## 4 Results  
### Confirmatory Factor Analysis Results  
SPSS software was used to analyze the data from 310 questionnaires. The overall reliability and validity of the 30-item questionnaire were analyzed. [PERSON]'s test for sphericity (\(p=0.000<0.05\)) and the Kaiser-Meyer-Olkin (KMO) measure value (=0.982 \(>\) 0.6) were calculated, showing that the sample size was adequate [65], as shown in Table 6. All values satisfied the minimum acceptable score proposed by [PERSON] [66]. For correlation between \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Item** & **Frequency** & **\%** \\ \hline \multirow{3}{*}{Gender} & Male & 147 & 47.42 \\ \cline{2-4}  & Female & 163 & 52.58 \\ \hline \multirow{3}{*}{Age} & 20–24 & 52 & 16.77 \\ \cline{2-4}  & 25–30 & 213 & 68.7 \\ \cline{2-4}  & 31–35 & 34 & 10.97 \\ \cline{2-4}  & 36–45 & 11 & 3.56 \\ \hline \multirow{3}{*}{Experience using mid-air gestures} & Used & 155 & 50 \\ \cline{2-4}  & Have not used & 155 & 50 \\ \hline Sum & & 310 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Demographic profile for respondents (N = 310). Figure 2: Seven types of mid-air gesture types. variables, factor analysis was considered valid and appropriate. The results indicated that the study data were suitable for factor analysis [67]. Confirmatory factor analysis was carried out to test the convergent validity, correlation, discriminant validity, and fit validity of each item in the scale proposed in this paper. Confirmatory factory analysis is a research method used to test whether the relationship between a factor and the corresponding measure item conforms to the theoretical relationship designed by the researcher and can be used for the scale analysis of questionnaires. Standard load coefficient values are commonly used to represent the correlation between factors and analysis items. The criteria proposed by [PERSON] and [PERSON] [68] were used to assess the convergence effectiveness of measurements. Their guidelines suggest a strong correlation when the statistical significance level \(p<0.05\) is greater than 0.6 for all standardized factor loads for calculated observation variables. In this study, the square root value of average variance extracted (AVE) was used to test the discriminant validity among factors, as shown in Table 7. The AVE and composite reliability (CR) values should be greater than 0.5 and 0.7, respectively. Table 7 is the result of validation analysis, and it is clear this scale has good convergence validity. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **KMO and Bartlett’s Test** & \\ \hline KM0 & & 0.982 \\ \hline  & Approx. Chi-Square & 8088.676 \\ Bartlett’s Test of Sphericity & df & 435.000 \\  & \(p\) & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: KMO and Bartlett’s tests. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Variable** & **Std. Estimate** & **AVE** & **CR** \\ \hline  & Standard value & \(>\)0.6 & \(>\)0.5 & \(>\)0.7 \\ \hline  & a1 Learnability & 0.824 & & \\ \cline{2-4}  & a2 Ease of remembering & 0.763 & & \\ \cline{2-4}  & a3 Usefulness & 0.788 & & \\ \cline{2-4}  & a4 Generality & 0.732 & 0.607 & 0.903 \\ \cline{2-4}  & a5 Continuity & 0.788 & & \\ \cline{2-4}  & a6 Universal & 0.776 & & \\ \hline  & b1 Tolerance & 0.738 & & \\ \cline{2-4}  & b2 Mis-operation & 0.802 & & \\ \cline{2-4}  & b3 Guidance & 0.831 & & \\ \cline{2-4}  & b4 Operational feedback & 0.823 & 0.646 & 0.903 \\ \cline{2-4}  & b5 Controllability & 0.801 & & \\ \cline{2-4}  & b6 Extensibility & 0.8 & & \\ \cline{2-4}  & b7 Helpful & 0.828 & & \\ \hline  & c1 Easy to identify & 0.808 & & \\ \cline{2-4}  & c2 Easy to trigger & 0.79 & & \\ \cline{2-4}  & c3 Accuracy & 0.803 & & \\ \cline{2-4}  & c4 Fluency & 0.82 & & \\ \cline{2-4}  & c5 Fast recognition & 0.814 & & \\ \cline{2-4}  & c6 High success rate & 0.806 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Load factor table. The model fit index was used for the overall model fit case.