## 4 Data Analysis and Results
The linear regression between GSS, GSD, GSC, GSE, and EP in Table 5 shows that 54.20% of variances (R\({}^{2}\)) were EP-attributable. As for the linearity verification, a VIF value below 10 demonstrates that there is no multi-correlation or multicollinearity problem, while tolerance statistics above 0.20 are also at a satisfactory level in this construct [69]. It shows that GSS (\(\beta\) = 0.48, \(p\) < 0.01) and GSC (\(\beta\) = 0.36, \(p\) < 0.01) have significant and positive effects on EP, while there is no significant effect of GSD on EP, or GSE on EP, and (\(p\) > 0.1). \(H_{2}\)_Hypothesis:_ Table 4 indicates a positive and significant relationship between GPP and FP. The linear regression between GPP and FP in Table 5 shows that 49.50% of the GPP variances (R\({}^{2}\)) were attributable to FP. GPP have a significant and positive (\(\beta\) = 0.70, \(p\) < 0.01) effect on FP. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Factors** & **GSS** & **GSD** & **GSC** & **GSE** & **EP** & **FP** & **Mean** & **SD** \\ \hline GSS & 0.74 & & & & & & 3.97 & 0.69330 \\ GSD & 0.73 * & 0.73 & & & & & 3.83 & 0.72375 \\ GSC & 0.67 * & 0.61 * & 0.68 & & & & 3.96 & 0.63460 \\ GSE & 0.24 * & 0.22 * & 0.34 * & 0.85 & & & 4.06 & 0.75599 \\ EP & 0.68 * & 0.52 * & 0.66 * & 0.28 * & 0.77 & & 3.85 & 0.89972 \\ FP & 0.68 * & 0.59 * & 0.60 * & 0.31 * & 0.75 * & 0.78 & 3.82 & 0.92415 \\ \hline  & & & GPP & 0.68 * & 0.70 * & 3.96 & 0.54074 \\ \hline \multicolumn{8}{c}{N = 455; * \(p\) < 0.01.} \\ \multicolumn{8}{c}{\(H_{1}\)_Hypothesis:_ As a result of the correlation analysis in Table 4, a positive and significant relationship was found between GPP and EP (H\({}_{1}\)). The linear regression between GPP and EP in Table 5 indicates that 46% of the GPP variances (R\({}^{2}\)) seemed to be attributable to EP, and GPP have a significant and positive (\(\beta\) = 0.68, \(p\) < 0.01) effect on EP. \begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{3}{*}{**Hypotheses**} & \multicolumn{3}{c}{**Model**} & \multicolumn{3}{c}{**ANOVA**} & \multicolumn{3}{c}{**Coefficients**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Summy**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} \\ \cline{2-13}  & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**Sig. **} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{**B**} & \multicolumn{1}{c}{**Std. **} & \multicolumn{1}{c}{**Error**} & \multicolumn{1}{c}{\(\beta\)**} & \multicolumn{1}{c}{**T**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} \\ \hline H\({}_{1}\) & 0.464 & 0.000 * & 392.258 & 1.133 & 0.06 & 0.68 & 19.805 & 0.000 * & 1.000 & 1.000 \\ \hline H\({}_{1a}\) & & & & 0.62 & 0.07 & 0.48 & 9.262 & 0.000 * & 0.39 & 2.582 \\ H\({}_{1b}\) & 0.542 & 0.000 * & 133.164  
\(H_{2a-b<d}\)_Sub-hypotheses_: It is shown in Table 4 that there is a positive and significant relationship between GSS, GSD, GSC, GSE, EP, and FP. The linear regression between GSS, GSD, GSC, GSE, and FP in Table 5 shows that 52.30% of the variances (R\({}^{2}\)) seem to be attributable to FP. As linearity verification, a VIF value below 10 demonstrates that there is no multi-correlation or multicollinearity problem, while tolerance statistics above 0.20 are also at a satisfactory level. In addition, GSS (\(\beta\) = 0.42, \(p\) < 0.01), GSD (\(\beta\) = 0.14, \(p\) < 0.01), GSC (\(\beta\) = 0.20, \(p\) < 0.01), and GSE (\(\beta\) = 0.12, \(p\) < 0.01) have significant and positive effects on FP. \(H_{3}\)_Hypothesis_: Table 4 shows a positive and significant relationship between EP and FP. The linear regression between EP and FP in Table 5 shows that 55.60% of the variance in EP (R\({}^{2}\)) was attributable to FP. EP has a significant and positive (\(\beta\) = 0.75, \(p\) < 0.01) effect on FP.