Fuzzy Cognitive Map for Human Resource Management Risks in the Fourth Generation Automotive Industry

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Objective

One of the challenges in human resource management is the impact of risks associated with Industry 4.0 on reducing workforce productivity. Therefore, the present study aims to identify and determine the cause-effect relationships of human resource risks in the fourth-generation automotive industry. 

Methodology

This study conducted through a mixed-method (qualitative-quantitative) approach. The study population comprises 16 experts and managers from an automotive company, selected through purposive non-probability sampling. 

Results

Risks were identified and extracted in the qualitative section using interviews and a combined (directed-conventional) content analysis approach, then evaluated through the fuzzy Delphi method. The findings from the qualitative section indicated that the most significant risks include “psychological risk,” “technical risk,” “operational risk,” “social risk,” “legal risk,” “organizational risk,” “managerial risk,” and “economic risk.” Subsequently, data collected through pairwise comparison questionnaires were analyzed using the fuzzy cognitive map method. The findings from this section revealed that “Human Resource Planning 4.0 risk,” “Employee Investment 4.0 risk,” “Employee Payment 4.0 risk,” “Employee Regulations 4.0 risk,” “Industry Standards 4.0 risk,” and “Employee Leadership 4.0 risk” are driving risks, in such a way that the connections propagate through “Human Resource Planning 4.0 risk” and “Employee Job Security 4.0 risk” across the entire system. 

Conclusion

Based on these findings, it is concluded that a lack of effective Human Resource Planning 4.0 exacerbates risks, thereby endangering employees' job security and resulting in psychological issues for employees within Industry 4.0. It is recommended that automotive decision-makers focus on digital human resource strategy.

Language:
Persian
Published:
Journal of Dynamic Management and Business Analysis, Volume:3 Issue: 4, 2024
Pages:
41 to 57
https://www.magiran.com/p2790820  
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