Analyzing the Impact of BI and CS Variables on Consumer Loyalty TEST • • • • • • • • TheBI 1 variable has a positive and significant effect on loyalty. TheBI 2 variable has a positive and significant effect on loyalty. TheBI 3 variable has a positive and significant effect on loyalty. TheBI 4 variable has a positive and significant effect on loyalty. TheCS 1 variable has a positive and significant effect on loyalty. TheCS 2 variable has a positive and significant effect on loyalty. TheCS 3 variable has a positive and significant effect on loyalty. TheCS 4 variable has a positive and significant effect on loyalty. Model Summary Adjusted R ModelR R Square Square Std. Error of the Estimate 1 .317a .100 .011 .85685 a. Predictors: (Constant), CS4, BI2, CS3, BI1, CS2, BI3, BI4, CS1 ANOVAa Sum of ModelSquares df 1 Mean Square Regression 6.631 8 .829 Residuals 59.469 81 .734 F Sig. .353b 1.129 Total 66.100 89 a. Dependent Variable: SB5 b. Predictors: (Constant), CS4, BI2, CS3, BI1, CS2, BI3, BI4, CS1 Coefficientsa Unstandardized Standardized Coefficients Coefficients Beta Model Std. Error t Sig. B 1 (Constant) 3.622 1.120 3.233 .002 BI1 -.009 .134 -.008 -.064 .949 BI2 .162 .134 .157 1.210 .230 BI3 .093 .138 .090 .679 .499 BI4 -.324 .135 -.316 -2.392 .019 CS1 -.004 .159 -.003 -.023 .982 CS2 .001 .121 .001 .012 .990 CS3 .210 .147 .181 1.428 .157 CS4 -.062 .105 -.069 -.589 .558 A. Dependent Variable: SB5 2) a). The model shows R = 0.317, meaning that the relationship between quality and decision is 31.7%. This means that the relationship is not close. The greater the R means the tighter the relationship. To ensure the type of relationship between variables can be seen in the following table: Value 0.0 - 0.19 0.2 - 0.39 0.4 - 0.59 0.6 - 0.79 0.8 - 0.99 Interpretation Very heavy Not heavy Quite heavy Erat Sangaterat • R square of 0.100 means that 1.00% of the decision variable can be explained by quality, while the remaining 99% can be explained by other factors examined by this study. • Standard Error of Estimate means measuring the variation of the predicted value. Standard Error of Estimate can also be referred to as standard deviation. The Standard Error of Estimate is 0.85685. then the smaller the standard deviation means the better the model. b). In the table, it can be seen that the significant value is 0.000 (more than 0.05), meaning that quality has a positive and significant effect on decisions. Means reject Ho and accept Hi. The regression equation is Y = 3.622 + 8.65 X, meaning that if the quality is increased by an amount of One-unit will increase the decision by 8.65. Determination Test • R = 0.317 means that the relationship between BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 and SB5 (Y) is 31.7%, meaning that the relationship is not tight. The greater the R means the relationship is very strong. Value Interpretation 0.0 - 0.19 0.2 - 0.39 Very heavy Not heavy 0.4 - 0.59 0.6 - 0.79 0.8 - 0.99 Quite heavy Erat Sangaterat • R Square of 0.100 means that 0.100% of Loyalty can be explained by BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 while the remaining 99% can be explained by other factors. other factors not examined by this research. • Adjusted R Square of 0.011 means that 0.11% of Loyalty can be explained by BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 while the remaining 99.89% can be explained by other factors not examined by this study. • Standard Error of Estimated means measuring the variation of the predicted value. Standard Error of Estimated can also be called standard deviation. In the table, the Standard Error of Estimated is 0.85685. the smaller the standard deviation, the better the model and vice versa, if the standard deviation gets bigger, the worse the model. • ANOVA (F TEST) • The table above reveals that the calculated F value is 1.129 with a significant level of 0.000. while F table at 95% confidence level (0.05). by because the calculation of F count> F table and the significant level of 0.000 <0.05, shows that the influence of the independent variables BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 as a whole is positive and significant on loyalty (Y). • In the table, it can be seen that regression df = 8 means that the number of independent variables is 8, namelyBI1,BI2,BI3,BI4,CS1,CS2,CS3,CS4. • Total df = 89 means that the number of respondents/data is 89 (where df = n-1) while the residual df = 81 is derived from 89-8 • Mean Square: Sum of Square/df. In the table mean square regression (8.29) = 6.631/8.