员工绩效:组织承诺、工作与生活的平衡以及技术压力的影响(非私营机构员工案例研究)

Imam Subchi, Evi Zakiyah, Juanda Naim, Maulidya Mafaza
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引用次数: 0

摘要

人力资源对一个组织的成功非常重要,良好的管理将对员工的绩效、组织承诺、工作与生活的平衡以及技术压力产生重大影响,教育机构和政府机构的各种研究和实际案例都证明了这一点。研究人员有兴趣讨论并了解雅加达希达亚图拉国立伊斯兰大学(Syarif Hidayatullah State Islamic University Jakarta)和日惹苏南卡利雅加国立伊斯兰大学(Sunan Kalijaga State Islamic University Yogyakarta)的组织承诺(Organizational Commitment)、工作与生活平衡(Work-Life Balance)和技术压力(Technostress)对非 PNS 员工的影响程度。本研究采用定量研究方法,探讨组织承诺、工作与生活平衡和技术压力变量对非PNS 员工的影响。本研究需要对每个项目进行结构效度测试。研究人员使用 Lisrel 8.7 应用程序进行 CFA:确证因子分析。然后,使用数据分析技术来确定雅加达 Syarif Hidayatullah 州立伊斯兰大学和日惹 Sunan Kalijaga 州立伊斯兰大学的组织承诺、工作与生活平衡和技术压力对非PNS 员工的影响。该数据分析技术使用 SPSS 应用程序进行多元回归分析。研究结果表明,所有自变量对因变量的影响占 16.8%。相比之下,其余 83.2% 的影响因素来自本研究的其他变量。
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Employee Performance: The Influence of Organizational Commitment, Work-Life Balance and Technostress (Case Study of Non-PNS Employees)
Human resources are very important for the success of an organization, with good management will have a significant impact on employee performance, organizational commitment, work-life balance, and technostress, as evidenced by various studies and practical examples in educational institutions and government agencies. Researchers are interested in discussing and seeing how much influence Organizational Commitment, Work-Life Balance, and Technostress have on non-PNS employees at Syarif Hidayatullah State Islamic University Jakarta and Sunan Kalijaga State Islamic University Yogyakarta. This study uses quantitative research methodology to see the influence of Organizational Commitment, Work-Life Balance, and Technostress variables on non-PNS employees. Construct validity testing on each item is needed to conduct this study. Researchers use CFA: Confirmatory Factor Analysis using the Lisrel 8.7 application. After that, data analysis techniques are used to determine the effect of Organizational Commitment, Work-Life Balance, and Technostress on non-PNS employees at Syarif Hidayatullah State Islamic University Jakarta and Sunan Kalijaga State Islamic University Yogyakarta. This data analysis technique uses multiple regression analysis using the SPSS application. The results of this study produce a total contribution of the influence of all independent variables on the dependent variable, which is 16.8%. In comparison, the remaining 83.2% is influenced by other variables from this study.
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