The Influence of Financial Technology and Quality of Service on Satisfaction and Loyalty on Employees

2. Secondary Data Secondary Data according to Indriantoro and Supomo (2012:129), states that secondary data is data that is a source of research data obtained by researchers indirectly through intermediaries (obtained by and recorded by other parties). Secondary data is generally in the form of evidence, records, or historical reports that have been compiled in published and unpublished archives (documentary data).

The formulation of the research problem uses an associative problem formulation, according to Sugiyono (2018: 63) stating "the associative problem formulation is a research question that is asking the relationship between two or more variables".The t-test was conducted to partially test the effect of the independent variable on the dependent variable with the assumption that other variables are considered constant. The t-test basically shows how far the influence of one independent variable individually in explaining the variation of the related variables is with a significant level of 5%. The test criteria are as follows:

Ho ; 1 ; 2 = 0, there is no partially significant effect of the independent variable on the dependent variable. Ho ; 1 ; 2 0, there is a partially significant effect of the independent variable on the dependent variable.

If tcount < ttable at a = 5%, then Ho is accepted.

If tcount > ttable at a = 5%, then Ho is rejected (Ha is accepted).

According to Ghozali (2015: 76), "The coefficient of determination (R2) essentially measures how far the model's ability to explain variations in the dependent variable is". The coefficient of determination (R2) is used to determine what percentage of variation in the dependent variable can be explained by variations in the independent variable.The coefficient of determination formula used to measure the effect of variable X on variable Y is as follows:

Information:

KD : The value of the coefficient of determination

R2 : The value of the correlation coefficient

Normality Test

III. Results

The normality test is used to test whether in the regression model, the confounding or residual variables have a normal distribution. There are two ways to detect whether the residuals are normally distributed or not, namely by graphical analysis and the statistical test of Ghozali (2013:163). Another test that can be done to see the normality of the data can be used by looking at the histogram graph of the spread of the data. The results of the normality test on the histogram data can be seen below.

In the histogram above, it can be seen that the graph on the histogram has a tendency to follow the diagonal line on the histogram, so the data is said to be normal.

The results of testing the normality of the data using the P-P Plot image show that the data points have spread around the diagonal line. Therefore, the residual data has been normally distributed.

a. TestdistributionisNormal.

b.Calculatedfromdata.

c.LillieforsSignificanceCorrection.

d.Thisis alowerboundofthetruesignificance.

Decision rule:

If sig 0.05, then reject Ha and accept Ho, meaning that the data is not normally distributed. If sig 0.05 then accept Ha reject Ho, meaning that the data is normally distributed.

Based on the results of data processing above, it is found that the results of sig are 0.200. In the Normality Test, i.e. 0.200 > 0.05, which means that the Ha data is normally distributed. Thus it can be concluded that the results of the above research are normally distributed.

Significance Test

Based on the results of calculations through SPSS version 24.0 obtained significant test results as follows: