Exploring the impact of artificial intelligence technology on work engagement: The mediating role of health harm in the workplace

Abstract

Research Purposes This study examines the mediating role of artificial intelligence (AI) technology in the relationship between health harm and work engagement among office workers in Greater Jakarta, Indonesia. Utilizing judgmental non-probability sampling and structural equation modeling (SEM), the research investigates the direct and indirect effects of AI on employee wellbeing and performance. Research Design / Metodology This research uses judgmental non-probability and purposive sampling methods, with sampling units selected based on the researcher's judgment and experience due to the size and distribution of the population. This is in line with established statistical guidelines. Data collection was carried out by distributing questionnaires via Google Forms, sampling to accommodate uncertainty in the number of participants. Data analysis was carried out using structural equation modeling (SEM). This method facilitates testing of models that capture the hierarchical nature of constructs and provides a deeper understanding of the relationships between observed and latent variables. Practical Implications These results contribute to the field of people management by offering valuable insights into the dynamics of employee work engagement. The study highlights the detrimental effects of health harm and the potential of AI technology to mitigate its impact, providing practical implications for organizations implementing AI in the workplace. Findings indicate a significant negative impact of health harm on work engagement. Additionally, AI technology is found to exert a positive influence on engagement, albeit with a moderate-to-strong effect. Importantly, AI technology serves as a significant mediator in the relationship between health harm and work engagement. Research Limitations This study is limited by its sample size, cross-sectional design, reliance on self-reported data, focus on Greater Jakarta, and narrow scope of health harm indicators. Future research could address these limitations by expanding the sample size, conducting longitudinal studies, incorporating objective measures, exploring different regions and industries, and examining a broader range of health harm factors.