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Table 1. Contributing factors to sustainability aspects of CDWM.

Table 1 Contributing factors to sustainability aspects of CDWM.

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Figure 1. The conceptual framework for sustainable construction and demolition waste management.
Figure 1. The conceptual framework for sustainable construction and demolition waste management.
Figure 2. Research process.
Figure 2. Research process.
* Reciprocals are not used as all categories/factors are merely compared with the most important category /factor. Table 2. EAHP pairwise comparison scale.
* Reciprocals are not used as all categories/factors are merely compared with the most important category /factor. Table 2. EAHP pairwise comparison scale.
Table 3. Profile of respondents. 4.3. Primary Data Tests
Table 3. Profile of respondents. 4.3. Primary Data Tests
Table 4. Global weight and ranking of each factor. 5.2. Measurement Model Assessment
Table 4. Global weight and ranking of each factor. 5.2. Measurement Model Assessment
Table 5. Result of the measurement model. Discriminant validity was evaluated in the last step of measurement model assessment. As recommended by Hair Jr, Hult, Ringle and Sarstedt [103], heterotrait-monotrait ratio (HTMT) is the most vigorous and effective test for discriminant validity assessment, which was conventionally assessed through Cross-Loadings and Fornell-Larcker criterion tests. Complete bootstrapping with 5000 iterations was used to determine the HTMT values [106]. The results for HTMT are represented in Table 6. As HTMT values were below 0.85, the results of discriminant validity were accepted [106].
Table 5. Result of the measurement model. Discriminant validity was evaluated in the last step of measurement model assessment. As recommended by Hair Jr, Hult, Ringle and Sarstedt [103], heterotrait-monotrait ratio (HTMT) is the most vigorous and effective test for discriminant validity assessment, which was conventionally assessed through Cross-Loadings and Fornell-Larcker criterion tests. Complete bootstrapping with 5000 iterations was used to determine the HTMT values [106]. The results for HTMT are represented in Table 6. As HTMT values were below 0.85, the results of discriminant validity were accepted [106].
Table 6. Discriminant validity of SCDWM-HTMT. 5.3. Structural Model Assessment
Table 6. Discriminant validity of SCDWM-HTMT. 5.3. Structural Model Assessment
Figure 3. Measurement model evaluation. In the final step, Stone—Geisser’s (Q*), which defines as the predominant measure of predictive relevance that postulates a model to properly predict each endogenous latent construct’s indices was calculated. Thus, blindfolding process in SmartPLS was run. Since, the sample size was N = 63, a D value of 4 was chosen since the remainder of the ‘63’ divide by ‘4’ is different from zero. A blindfolding process applies only to endogenous latent constructs [75,103,108]. Table 7 summarizes the results for collinearity assessment, effect size (f*), hypotheses testing, and predictive relevance (Q*) assessment.
Figure 3. Measurement model evaluation. In the final step, Stone—Geisser’s (Q*), which defines as the predominant measure of predictive relevance that postulates a model to properly predict each endogenous latent construct’s indices was calculated. Thus, blindfolding process in SmartPLS was run. Since, the sample size was N = 63, a D value of 4 was chosen since the remainder of the ‘63’ divide by ‘4’ is different from zero. A blindfolding process applies only to endogenous latent constructs [75,103,108]. Table 7 summarizes the results for collinearity assessment, effect size (f*), hypotheses testing, and predictive relevance (Q*) assessment.
Please select the most important category, and the most important factor in each category from Table below.
Please select the most important category, and the most important factor in each category from Table below.
(1) = Not important, (2) = Slightly important, (3) = Important, (4) = Fairly important, (5) = Very important
(1) = Not important, (2) = Slightly important, (3) = Important, (4) = Fairly important, (5) = Very important
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