## 5 Results  
### Measurement Model Evaluation  
PLS-SEM develops a series of empirical test criteria to evaluate the reflective and formative measurement model respectively [21; 65]. In this research model, all the first order constructs of \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**Code** & **Item** & \multicolumn{4}{c}{**Interviewee**} & **Sum** & **Adjustment** \\ \hline  & & **1** & **2** & **3** & **4** & **5** & & \\ \hline
**BI** & Backward integration & & & & & & \\ b\_1 & Source control & \(\times\) & \(\times\) & & \(\times\) & 4 & & \\ b\_2 & Bring influence to the off-site process & & & & & 0 & Delete \\ b\_3 & Off-site process monitoring & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 5 & renumbered as bi\_2 \\ b\_4 & Transfer partial on-site activities into off-site, & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 5 & renumbered as bi\_3 \\ \hline
**FI** & Forward integration & & & & & & \\ f\_1 & Acceptance of ideas proposed by clients or end-users & \(\times\) & \(\times\) & \(\times\) & & \(\times\) & 4 & \\ f\_2 & Improvement based on ideas from clients or & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 5 & \\ f\_3 & Joint decision-making & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 4 & \\ f\_4 & Joint decision-driven implementation & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 4 & 4 & \\ \hline RL & Reverse logistics & & & & & & \\ r\_1 & Purchase permit of recycled materials & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 4 & \\ r\_2 & Recycle C\&D waste to the secondary market & \(\times\) & \(\times\) & \(\times\) & & \(\times\) & 4 & \\ r\_3 & Recycle C\&D waste to the raw material market & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Adjusted measurement Items of CSCI. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Personal Attribute** & **Categorization** & **Number of Respondents** & **Percentage (\%)** \\ \hline  & Contractor & 25 & 32.89 \\  & Owner & 32 & 42.11 \\ Organization type & Material Supplier & 4 & 5.27 \\  & Consulting & 6 & 7.89 \\  & Designer & 9 & 11.84 \\ \hline  & \textless{}1 million & 4 & 5.26 \\  & 1 million–10 million & 7 & 9.21 \\ Investment scale (RMB, the Chinese currency) & 10 million–100 million & 8 & 10.53 \\  & 100 million–1000 million & 26 & 34.21 \\  & 1000 million–1000 million & 24 & 31.58 \\  & \textgreater{}10,000 million & 7 & 9.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Respondent details. CSCI--i.e., BI, FI, RL, and CSCI itself--belong to the formative constructs. Table 6 shows the formative measurement model evaluation results. As shown in Table 6, the absolute indicator contribution to the constructs, i.e., indicator's weights, and their significance levels are given. Only the indicator weight of bi_1 to BI is nonsignificant. Other indicators' weights are significant. Another important criterion is the indicator's variance inflation factor (VIF), and the rule of thumb of VIF is lower than 5 generally or lower than 3.3 for the formative indicator [21]. In the measurement model, all the formative indicators' VIF values are lower than 3.3, which means strictly satisfying the requirement of multicollinearity. Meanwhile, an evaluation of the reflective indicators in the research model is required. For the reflective measurement model, the indicator reliability, internal consistency reliability, convergent validity, and discriminant validity should be evaluated [21; 66]. All the first order constructs of SUCM--i.e., BED, MPC, CST, and SUCM itself--belong to the reflective constructs. Table 7 provides the evaluation results of the indicator reliability, internal consistency reliability, and convergent validity. The indicator reliability can be evaluated by the factor loadings, which are empirically suggested to be more than 0.7 [21]. In the exploratory research, an acceptable minimum value of factor loading is 0.60 to 0.70.