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The Effect of Workload, Professionalism, Incentives and Work Motivation on Health Worker Performance

Sep 2026 · El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam · Vol 7, pp. 5597-5608 · 0 citations · 22 references

TL;DR

The influence of workload, professionalism, incentives, and work motivation on the performance of health workers at Dr. Soedjarwadi Regional Mental Hospital, Central Java Province, both partially and simultaneously is analyzed.

Abstract

This study aims to analyze the influence of workload, professionalism, incentives, and work motivation on the performance of health workers at Dr. RM. Soedjarwadi Regional Mental Hospital, Central Java Province, both partially and simultaneously. This study uses a quantitative approach with a survey method. The research sample is 25% of all health workers directly involved in patient care at Dr. RM. Soedjarwadi Regional Mental Hospital, excluding medical personnel (doctors), with an incidental sampling technique. Data were collected through questionnaires and analyzed using multiple linear regression with the help of SPSS, including the classical assumption test, F test, t test, and coefficient of determination (R²). The results of the study indicate that: (1) workload has a positive and significant effect on the performance of health workers (B = 0.629; sig. = 0.000); (2) professionalism has a positive and significant effect on the performance of health workers (B = 0.311; sig. =0.023); (3) incentives do not have a positive and significant effect on the performance of health workers (B = 0.043; sig. =0.627); (4) work motivation does not have a positive and significant effect on the performance of health workers (B = -0.145; sig. =0.326); and (5) workload, professionalism, incentives, and work motivation simultaneously have a positive and significant influence on the performance of health workers (F =15,487; sig. = 0.000). The Adjusted R Square value is0.475shows that the four variables are able to explain 47.5% of the variance in health worker performance, while the remaining 52.5% is influenced by other factors outside the model.

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