## 4 Results
### The Effects of Individual Network Structure on Tourism Performances
Furthermore, no matter the coefficients of DC from the source and target, the values are higher than other coefficients with the positive impacts, which means that DC is the most important metric for individual performance improvement. The variable _dis_ shows the negative effect as we assumed, which indicates that a higher distance between two countries causes fewer visitors from one to another. The \(EC_{i}\), \(EC_{j}\), \(LCLC_{i}\) and \(LCLC_{j}\) also negatively impact international tourism arrivals, which have not been detected ever and deny H2f and H2g. The higher score of eigenvector centrality of a node means that this node relates to many others with a higher score, which may cause the individual region's international tourists' loss, and that is similar to the research from [PERSON], et al. [77]. Then, we analyze the effect of all the variables each year to detect the changes of impact yearly. All the degrees of variables' impact have been converted to the direct effect on ITA for each year from 1995 to 2019 based on visualization in MATLAB v7.0 (Figure 8). The result indicates that the influence of CC has been changing considerably in both target and source. The effects of CC from the source are the most changeable with 7414.14% between the lowest (1995) and the highest (2018), and the highest (2000) of CC from the target is higher with 1148.41% more than the lowest (1997). Before 2000 and in the years of 2002, 2004, and 2008, the coefficients for LCLC of the target are positive, but in other years, the coefficients are negative, demonstrating that the influence of targets' LCLC is uncertain. The negative effect of EC and LCLC from the target as well as the distance have been amplified, whereas the coefficients of \(CCi\) are gradually more positive. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Coefficient**} & \multirow{2}{*}{**Std. Err.**} & \multicolumn{2}{c}{**95\% Confidence**} & \multirow{2}{*}{**VIF**} & \multirow{2}{*}{**Revised VIF**} \\  & & & & & **Lower** & & **Upper** \\ \hline Constant & 42.8090 & 0.0838 & 511.08 *** & 42.6448 & 42.9732 & - & - \\ \(\ln\textit{dis}\) & \(-1.7354\) & 0.0049 & \(-356.15\) *** & \(-1.7449\) & \(-1.7258\) & \(1.154\) & \(1.154\) \\ \(\ln DC_{i}\) & 0.7068 & 0.0148 & 47.83 *** & 0.6778 & 0.7357 & 8.784 & 6.836 \\ \(\ln DC_{j}\) & - & - & - & - & - & 11.331 & - \\ \(\ln CC_{i}\) & 8.9563 & 0.0380 & 235.52 *** & 8.8818 & 9.0308 & 2.468 & 2.311 \\ \(\ln CC_{j}\) & 9.3708 & 0.0394 & 238.08 *** & 9.2936 & 9.4479 & 3.068 & 2.165 \\ \(\ln BC_{j}\) & - & - & - & - & - & 12.853 & - \\ \(\ln BC_{j}\) & 0.1994 & 0.0040 & 49.82 *** & 0.1916 & 0.2072 & 6.431 & 6.145 \\ \(\ln EC_{i}\) & \(-0.0017\) & 0.0016 & \(-1.08\) *** & \(-0.0048\) & 0.0014 & 7.782 & 2.181 \\ \(\ln EC_{j}\) & \(-1.0637\) & 0.0124 & \(-85.46\) *** & \(-1.0880\) & \(-1.0393\) & 5.942 & 3.204 \\ \(\ln LCLC_{i}\) & \(-1.4942\) & 0.0262 & \(-57.05\) *** & \(-1.5455\) & \(-1.4428\) & 4.600 & 3.401 \\ \(\ln LCLC_{j}\) & \(-1.6121\) & 0.0282 & \(-57.08\) *** & \(-1.6674\) & \(-1.5567\) & 4.557 & 4.049 \\ \hline \hline \end{tabular} Note. Here *** means the coefficient is significant in the level of 0.01.  
\end{table} 
Table 5: The estimation of variables’ effect.