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This study aims to analyze the effect of green human resources management on job satisfaction through green work engagement and meaningful work. This research method is quantitative. The research sample of 340 respondents was selected using a purposive sampling method. Research data was obtained from distributing online questionnaires through social media. The sample used for this research is a company engaged in the manufacturing industry. The data analysis used to test the hypothesis is descriptive analysis and Partial Least Square (PLS) Structural Equation Modeling (SEM) analysis. The results of the study show that the application of green human resources management has a positive and significant effect on job satisfaction. In addition, meaningful work is proven to be able to mediate between green human resource management and job satisfaction. The results of the study also show that green human resources management has a positive influence on green work engagement. The results of the study also show that green work engagement has a positive and significant effect on job satisfaction in the manufacturing industry. The results of this study provide information that optimal implementation of green human resources management will increase the job satisfaction of manufacturing industry employees and encourage employee involvement in the manufacturing industry so that jobs related to the environment can be a source of inspiration and enthusiasm for manufacturing industry employees at work.
Green Human Resources Management (GHRM) ; Job Satisfaction; Green work Engagement(GWE) ; Manufacturing Industries
Green Human Resources Management (GHRM) ; Job Satisfaction; Green work Engagement(GWE) ; Manufacturing Industries
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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| downloads | 11 |

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