In this study, researchers used respondent data, such as gender, age and length of employment of respondents in order to provide information about the characteristics of respondents. Where from the questionnaires distributed as many as 186. The discussion in this chapter is the result of field studies to obtain data on answers to questionnaires that measure the five main variables in this study, namely budget participation, job characteristics, emotional intelligence, work motivation and power performance of budget users. Data analysis with parametric and non-parametric statistics using SEM-PLS (Structural Equation Modeling-Partial Least Square) regarding research variables, instrument testing, normality testing, hypothesis testing, and discussion of the results of hypothesis testing and Path Analysis. The effect of the X3 variable on X4 has a P-Values value of 0.000 <0.05, so it can be stated that the effect of X3 on X4 is significant. The effect of the X3 variable on Y has a P-Values value of 0.000 <0.05, so it can be stated that the effect of X3 on Y is significant. The effect of the X4 variable on Y has a P-Values value of 0.213 <0.05, so it can be stated that the effect of X4 on Y is significant. The effect of the X1 variable on X4 has a P-Values value of 0.066 > 0.05, so it can be stated that the effect of X1 on X4 is not significant. The effect of the X1 variable on Y has a P-Values of 0.481 > 0.05, so it can be stated that the effect of X1 on Y is not significant. The effect of the X2 variable on X4 has a P-Values value of 0.251 > 0.05, so it can be stated that the effect of X2 on X4 is not significant. The effect of the X2 variable on Y has a P-Values value of 0.124 > 0.05, so it can be stated that the effect of X2 on Y is not significant.