<![CDATA[In this study, researchers used respondents' data, such as gender, age and duration of work of respondents to be able to provide information about the characteristics of respondents. Where from the questionnaire distributed questionnaire as many as 60 respondents using census techniques. Data analysis with parametric and non parametric statistics using SEM-PLS (Structural Equation Modeling-Partial Least Square) regarding research variables, instrument test, normality test, hypothesis test, and discussion of the results of hypothesis testing and Path Path Analysis. This study uses path analysis (path analysis) to test the pattern of relationships that reveal the influence of variables or a set of variables on other variables, both direct influence and indirect influence. The results of the study are as follows: The effect of the X3 variable on X4 has a P-value of 0.009 <0.05, so that it can be associated with the influence between X3 and X4 is significant. The effect of the X3 variable on Y has a P-Value value of 0.009> 0.05, so that the relationship between X3 and Y can be determined to be significant. The effect of the X4 variable on Y has a P-Value value of 0.009> 0.05, so that it can be determined the influence between X4 on Y is significant. The effect of X1 on X4 has a P-Value value of 0.016 <0.00, so that it can be accounted for between X1 and X4 is significant. The effect of X1 on Y has a P-value of 0.040> 0.06, so that it can be accounted for because the influence of X1 on Y is significant. The effect of the X2 variable on X4 has a P-Value value of 0.006 <0.42, so it can be determined that the relationship between X2 to X4 is significant. The effect of the X2 variable on Y has a P-Value value of 0.012 <0.18, so that it can be accounted for because the influence between X2 on Y is significant.]]>
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