Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/175774
Title: MULTI-ATTRIBUTE DECISION MAKING MODEL FOR EVALUATION AND SELECTION OF CONSULTANTS FOR DESIGN-AND-BUILD PROJECTS IN SINGAPORE
Authors: LING YEAN YNG
Issue Date: 1999
Citation: LING YEAN YNG (1999). MULTI-ATTRIBUTE DECISION MAKING MODEL FOR EVALUATION AND SELECTION OF CONSULTANTS FOR DESIGN-AND-BUILD PROJECTS IN SINGAPORE. ScholarBank@NUS Repository.
Abstract: As the design-and-build (DB) contractual arrangement becomes more widely used in Singapore, contractors who are not using in-house designers will increasingly need to select design consultants (architects and engineers) to help them to discharge their duty of providing the design for the DB project. Currently, there is no structured method available for contractors to select consultants. The rationale of this study is to construct a model, called the Consultant Selection Model, to assist contractors to select consultants for DB projects. The specific purposes of the research are to identify the criteria which DB contractors consider when making consultant selection decisions, to ascertain the relative importance of the criteria identified, and to isolate essential criteria which consultants must meet in order to be suitable for engagement for DB projects. From a literature survey and interviews with several DB contractors, 40 attributes that may be used for evaluating consultants were identified. A major survey of DB contractors, preceded by a pilot survey, was conducted to gauge whether they felt that the attributes identified were important for carrying out the design function in DB projects. Data were collected via mailed standard questionnaire. Respondents were selected from contractors who registered with the Construction Industry Development Board and who have DB experience. The questionnaire requested respondents to indicate, on a five-point scale, the importance of various attributes listed in the questionnaire. Another survey was conducted to find out the consultants' reactions to these 40 attributes. Data obtained from the survey were processed and the various attributes were categorised into 'important' and 'not important' using inferential statistics. The principal finding is that 34 of the 40 attributes identified are important for consultant selection. These 34 attributes were used to construct the Consultant Selection Model. The viii conclusion of this study is that consultants who are selected to undertake DB projects need to maintain 'hard skills', possess 'soft skills', quote low fees, and possess 'network' attributes. 'Hard skills' enable consultants to undertake task performance, which is the consultants' proficiency in performing their job-specific tasks. The organisational psychology literature suggests that these skills include 'general mental ability', 'job knowledge', 'task proficiency', and 'job experience'. In order to perform competently, consultants also need to possess a set of 'soft skills'. These 'soft skills' are important because consultants interact in an organisational setting instead of working by themselves, and therefore need to communicate with one another, coordinate actions, follow instructions, and occasionally go beyond their job descriptions. These skills comprised 'conscientiousness', 'initiative', 'social skills', 'controllability' and 'commitment'. Besides their quality derived from their 'hard' and 'soft'skills. consultants are also selected based on the competitiveness of the fees that they charge. In addition, DB contractors also consider 'network factor' comprising 'prior relationship', 'ongoing relationship' and 'reputation', when selecting consultants. It is recommended that contractors who wish to undertake DB projects and need to engage external consultants use the Consultant Selection Model to select consultants for their DB projects so that the 'right' consultant will be selected, and the chances of project success will be higher.
URI: https://scholarbank.nus.edu.sg/handle/10635/175774
Appears in Collections:Ph.D Theses (Restricted)

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