# Perceptions and Resistance to Accept Smart Clothing:
## 4. Results
### Hypothesis Testing  
Multiple regression analysis was conducted to verify the effect of smart clothing properties on innovation resistance. As a result of the analysis, as shown in Table 3, the compatibility, relative advantages, and cost rationality of smart clothing did not significantly affect innovation resistance, so hypothesis 1, hypothesis 5, and hypothesis 6 were rejected. As for the perceived risk, the standardized regression coefficient was 0.219 (\(p<0.001\)), and for the unavailability, the standardized regression coefficient was 0.251 (\(p<0.01\)), indicating that it had a significant positive (+) effect on the innovation resistance, so hypothesis 3 and hypothesis 4 was supported. On the other hand, in the expectation of improvement, the standardized regression coefficient was \(-0.403\) (\(p<0.001\)), indicating that it had a negative (-) effect on the innovation resistance, and hypothesis 2 was rejected. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Variables** & **Mean** & **S.D.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Sex & 1.39 & 0.489 & 1 & & & & & & & & & \\ \hline Age & 3.856 & 1.108 & \(\begin{matrix}-0.277\\ ** \\ \end{matrix}\) & 1 & & & & & & & & & & & \\ \hline Perceived Compatibility & 3.009 & 0.723 & \(\begin{matrix}-0.153\\ ** \\ \end{matrix}\) & 0.117 * & 1 & & & & & & & & & \\ \hline Expectations of future product capabilities & 3.685 & 0.8222 & \(\begin{matrix}-0.135\\ *\end{matrix}\) & \(\begin{matrix}-0.059\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.436\\ **\\ \end{matrix}\) & 1 & & & & & & & & & \\ \hline Perceived Unavailability & 3.094 & 0.709 & 0.011 & \(\begin{matrix}-0.004\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.158\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.030\\ ** \\ \end{matrix}\) & 1 & & & & & & & & \\ \hline Perceived Risk & 3.177 & 0.616 & 0.106 & \(\begin{matrix}-0.164\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.223\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.052\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.533\\ **\\ \end{matrix}\) & 1 & & & & & & & \\ \hline Perceived Relative Advantage & 3.533 & 0.622 & \(\begin{matrix}-0.085\\ ** \\ \end{matrix}\) & 0.045 & \(\begin{matrix}0.683\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.422\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.207\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.202\\ ** \\ \end{matrix}\) & 1 & & & & & & \\ \hline Perceived Monetary Value & 2.356 & 0.618 & \(\begin{matrix}0.168\\ ** \\ \end{matrix}\) & 0.031 & \(\begin{matrix}0.277\\ ** \\ \end{matrix}\) & 0.058 & \(\begin{matrix}-0.066\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.020\\ ** \\ \end{matrix}\) & 1 & & & & & & \\ \hline Fashion Innovativeness & 2.595 & 0.787 & 0.060 & 0.012 & \(\begin{matrix}0.246\\ ** \\ \end{matrix}\) & 0.090 & \(\begin{matrix}-0.025\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.159\\ ** \\ \end{matrix}\) & 0.187 & 1 & & & & & \\ \hline Technological Innovativeness & 3.115 & 0.694 & \(\begin{matrix}-0.126\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.042\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.284\\ ** \\ \end{matrix}\) & 0.282 & \(\begin{matrix}0.041\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.030\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.191\\ ** \\ \end{matrix}\) & 0.026 & \(\begin{matrix}0.580\\ ** \\ \end{matrix}\) & 1 & & & & \\ \hline Innovation Resistance & 2.601 & 0.704 & \(\begin{matrix}0.168\\ ** \\ \end{matrix}\) & 0.048 & \(\begin{matrix}-0.332\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.457\\ ** \\ \end{matrix}\) & \(\begin{matrix}0.365\\ ** \\ \end{matrix}\) & 0.492 & \(\begin{matrix}-0.300\\ ** \\ \end{matrix}\) & \(\begin{matrix}-0.253\\ ** \\ \end{matrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean, standard deviation, and correlation. The hierarchical regression method was used to verify the effect of consumers' fashion innovativeness and technological innovativeness between the attributes of smart clothing and consumer innovation resistance (Table 4).