## 4 Results
### Model Evaluation  
As presented in Tables 3 and 5, seven models have been selected to predict the utilization level of different technology types of the practitioners in the United States and China. Then, an external test dataset consisting of 16 cases were selected to evaluate the accuracy of the developed prediction models, which included 8 from the United States and 8 from China. The specific information of the cases and the corresponding prediction results are presented in Table 7. Six out of the seven models have the correctness ratio no less than 75%. It is noticeable that the prediction model of the utilization level of smart machinery in China has a correctness ratio of 100%. Only the model to predict the utilization level of smart machinery in the United States has a correctness ratio less than 75%. For those wrong predictions, the difference between the predicted value and actual value is 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **j = 1** & **j = 2** & **j = 3** \\ \hline
3D and nD model & \(-\)2.97 & \(-\)0.30 & 1.57 \\ Extended reality & \(-\)1.38 & 0.17 & 1.66 \\ Internet of Things & \(-\)0.90 & 0.63 & 2.08 \\ Smart Machinery & \(-\)1.17 & 0.43 & 2.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(\alpha_{j}\) value of different technology type for China.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{5}{c}{**Background**} & \multicolumn{5}{c}{**Predicted Technology Utilization Level**} \\ \cline{2-9}  & **Country** & **Company** & **Company** & **Working** & **Working** & **Working** & **3D and nD** & **Extended** & **Internet** & **Smart** \\  & **Type** & **Size** & **Position** & **Experience** & **Model** & **Reality** & **of Things** & **Machinery** \\ \hline
1 & U.S. & Manufacturer & Medium & Engineering & Junior & N/A & Low & High * & Medium \\ \hline
2 & U.S. & Developer & Small & Administration & Medium & N/A & Medium & High & Low \\ \hline
3 & U.S. & Construction & Small &
\begin{tabular}{c} Project \\ management \\ \end{tabular} & Junior & N/A & Low & High * & None \\ \hline
4 & U.S. & Construction & Medium & Engineering & Junior & N/A & Low & High & None \\ \hline
5 & U.S. & Manufacturer & Small & Engineering & Junior & N/A & Low * & Low & High \\ \hline
6 & U.S. & Manufacturer & Large &
\begin{tabular}{c} Project \\ management \\ \end{tabular} & Junior & N/A & Medium * & High & High \\ \hline
7 & U.S. & Consultant & Large & Administration & Medium & N/A & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 7: Application of the ordinal logistic regression model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Ordinal Logistic Regression Model** \\ \hline
3D and nD model & \(\begin{array}{c}Logit\left[P(Y\leq j)\right]\\ =\alpha_{j}-(-0.75)*CT2-0.57*CT3-(-1.23)*CT4-(-0.34)*CS2-(-0.09)*CS3 \\ \end{array}\) \\ \hline Extended reality & \(\begin{array}{c}Logit\left[P(Y\leq j)\right]\\ =\alpha_{j}-2.29*CT2-0.63*CT3-(-0.47)*CT4-(-1.21)*CS2-(-0.74)*CS3 \\ \end{array}\) \\ \hline Internet of Things & \(\begin{array}{c}[PERSON]\left[P(Y\leq j)\right]\\ =\alpha_{j}-0.52*CT2-0.84*CT3-(-0.40)*CT4-(-1.08)*CS2-(-1.09)*CS3-(-0.84)*WE2-(-1.36)*WE3 \\ \end{array}\) \\ \hline Smart Machinery & \(\begin{array}{c}[PERSON]\left[P(Y\leq j)\right]\\ =\alpha_{j}-1.23*CT2-1.57*CT3-(-0.10)*CT4-(-1.09)*CS2-(-0.28)*CS3-(-0.57)*WE2-(-0.38)*WE3 \\ \end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ordinal logistic regression model for China.