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A MULTI-MEDIATION MODEL ANALYSIS OF INDUSTRY 4.0, MANUFACTURING PROCESS FACTORS AND GREEN PERFORMANCE UNDER COVID-19
Journal of Applied Structural Equation Modeling ; 7(1):48-72, 2023.
Article in English | Scopus | ID: covidwho-2312877
ABSTRACT
This research focuses on investigating the impact of industry 4.0 (I4.0) on green performance through manufacturing process factors under COVID-19 by drawing on resource dependency theory. The research uses a quantitative approach, and the data were collected from 614 manufacturing companies in Egypt and were analysed using CB-SEM. The results indicated that there is a direct significant relationship between I4.0 and green performance. In addition, results revealed that manufacturing process factor pull system can significantly mediate the relationship between industry 4.0 and green performance. However, setup time reduction and continuous flow did not have a significant mediating role. Finally, COVID-19 contingency policies had a negative significant moderating role in the impact of industry 4.0 and pull system on green performance. The findings of this research will help in extending RDT through conceptualising it in different settings and using its ideas to build a model that can support manufacturers in maintaining green practices through unitising lean manufacturing and I4.0, especially that focusing on green practices is challenging, and market disruptions, such as COVID-19, increase the difficulty of enhancing green performance. This will also fill the gap regarding the dynamic relationship between I4.0, lean manufacturing and green performance under COVID-19. © 2023 Journal of Applied Structural Equation Modeling.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Journal of Applied Structural Equation Modeling Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Journal of Applied Structural Equation Modeling Year: 2023 Document Type: Article