## 5 Analysis and Findings
The negative path of interaction variable between anxiety and psychological resources to compliance is significantly larger in \"China 2020\" than in \"Wuhan 2023\" at the 5% level. Let us also look at the relationship between demographic variables and main variables. In the path from sex to compliance, \"China 2020\" is significantly larger than \"Wuhan 2023\" at the 1% level. Similarly, the path from tenure to fatigue is significantly larger for \"China 2020\" than for \"Wuhan 2023\" at the 5% level. Additionally, although there are no significant group differences, the path from tenure to compliance is significant at the 0.1% level in \"China 2020\", but not significant at the 5% level in \"Wuhan 2023\". These differences may also be influenced by a variety of factors, including time and location. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{**China 2020**} & \multicolumn{4}{c}{**Wuhan 2023**} \\ \cline{2-7}  & \(\alpha\) & **Mean** & **SD** & \(\alpha\) & **Mean** & **SD** & \(t\) \\ \hline Social resources & 0.952 & 3.922 & 0.940 & 0.963 & 4.081 & 0.861 & 4.369 *** \\ Psychological resources & 0.938 & 4.346 & 0.762 & 0.965 & 4.385 & 0.762 & 1.273 \\ Fatigue & 0.872 & 2.508 & 1.169 & 0.914 & 2.266 & 1.136 & 5.343 *** \\ Anxiety & 0.753 & 3.630 & 1.121 & 0.782 & 3.364 & 1.135 & 5.931 *** \\ Compliance & 0.914 & 4.639 & 0.707 & 0.874 & 4.380 & 0.781 & 9.023 *** \\ Turnover intention & 0.923 & 1.812 & 1.126 & 0.953 & 1.869 & 1.140 & 1.275 \\ Age & - & 1.960 & 0.785 & - & 2.130 & 0.780 & 5.582 *** \\ Tenure & - & 2.760 & 1.134 & - & 2.530 & 1.182 & 4.924 *** \\ Manager & - & 0.050 & 0.219 & - & 0.020 & 0.130 & 4.133 *** \\ Sex & - & 1.620 & 0.485 & - & 1.610 & 0.489 & 0.902 \\ \hline \end{tabular} Note: \(n=2973\) for “China 2020” and \(n=813\) for “Wuhan 2023”. *** \(p<0.001\). \(\alpha\): the reliability coefficients, \(t\): Student’s \(t\)-test.  
\end{table} 
Table 1: Mean value of each variable and comparison between groups. \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline  & & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
1 & Social resources & & & 0.639 *** & \(-\)0.408 *** & \(-\)0.040 & 0.656 *** & \(-\)0.387 *** & 0.160 *** & 0.107 *** & 0.130 *** & 0.154 *** \\
2 & Psychological resources & 0.578 *** & & \(-\)0.237 *** & 0.087 *** & 0.743 *** & \(-\)0.232 *** & 0.153 *** & 0.0360 & 0.083* & 0.065 \\
3 & Fatigue & \(-\)0.394 *** & \(-\)0.203 *** & 0.391 *** & \(-\)0.220 *** & \(-\)0.487 *** & \(-\)0.121 *** & \(-\)0.074 *** & \(-\)0.066 & \(-\)0.073 * \\
4 & Anxiety & \(-\)0.028 & 0.148 *** & 0.353 *** & & 0.090 *** & 0.133 *** & \(-\)0.005 & \(-\)0.005 & \(-\)0.170 *** & 0.090 * \\
5 & Compliance & 0.471 *** & 0.674 *** & \(-\)0.082 *** & 0.250 *** & \(-\)0.258 *** & 0.129 *** & 0.079 * & 0.093 *** & 0.062 \\
6 & Turnover intention & \(-\)0.376 *** & \(-\)0.243 *** & 0.0242 *** & 0.202 *** & \(-\)0.209 *** & \(-\)0.264 *** & \(-\)0.268 *** & \(-\)0.101 *** & \(-\)0.170 *** \\
7 & Age & & & & & & & & & & \\
8 & Tenure & 0.014 *** & 0.033 & 0.024 & \(-\)0.001 & 0.083 *** & \(-\)0.135 *** & 0.421 *** & 0.149 *** & 0.213 *** \\
9 & Manager & 0.074 *** & 0.059 *** & \(-\)0.074 *** & \(-\)0.117 *** & 0.048* & \(-\)0.079 *** & 0.128 *** & 0.167 *** & \(-\)0.087 * \\
10 & Sex & 0.047 * & 0.019 & \(-\)0.013 & 0.079 *** & 0.087 *** & \(-\)0.100 *** & 0.099 *** & 0.070 *** & \(-\)0.141 *** \\ \hline \end{tabular} Note \(\pi\) = 2973 for China 2020 (lower left) and \(\pi\) = 833 for Wuhan 2020 (upper right). \(\pi\) \(<\) 0.001, \(\pi\) \(<\) 0.01, \(\pi\) \(<\) 0.05. The figures are the correlation coefficients.  
\end{table} 
Table 2: Results of correlation analysis. In the above analysis, the model for \"China 2020\" was used, so the goodness of fit for \"Wuhan 2023\" is low. Therefore, we conducted a new path analysis using only \"Wuhan 2023\" data by deleting paths that were not significant and created a model that better reflects the psychological state of the workplace after the abolition of regulations.