to determine the Pearson product-moment correlation between the scores of various input assessments and output assessments. Findings ‐ The findings revealed relationships between some of the input
assessment scores and the output results. Emerging as positive, all the discerned relationships confirmed that better outputs did in fact relate well to better inputs. Research limitations/implications ‐ The PASS system is designed to be very objective, hence the
criteria and assessment of inputs may be restricted to easily measurable items. The sample size obtainable was small, but still considered to be adequate for this initial study. Practical implications ‐ Construction clients could choose to improve their contractor
selection processes by identifying and incorporating contractor input factors that are seen to influence performance outputs. Contractors can also improve their outputs by focusing on the identified critical inputs. Originality/value ‐ Few studies have sought to discern
relationships between contractor inputs and their performance outputs through a quantitative approach. This case study provided a methodology, incorporating a statistics-based approach along with examples and explanations of how inputs can influence contractor outputs.
Keywords: Construction industry; Contractor selection; Hong Kong; Performance assessment; Performance criteria; Performance prediction
Document Type: Research Article
Publication date: 13 January 2012
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