WORK SPIRIT DETERMINATION, WORK RESPONSIBILITY, AND WORK FACILITIES WITH WORK MOTIVATION AS INTERVENING VARIABLES TO PERFORMANCE OF UNIVERSITY EMPLOYEES BATAM USING SEM-PLS (PARTIAL LEAST SQUARE)

In this study, researchers use respondents' data, such as gender, age, and duration of work of respondents to be able to provide information about the characteristics of respondents. The questionnaire distributes as many as 65 respondents using census techniques. This study is the result of a field study to obtain questionnaire answer data which measures five main variables in this study, namely work morale, work responsibility, work facilities, work motivation, and employee performance. 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 Analysis. The study uses 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 calculation of the path coefficient in this study is assisted by Smart PLS Ver 3.0. To find out the direct and indirect interactions between variables, it can be seen from the results of the calculation of the path coefficient and to determine the significance. The results of the study are as follows: the effect of the X3 variable on the X4 variable has a p-value of 0,000 <0,05 so it can be stated that the influence between X3 and X4 is significant. The effect of the X3 variable on the Y variable has a p-value of 0,000> 0.05 so it can be stated that the effect between X3 and Y is significant. The effect of the X4 variable on the Y variable has a p-value of 0.006> 0.05 so it can be stated that the influence between X4 and Y is significant. The effect of the X1 variable on the X4 variable has a p-value of 0.038 <0.05 so it can be stated that the influence between X1 and X4 is significant. The effect of the variable X1 on the Y variable has a p-value of 0.009> 0.05 so it can be stated that the influence between X1 and Y is significant. The effect of the X2 variable on the X4 variable has a p-value of 0.012 <0.05 so it can be stated that the effect between X2 and X4 is significant.