Developing a partnering performance index (PPI) for construction projects : a Fuzzy Set Theory approach

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Developing a partnering performance index (PPI) for construction projects : a Fuzzy Set Theory approach

 

Author: Yeung, Fai-yip
Title: Developing a partnering performance index (PPI) for construction projects : a Fuzzy Set Theory approach
Degree: Ph.D.
Year: 2007
Subject: Hong Kong Polytechnic University -- Dissertations.
Construction industry -- Management.
Partnership.
Project management.
Participatory monitoring and evaluation (Project management)
Fuzzy sets.
Department: Dept. of Building and Real Estate
Pages: xxv, 345 leaves : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2165726
URI: http://theses.lib.polyu.edu.hk/handle/200/2175
Abstract: Research into performance measures for partnering projects in construction becomes vital because an increasing trend of client organisations has been observed to adopt partnering approach to their building and construction projects worldwide over the last decade. However, few, if any, comprehensive and systematic research studies focus on developing a comprehensive, objective, reliable and practical performance evaluation model for partnering projects. The aim of this research study is to develop a model using the Delphi Survey Technique and the Fuzzy Set Theory for objectively, reliably and practically measuring the partnering performance of construction projects in Hong Kong. Based on a consolidated conceptual framework encompassing 25 performance measures for partnering projects developed from literature review, a Partnering Performance Index (PPI), which is composed of seven weighted Key Performance Indicators (KPIs), has been generated by conducting 4 rounds of Delphi questionnaire survey with 31 construction experts in Hong Kong. The seven most important weighted KPIs were: (1) Time Performance, with the weighting of 0.167; (2) Cost Performance, with the weighting of 0.160; (3) Top Management Commitment, with the weighting of 0.150; (4) Quality Performance, with the weighting of 0.143; (5) Trust and Respect Performance, with the weighting of 0.143; (6) Effective Communications Performance, with the weighting of 0.131; and (7) Innovation and Improvement, with the weighting of 0.106. The weighting for each of the seven selected KPIs is calculated by the mean ratings of a particular KPI divided by the summation of the mean ratings of all the selected KPIs. The PPI can assist in developing a benchmark for measuring the partnering performance of construction projects in Hong Kong. However, it is likely that different assessors may have their own semantic interpretation on each KPI. In order to avoid any discrepancies in interpreting the meaning of each KPI and provide objective evaluation result based on quantitative evidences, a set of Quantitative Indicators (QIs) has been established by firstly conducting 5 structured face-to-face interviews with leading industrial practitioners in Hong Kong and subsequently 2 rounds of Delphi questionnaire survey with the same group of panel experts in Hong Kong. The QIs identified with the highest mean ratings for each of the seven most important weighted KPIs were respectively found to be: (1) 'Variation of Actual Completion Time Expressed as a Percentage of Finally Agreed Completion Time'; (2) 'Variation of Actual Project Cost Expressed as a Percentage of Finally Agreed Project Cost'; (3) 'Percentage of Top Management Attendance in Partnering Meetings'; (4) 'Average Number of Non-conformance Reports Generated Per Month'; (5) 'Perceived Key Stakeholders' Satisfaction Scores on Trust and Respect Performance by Using a 10-point Likert Scale'; (6) 'Perceived Key Stakeholders' Satisfaction Scores on Effective Communications Performance by Using a 10-point Likert Scale'; and (7) 'Cost Saving Resulted from Innovation Expressed as a Percentage of Total Project Cost'. By incorporating the QIs into the evaluation process, different assessors could perform their evaluation process based on quantitative evidences. However, the establishment of a set of QIs cannot fully tackle the subjectivity of performance evaluation. For the sake of rectifying this deficiency, this research study has further applied a Fuzzy Set Theory approach through conducting an empirical questionnaire survey with the same group of panel experts in Hong Kong to establish a well-defined range of Quantitative Requirements (QRs) for each QI measured at five different performance levels, namely, 'poor', 'average', 'good', 'very good', and 'excellent'. By using the Modified Horizontal Approach, Fuzzy Membership Functions (FMFs) have been constructed through Constrained Regression Line with the Bisector Error Method. The proposed performance evaluation model is not only novel in nature but it can also improve the objectivity, reliability and practicality of performance evaluation for partnering projects.

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