## 3 Research Purpose and Methodology
### Ordinal Logistic Regression Model  
Ordinal logistic regression was applied to develop the prediction model in Minitab. Ordinal logistic regression was used to predict an ordinal dependent variable given one or more independent variables [32]. For this research, the technology utilization level was regarded as the ordinal dependent variable (i.e., multi-class ordered variables), which included levels from 1 to 4; company size, company type, working position, and working experience were regarded as categorical independent variables (i.e., multi-class unordered variables), and the categories for each variable are presented in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Category** & **Label** \\ \hline \multirow{4}{*}{Company type} & Construction company & CT1 \\  & Component manufacturer & CT2 \\  & Developer & CT3 \\  & Consultant organization & CT4 \\ \hline \multirow{4}{*}{(annual avenue in \$USD million dollars 1)} & \textless{}100 (i.e., small) & CS1 \\  & 100 to 1000 (i.e., medium) & CS2 \\  & \textgreater{}1000 (i.e., large) & CS3 \\ \hline \multirow{4}{*}{Working position} & Engineering and Designing & WP1 \\  & Administration & WP2 \\  & Project management & WP3 \\ \hline \multirow{4}{*}{Working experience (in years)} & \textless{}5 (i.e., junior) & WE1 \\  & 5 to15 (i.e., medium) & WE2 \\ \textgreater{}25 (i.e., senior) & WE3 \\ \hline \hline \end{tabular}  
* The money is transferred into Chinese currency (i.e., RMB) according to the exchange rate when distributing the survey in China.  
\end{table} 
Table 1: Predictor variables and corresponding groups. Assuming a dependent variable has \(j\) levels, and there are \(M\) independent variables, the mathematical formulation of the ordinal logistic regression model is given below:  
\[Logit_{j}\left(P(Y\leq j)\right)\ =\ ln\left(\frac{P\left(Y\leq j\right)}{P\left(Y>j \right)}\right)\ =\ \alpha_{j}-\sum_{i=1}^{M}\beta_{i}X_{i} \tag{1}\]  
In Formula (1), \(Logit(P(Y\leq j))\) is the logarithmic link function of the probability of occurrence of an event when dependent variable \(Y\)'s value is no larger than \(j\). \(\alpha_{j}\) represents the intercept of the corresponding regression model. \(\beta_{i}\) represents the regression coefficient of the corresponding independent variable \(X_{i}\). For the categorical independent variable, the first category is regarded as the reference category. Regression coefficients of the other categories indicate their influence on \(Y\) compared to the reference category. Specifically, positive coefficients indicate that the first level of the dependent variable (i.e., technology utilization level 1) is more likely to happen at this category of \(X_{i}\) than at the reference category. Negative coefficients indicate that the last level of the dependent variable (i.e., technology utilization level 4) is more likely to happen at this category of \(X_{i}\) than at the reference category. The four independent variables involved in this article are all unmeasurable categorical variables. Since the categorical variable cannot be directly input into the regression model like a numerical variable, it is necessary to convert the four categorical variables into a dummy variable. Each category of the independent variable \(X_{i}\) is encoded in a 0/1 form, where 1 refers to presence and 0 refers to absence. Based on Formula (1), the probability of the event when dependent variable \(Y\)'s level is no larger than \(j\) can be obtained through Formula (2). The probability of each level of \(Y\) can be obtained in Formula (3). Then, the model will assign the sample's \(Y\) value as the level with the largest probability:  
\[P(Y\leq j)\ =\ \frac{e^{\alpha_{j}+\sum_{i=1}^{M}\beta_{i}X_{i}}}{1+e^{\alpha_ {j}+\sum_{i=1}^{M}\beta_{i}X_{i}}} \tag{2}\]  
\[\text{P}(\text{Y}\ =\ \text{j})\ =\ \left\{ \begin{array}{rl}P(Y\leq 1),\ j\ =\ 1\\ P(Y\leq j)-P(Y\leq j-1),\ j\ =\ 2,\ \ldots,\ J-1\\ 1-P(Y\leq J-1),\ j\ =\ J\end{array} \right. \tag{3}\]  
Then, it was necessary to evaluate the fitness of the model.