通过PT.美丽的Tuban大陆的Partial Square(拜托)分析工作环境因素对员工绩效的影响

Ana Zul Fia, S. Sunardi, Tranggono Tranggono
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引用次数: 0

摘要

人力资源是在公司中扮演重要角色的生产要素之一,因为与其他要素相比,人力资源具有非常具体的特征。PT. Benua Indah Tuban是一家生产粘土的制造公司,粘土被用作肥料的基本成分。在该公司的经营活动中,员工在相同的工作环境中与不断变化的条件有很多互动。PT. Benua Indah Tuban在2020年1月至10月期间的员工绩效有所下降。因此,有必要研究工作环境因素对PT. Benua Indah公司员工绩效的影响,从而提高生产部门员工的绩效。本研究将社会环境、物理环境和组织环境作为影响员工绩效的因素。对于数据分析,使用智能PLS应用程序进行模型测试。这种研究方法使用50个样本,一个样本和一个总体或所谓的饱和样本。研究结果表明,物理环境变量对绩效的显著影响为64.2%,组织环境变量对绩效的显著影响为28.6%。而社会环境变量对绩效的影响不显著-3.9%。本研究结果所建立的预测模型对顾客满意变量的解释率为63.2% (R2 = 0.632)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analisis Pengaruh Faktor-Faktor Lingkungan Kerja Terhadap Kinerja Karyawan Dengan Metode Partial Least Square (PLS) Di PT. Benua Indah Tuban
Human resources are one of the production factors that have an important role in a company because they have very specific characteristics compared to other factors. PT. Benua Indah Tuban is a manufacturing company producing clay which is used as a basic ingredient for fertilizers. In its operational activities, workers in this company interact a lot with the same work environment with changing conditions. Employee performance of PT. Benua Indah Tuban during the months January-October 2020 experienced a decline. Thus it is necessary to study the influence of work environment factors on the performance of employees of PT. Benua Indah so that it can improve the performance of employees in the production department. This study uses the social environment, physical environment, and organizational environment as factors that affect employee performance. For data analysis, a model test using the Smart PLS application is used. This research method uses 50 samples with a sample as well as a population or what is called a saturated sample. The results of this study indicate that the physical environment variables have a significant effect on performance by 64.2% and the organizational environment has a significant effect on performance by 28.6%. While the social environment variable does not significantly influence the performance of -3.9%. The prediction model from the results of this study is able to explain the customer satisfaction variable by 63.2% (R2 = 0.632).  
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