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Factor 1 2 3 4 5 6
PV4 0.784
PV3 0.781
PV2 0.762
SP4 0.838
SP1 0.814
SP3 0.755
SP2 0.736
TR2 0.884
TR1 0.881
TR3 0.875
Source: Author’s analysis from SPSS
4.4. Pearson correlation analysis
The Pearson correlation analysis indicates that all independent variables are
significantly associated with the dependent variable, as the significance values (Sig.) for all
relationships are below 0.05. Moreover, the correlation coefficients are positive,
suggesting that the independent variables are positively related to employee engagement.
These findings imply that increases in the independent variables are accompanied
by corresponding increases in the level of employee engagement. Therefore, there is
preliminary evidence that the independent variables are capable of explaining variations
in the dependent variable and are appropriate for inclusion in the subsequent multiple
regression analysis.
4.5. OLS linear regression analysis
The results of the OLS linear regression analysis indicate that the research model
demonstrates a relatively good fit with the survey data. The multiple correlation
coefficient (R) is 0.695, reflecting a moderately strong relationship between the
independent variables and the dependent variable, Intention to Use Digital Payment.
The coefficient of determination (R²) is 0.518, meaning that the independent
variables in the model explain 51.8% of the variance in the dependent variable. The
Adjusted R² value is 0.515, which is very close to R², indicating that the model has high
stability and good generalizability, with its explanatory power not significantly affected by
the number of predictors included in the analysis.
The standard error of the estimate is 0.72504, suggesting an acceptable level of
deviation between the predicted and actual values. In addition, the Durbin–Watson
statistic is 1.568, which falls within the acceptable range of 1.5 to 2.5, indicating that
there is no serious autocorrelation problem in the residuals.
Overall, the constructed linear regression model is appropriate for the research data
and satisfies the fundamental assumptions, providing a solid basis for further analysis of
regression coefficients and hypothesis testing.
Table 4. Model Summary b
Adjusted R Std. Error of the
Model R R Square Durbin-Watson
Square Estimate
1 .695 a .518 .515 .72504 1.568

a. Predictors: (Constant), F_PV, F_TR, F_SP, F_PU, F_PEOU, F_SI

b. Dependent Variable: F_BI

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