Determinants of Employee Performance Through Artificial Intelligence: Green Human Resource Management and Training

Chintya Ones Charli (), Suharno Pawirosumarto and Lusiana Luasiana
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Chintya Ones Charli: Putra Indonesia University YPTK
Suharno Pawirosumarto: Putra Indonesia University YPTK
Lusiana Luasiana: Putra Indonesia University YPTK

A chapter in Proceedings of the 10th Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-10 2022), 2025, pp 1703-1714 from Springer

Abstract: Abstract This article examines the influence of Green Human Resource Management (GHRM) and training on employee performance using Artificial Intelligence (AI) as an intervening variable in the Department of Transportation of Padang City. The study employs a quantitative approach through a survey with 90 respondents, The analysis was conducted using the Structural Equation Modeling (SEM) method, which helped assess the relationships between the variables and determine the statistical significance of the impact of AI on employee performance. The findings indicate that GHRM significantly impacts AI with a coefficient of 0.796, while job training also contributes to AI implementation, albeit with a smaller coefficient (0.151). AI itself has been shown to significantly affect employee performance, with a coefficient of 0.714. GHRM also directly impacts employee performance, but the direct influence of job training on employee performance is not significant. These results suggest that the implementation of AI can enhance the effects of GHRM and training in improving employee performance. Therefore, organizations are encouraged to integrate GHRM and training supported by AI to boost organizational efficiency and productivity. This study highlights the importance of using AI in HR management and training to effectively achieve organizational goals.

Keywords: Employee Performance; Artificial Intelligence; Green HRM; Training (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-839-4_133

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DOI: 10.2991/978-94-6463-839-4_133

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