Strength Of The Link Between Perceived Usefulness And Attitude Analysis
Strength of the link between perceived usefulness and attitude is positive and is .50. Also, it can be stated for the perceived ease of use and attitude where the strength of the link is .42. We have negative relationship between perceived usefulness and intention and is -0.6. Perceived ease of use and intention is also negative -.23. Attitude and intention are positively related as the numbers prove 1.15.
TABLE 29 Regression weights (AMOS) Estimate S.E. C.R. P Label
Attitude <--- Perceived Usefulness 1.000
Now we are going to test the hypotheses. In table 29 we have our regression weights and impacts on each variable individually. P value with three stars*** = P ≤ 0.001. We see that our significance is less than P ≤ 0.05 which proves …show more content…
B Std. Error Beta
1 (Constant) .653 .189 3.457 .001 PU_AVG .073 .040 .096 1.833 .068 PEOU_AVG .086 .055 .094 1.559 .120 ATT_AVG .680 .059 .651 11.514 .000
a. Dependent Variable: ITO_AVG
Table 31 is also providing us insights about the significance and regression weights. Only difference is that this one is calculated in SPSS for double check. Also, in those results we can see that our perceived usefulness and perceived ease of use toward dependent variable intention to use is rejected. Attitude toward intention to use e-banking is strong relationship and is accepted as our last hypothesis.
TABLE 32 Factors affecting attitude toward e-banking
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta
1 (Constant) 1.617 .178 9.061 .000 PU_AVG .136 .043 .188 3.196 .002 PEOU_AVG .492 .052 .560 9.505 .000
a. Dependent Variable: …show more content…
We can see that both independent variables have great significance and we can state that they have positive effect on attitude toward e-banking.
1. Is there any difference between gender category and intention to use e-banking?
TABLE 33 Gender difference statistics
Group Statistics Gender N Mean Std. Deviation Std. Error Mean
ITO_AVG Male 106 4.2264 .96888 .09411 Female 136 4.0625 1.06011 .09090
According to our table 33, we can state that there is no big difference in the means of the groups. Male groups is slightly leaning more towards the number 5 which is strongly agree but still difference is not big. Males and females share same opinions when it comes to intention to use variable.
2. Is there any difference between age category and intention to use e-banking?
TABLE 34 Age difference statistics
ITO_AVG
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound
18-25 37 4.1351 .75138 .12353 3.8846 4.3857 3.00 5.00
25-60 178 4.2584 .95258 .07140 4.1175 4.3993 1.00 5.00
60+ 27 3.3148 1.38778 .26708 2.7658 3.8638 1.50 5.00
Total 242 4.1343 1.02233 .06572 4.0048 4.2638 1.00
TABLE 29 Regression weights (AMOS) Estimate S.E. C.R. P Label
Attitude <--- Perceived Usefulness 1.000
Now we are going to test the hypotheses. In table 29 we have our regression weights and impacts on each variable individually. P value with three stars*** = P ≤ 0.001. We see that our significance is less than P ≤ 0.05 which proves …show more content…
B Std. Error Beta
1 (Constant) .653 .189 3.457 .001 PU_AVG .073 .040 .096 1.833 .068 PEOU_AVG .086 .055 .094 1.559 .120 ATT_AVG .680 .059 .651 11.514 .000
a. Dependent Variable: ITO_AVG
Table 31 is also providing us insights about the significance and regression weights. Only difference is that this one is calculated in SPSS for double check. Also, in those results we can see that our perceived usefulness and perceived ease of use toward dependent variable intention to use is rejected. Attitude toward intention to use e-banking is strong relationship and is accepted as our last hypothesis.
TABLE 32 Factors affecting attitude toward e-banking
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta
1 (Constant) 1.617 .178 9.061 .000 PU_AVG .136 .043 .188 3.196 .002 PEOU_AVG .492 .052 .560 9.505 .000
a. Dependent Variable: …show more content…
We can see that both independent variables have great significance and we can state that they have positive effect on attitude toward e-banking.
1. Is there any difference between gender category and intention to use e-banking?
TABLE 33 Gender difference statistics
Group Statistics Gender N Mean Std. Deviation Std. Error Mean
ITO_AVG Male 106 4.2264 .96888 .09411 Female 136 4.0625 1.06011 .09090
According to our table 33, we can state that there is no big difference in the means of the groups. Male groups is slightly leaning more towards the number 5 which is strongly agree but still difference is not big. Males and females share same opinions when it comes to intention to use variable.
2. Is there any difference between age category and intention to use e-banking?
TABLE 34 Age difference statistics
ITO_AVG
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound
18-25 37 4.1351 .75138 .12353 3.8846 4.3857 3.00 5.00
25-60 178 4.2584 .95258 .07140 4.1175 4.3993 1.00 5.00
60+ 27 3.3148 1.38778 .26708 2.7658 3.8638 1.50 5.00
Total 242 4.1343 1.02233 .06572 4.0048 4.2638 1.00