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review and study of various countries’ SMS. A questionnaire was then designed to survey practitioners’ perception of the importance of the collated attributes. The developed questionnaires were sent to 420 randomly selected general building contractors who were registered with the Building Construction Authority (BCA) of Singapore(step 3).
In step 4, a review was done on selected award winning companies of the Annual Safety Performance Award (ASPA) documents to determine their best practices and SMS used within these organisations.
In step 5, preliminary interviews were conducted with three safety auditors to find out their auditing practices. A preliminary framework was developed (step 6) based on the literature review and the postal survey results(step 7). From the results a model to measure the effectiveness of SMS was invented (step 8). This model is based on multi-attribute value technique (MAVT) [17]. In step 8, all possible attributes to be incorporated into the model were identified through reviewing the CP79, checklists, tools and practices adopted in other countries.
In steps 9 and 10, the importance weights for the factors and attributes of the CSI framework were determined. For the first level factors and second level attributes, the weights were determined through Analytic Hierarchy Process (AHP) (step 9), where 30 industry experts were interviewed. Due to the relatively large amount of time needed to conduct AHP, and the large number of lower level attributes e4500T, the lower level attributes’ weights were determined based on 5-point Likert Scale (step 10), where 1 ? not important;3 ? neutral; and 5 ? very important/critical. Twelve industry experts were interviewed to obtain the importance weights.
A rating method was developed (step11) and verified by nine industry experts. Thereafter the model was tested through three site audits (step 12). Based on the feedback,minor improvements were made (step 14) before the final version was presented (step 15). The survey results (step7) had been reported in Teo et al. [18]. This paper focused on step 8 onwards, describing the development of the proposed model and its validation. 4. Model construction (step 8)
From the literature review and survey results [18], the many attributes affecting safety were found. These were structured into an MAVT model. The MAVT approach to solving problems with multiple attributes is to develop a scoring model, where each attribute is assigned a weight to reflect its importance, and each construction site is rated on a scale of 0–1 against all attributes. Thereafter, the weight is multiplied by
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the rating, and the product is summed for each alternative. The inputs to the model which need to be determined are as follows [17]: ? list of attributes that need to be evaluated; ? importance weights of attributes;
? the construction site’s rating for each attribute; and
? an aggregation rule, to determine the score of each alternative. 4.1. List of attributes
The attributes that contractors and their construction sites need to achieve in order to ensure high level of site safety were identified through literature review and their relevance tested in an industry wide survey [18]. The significantly important variables (identified through ttest) were input into the SPSS software and factor analysis was carried out, to ascertain if there is any further relationship among the many proposed safety strategies. Factor analysis is motivated by the fact that measured variables can sometimes be correlated in such a way that their correlation may be reconstructed by a smaller set of parameters, which could represent the underlying structure in a concise and interpretable form.
Fig. 1 3P+I Model
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Fig. 2. 3P + I hierarchical framework
The factor analysis produced four principal components, labelled as Policy Factor, Process Factor,Personnel Factor and Incentive Factor (3P + I). Each factor comprised several attributes. See Fig. 2 for the 3P+ I model.
The four factors and relevant attributes were organized into a hierarchy tree or value tree, where the goals at the top may be abstract, but lower down on the hierarchy, the goals are measurable, non-conflicting, coherent and logical (see Fig. 2). Higher level objectives are usually the decision maker’s objectives in global terms. These
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objectives need to be of the highest order and must collectively represent the decision maker’s total objectives. Each higher level objective is successively subdivided into twigs which are intermediate level objectives, and finally to lower level objectives. The value tree allows attributes to be presented in an orderly structure that helps in problem evaluation, and elicitation of importance weights for twigs.
In this study, the highest level objective in the hierarchy is known as a ‘factor’. The four factors are: policy; process; personnel and incentive (see Fig. 2,Level 1). Second level attributes were the significant attributes derived from the survey questionnaire, t-test and factor analysis. Each second level attribute was further opertionalised to lower level attributes until a measurable lowest level attribute was obtained. The finalised list contained 590 attributes and these make up the CSI checklist. 4.2. Importance weights of attributes
There is a need to make a distinction between what are essential and what are desirable attributes in the 3P+ I hierarchical framework which as mentioned earlier, contained 590 detailed attributes. This is because different attributes are of different importance with respect to site safety. It is therefore necessary to find out the degree of importance of each attribute by assigning them weights. The weight is important to decision makers because it expresses the importance of each attribute relative to the others. For those attributes being evaluated, a weight indicates what the decision makers are most concerned about in a quantitative way.
There are several conventions to follow in assigning weights to attributes [17]. One convention is that the final weight for each twig on the hierarchy tree is obtained by ‘multiplying through the tree’. The next convention is to normalise the weights, that is, to make weights sum to 1 at each level of the tree. This study adopted two methods to obtain the importance weights, using:
? Saaty’s [19] AHP for higher level attributes (levels 1 and 2). ? Likert Scale for lower level attributes (level 3 onwards).
4.2.1. Importance weights for higher level attributes using AHP (step 9) The questionnaire to obtain the first and second level weights using AHP. The weights of the four factors (Policy, Process, Personnel and Incentives) make up the first level weights. The second level weights are the 17 sub-factors of the 3P + I model (see Fig. 2).
The questionnaire consists of five sections. They are (1) factors relating to site safety through policy, process, personnel and incentive aspects (level one weights); (2) factors relating to site safety through policy aspect (level two weights); (3) factors