DETERMINATION OF WORKLOAD, WORK ENVIRONMENT AND LEADERSHIP STYLE WITH MOTIVATION TO WORK AS VARIABLE MEDIATOR TO PERFORMANCE HEALTH SERVICE OFFICERS CITY OF TANJUNG PINANG

Abstract

In this study, researchers used data respondents, such as gender, age and long working respondents to provide information on the characteristics of respondents. The questionnaire was spread over 82. The discussion in this chapter is the result of a field study to obtain a questionnaire response data that measures five key variables in the study: workload, working environment, leadership style, work motivation and employee performance. Analysis of data with parametric and non parametrics statistics using SEM-PLS (structural Equation Modelling-Partial Least Square) on the research variables, instrument test, normality test, hypothesis test, as well as discussion of the hypothesis test results and path analysis Path. This research uses path analysis to test relationship patterns that reveal the influence of variables or a set of variables against other variables, both direct influences and indirect influences. Calculation of line coefficient in this study assisted with Smart PLS Ver 3.0. To find out the direct and indirect influences between variables then be seen from the calculation result of the line coefficient and to know the significance. The effect of the X3 variable against X4 has a P-Values value of 0.012 < 0.05, so it can be stated that the effect between X3 against X4 is significant. The effect of the variable X3 against Y has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X3 to Y is significant. The effect of X4 to Y variables has a P-Values value of 0.048 < 0.05, so it can be stated that the effect between X4 to Y is significant. The effect of the X1 variable against X4 has a P-Values value of 0.038 < 0.05, so it can be stated that the effect between X1 against X4 is significant. The effect of a X1 variable against Y has a P-Values value of 0.000 < 0.05, so it can be stated that the effect between X1 to Y is significant. The effect of a variable X2 against X4 has a P-Values value of 0.000 < 0.05, so it can be stated that the effect of X2 against X4 is significant. The effect of a variable X2 against Y has a P-Values value of 0.030 < 0.05, so it can be stated that the effect of the X2 against Y is significant.