Faisal, Muhammad Alfin Al
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The Influence of Quality of Work Life on Nurses' Performance through Organizational Citizenship Behavior at Restu Ibu Hospital Faisal, Muhammad Alfin Al; Garnida, Aliefety Putu; Santosa, Yaya Aria; Ariestanti, Yenni
Journal of Ageing And Family Vol 4, No 2 (2024): Journal of Ageing And Family
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM Universitas Respati Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52643/joaf.v4i2.7231

Abstract

Nurses, as the frontline in medical services at hospitals, have performance influenced by the Quality of Work Life (QWL). Good QWL enhances nurses' motivation, work-life balance, and job satisfaction. Organizational Citizenship Behavior (OCB), which includes voluntary behaviors such as helping colleagues and taking initiative, strengthens teamwork and hospital effectiveness. However, nurses in Indonesia still face challenges such as high workload and lack of appreciation. This study aims to determine the effect of QWL on nurses' performance through OCB at Restu Ibu Hospital. The study employs a descriptive study with a cross-sectional design. The sample consists of 112 nurses, with data analyzed using chi-square tests and binary logistic regression through questionnaires. The results indicate that the majority of respondents are female, aged 30-49 years, hold a bachelor's degree, and have more than four years of work experience. QWL, OCB, and nurse performance are found to be in the high category (99.1%). The chi-square test shows a significant influence between QWL and OCB on nurse performance with a p-value of 0.009, and QWL significantly affects OCB. Binary logistic regression analysis reveals that QWL and OCB significantly affect nurses' performance with a p-value of 0.000 and an Odds Ratio of 111.000, indicating that each one-unit increase in QWL or OCB enhances the likelihood of better nurse performance by 111 times. The Nagelkerke R Square value of 1.000 signifies that this regression model has perfect predictive capability.