2.2 MÉTODOS
2.2.1 ESTUDIO AUDIOVESTIBULAR
In this case study, potential statistical relationships between the ‘input’ factors and ‘output’ factors from a sample of public housing construction projects, were tested and identified. Correlation analyses between various Input and Output scores helped to identify the more ‘significant’ (and potentially critical) factors. The input assessment results do indicate some significant correlations with output scores. Also many of the ‘input’ factors showed some correlations with some ‘output’ factors. It is therefore hypothesised that these ‘input’ factors can be used, possibly among others as will be ascertained in follow-up work, as performance assessment criteria. These could then also be used for predicting future performance as well.
However, this part of the study does have some limitations and needs more work before the final performance predicting criteria can be reliably identified. Firstly, the initial study was only based on statistical analyses. Also, while the assessment period of the available data was from Sep. 2003 to Sep. 2006, the current PASS manual was published in Jan. 2007. However, the changes were checked and found to not affect
Table 6. Correlations between Output and Input Assessment Scores
WS SW AI AF IA1 IA2 IA3 IA4
WS Pearson Correlation 1 .314 .211 .240 .009 .127 .151 .192 Sig. (2-tailed) .075 .238 .294 .962 .480 .400 .283 N 33 33 21 33 33 33 33 SW Pearson Correlation 1 .259 .383 .155 .179 .262 .121 Sig. (2-tailed) .117 .065 .351 .282 .112 .471 N 38 24 38 38 38 38 AI Pearson Correlation 1 .341 -.217 -.022 -.078 -.135 Sig. (2-tailed) .103 .191 .895 .643 .418 N 24 38 38 38 38 AF Pearson Correlation 1 -.032 -.148 -.006 -.087 Sig. (2-tailed) .881 .489 .977 .687 N 24 24 24 24
IA1 Pearson Correlation 1 .390(*) .411(*) .328(*)
Sig. (2-tailed) .015 .010 .045
N 38 38 38
IA2 Pearson Correlation 1 .142 .187
Sig. (2-tailed) .394 .261
N 38 38
IA3 Pearson Correlation
1 .725(**
)
Sig. (2-tailed) .000
N 38
IA4 Pearson Correlation 1
Sig. (2-tailed)
N
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
areas studied significantly; hence the results are still valid. From a broader perspective, the study to date has been more quantitative, while there are many other factors that can affect the scores, as well as the explanations of the results. Secondly, the research was based on a single system adopted in Hong Kong by one client, albeit a major player in the Hong Kong construction industry. More case studies should be conducted and/or opinions solicited via questionnaire, interviews and/or a focus group.
Apart from the above limitations, the completion of this case study is the first major step in the first author’s research. The results from this case study, together with the
information gained from preliminary interviews and literature reviews, will be used to generate a questionnaire survey, which will be sent to clients, contractors and consultants in the Hong Kong. Next, the consolidated results will be used to formulate a framework for a contractor selection decision support system that incorporates more reliable and consolidated past performance information. The system will provide more information about contractors as well as their past project performance to facilitate clients’ decisions during the contractor prequalification and tender selection processes.
6
Acknowledgement
The support of Grant HKU7138/05E from the Hong Kong Research Grants Council is gratefully acknowledged, as is the kind co-operation of the Hong Kong Housing Authority in sharing their expertise and experiences in this domain, together with their data (which were of course ‘blinded’ in terms of contractor and project names, since only needed for independent analyses).
7
References
Alarcon, L. F. & Mourgues, C. (2002) Performance modeling for contractor selection. Journal of Management in Engineering, 18, pp 52-60.
Arslan, G., Kivrak, S., Birgonul, M. T. & Dikmen, I. (2008) Improving sub-contractor selection process in construction projects: Web-based sub-contractor evaluation system (WEBSES). Automation in Construction, 17, pp 480-488.
Assaf, S. & Jannadi, M. O. (1994) Multi-criterion decision-making model for contractor prequalification selection. Building Research and Information, 22, pp 332-335. Battikha, M. G. (2002) QUALICON: Computer-based system for construction quality
management. Journal of Construction Engineering and Management, 128, pp 164- 173.
Construction Quality Assessment System-Conquas, Building and Construction Authority, Singapore. viewed: 20/5/08.
Cheng, E. W. L. & Li, H. (2004) Contractor selection using the analytic network process. Construction Management and Economics, 22, pp 1021-1032.
Contractor Power
Contract information system by Statistics Canada.
COMIS, Housing Authority, Housing Department, Hong Kong, Hong Kong SAR
Government.
20/5/08.
CPARS (Contractor Performance Assessment Reporting System) by NASA
Crowley, L. G. & Hancher, D. E. (1995) Evaluation of competitive bids. Journal of Construction Engineering and Management, 121, pp 238-245.
El Wardani, M. A., Messner, J. I. & Horman, M. J. (2006) Comparing procurement methods for design-build projects. Journal of Construction Engineering and Management, 132, pp 230-238.
Hatush, Z. & Skitmore, M. (1998) Contractor selection using multicriteria utility theory: An additive model. Building and Environment, 33, pp 105-115.
Performance Assessment Scoring System(PASS), Housing Authority, Housing Department, Hong Kong, Hong Kong SAR Government. http://sc.housingauthority.gov.hk/gb/www.housingauthority.gov.hk/en/businesspartn ers/buildingpass/0,,,00.html, viewed: 20/5/08.
Holt, G. D., Olomolaiye, P. O. & Harris, F. C. (1994) Evaluating performance potential in the selection of construction contractors. Engineering, Construction and Architectural Management, 1, 21
Kumaraswamy, M. M. (1996) Contractor evaluation and selection: a Hong Kong perspective. Building and Environment, 31, pp 273-282.
Kumaraswamy, M. (2006) Exploring the legal aspects of relational contracting. Journal of Professional Issues in Engineering Education and Practice, 132, pp 42-43.
Kumaraswamy, M., Rahman, M., Palaneeswaran, E., Ugwu, O., Anvuur, A. & Yogeswaran, K. (2007) Multi-Disciplinary and Multi-Functional Teams in PPP Procurement and Delivery. Symposium: Building Across Borders Built Environment Procurement, CIB W092 Procurement Systems. Hunter Valley, Australia, 23rd-26th Sep. 2007, pp 250-262.
Li, Y., Chen, S. & Nie, X. (2005) Fuzzy pattern recognition approach to construction contractor selection. Fuzzy Optimization and Decision Making, 4, pp 103-118. Ng, S. T., Palaneeswaran, E. & Kumaraswamy, M. M. (2002) A dynamic e-Reporting
system for contractor's performance appraisal. Advances in Engineering Software, 33, pp 339-349.
Palaneeswaran, E., Kumaraswamy, M., Ng, T. & Lam, K. (2007) Reinforcing Lowest Price-Based Contractor Selections – A Reinvention Perspective. Symposium: Building Across Borders Built Environment Procurement, CIB W092 Procurement Systems. Hunter Valley, Australia, 23rd-26th Sep. 2007, pp 192-200.
Prascevic, Z. & Petrovic-Lazarevic, S. (1996) Probabilistic-possibilistic approach to the optimal selection of a contractor. Yugoslav Journal of Operations Research, 6, pp 129-136.
Russell, J. S. & Skibniewski, M. J. (1988) Decision Criteria in Contractor Prequalification. Journal of Management in Engineering, 4, 16.
Russell, J. S. & Skibniewski, M. J. (1990) QUALIFIER-1: contractor prequalification model Journal of computing in civil engineering 4, 13.
Russell, J. S., Hancher, D. E. & Skibniewski, M. J. (1992) Contractor prequalification data for construction owners. Construction Management & Economics. Routledge. Singh, D. & Tiong, R. L. K. (2005) A fuzzy decision framework for contractor
selection. Journal of Construction Engineering and Management, 131, pp 62-70. Singh, D. & Tiong, R. L. K. (2006) Contractor selection criteria: Investigation of
opinions of Singapore construction practitioners. Journal of Construction Engineering and Management, 132, pp 998-1008.
Works Branch, 2004, Tender Evaluation of Works Contracts, ETWB Technical Circular (Works) No. 8/2004, Hong Kong SAR Government.
Works Branch, 2007, Contractors’ Performance Index System, ETWB Technical Circular (Works) No. 3/2007, Hong Kong SAR Government.
Yasamis, F. (1999) Contractor quality performance (CQP) evaluation model in pavement projects. PhD Thesis, Illinois Institute of Technology.