The Influence of Leadership, Training, Competence and Work Discipline on the Performance of ASN of the Medan City Regional Research Agency with Work Motivation as an Intervening Variable

Pitta Bonaria Girsang; M. Chaerul Rizky; Eli Delvi Yanti

doi:10.61994/equivalent.v4i2.1888

Authors

Pitta Bonaria Girsang Universitas Pembangunan Panca Budi, Medan, North Sumatra Author
M. Chaerul Rizky Universitas Pembangunan Panca Budi, Medan, North Sumatra Author
Eli Delvi Yanti Universitas Pembangunan Panca Budi, Medan, North Sumatra Author

DOI:

https://doi.org/10.61994/equivalent.v4i2.1888

Keywords:

Leadership, Training, Competence, Work Discipline, Work Motivation, ASN Performance

Abstract

This study aims to analyze the influence of leadership, training, competence, and work discipline on the performance of ASN of the Medan City Regional Research Agency with work motivation as an intervening variable. This study uses a quantitative approach with a causal associative method involving all ASN employees of the Medan City Regional Research Agency as a population and research sample through a saturated sampling technique totaling 38 respondents. Data was collected using a questionnaire with a Likert scale that had been tested for validity and reliability, then analyzed using Structural Equation Modeling-Partial Least Square (SEM-PLS) to test the direct and indirect relationships between variables. The results of the study show that work discipline is the most dominant factor that has a positive and significant effect on the performance of ASN, both directly and on work motivation. Competency has been proven to have a significant effect on work motivation but does not have a direct effect on the performance of ASN. On the other hand, leadership and training do not show a significant influence on both ASN performance and work motivation. Another important finding is that work motivation fails to play a role as an effective intervening variable in mediating the influence of the variables of leadership, training, competence, and work discipline on the performance of ASN. This research model has excellent predictive capabilities and is suitable for predicting the performance of ASN.

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