AI Adoption and Employee Performance: Evidence from Malaysia's Manufacturing Sector in Krubong, Malacca

Abstract

This study examines the relationship between artificial intelligence (AI) adoption and employee performance in Malaysia’s manufacturing sector, focusing on executive-level employees in Krubong, Malacca. While AI integration is increasingly recognized as a driver of organisational efficiency, empirical evidence on its direct impact on employee performance in regional manufacturing contexts remains limited. To address this gap, a quantitative approach was employed, utilising a structured questionnaire to collect data from 103 executives and senior managers. The survey assessed key dimensions of AI adoption, including perceived usefulness, ease of use, trust, and risk, alongside measurable performance outcomes. Statistical analysis, conducted using Spearman’s rank correlation, revealed significant positive relationships between AI adoption and employee performance, with correlation coefficients ranging from moderate to strong (ρ = 0.576 to 0.762). Notably, perceived enjoyment (ρ = 0.762) and interaction needs (ρ = 0.758) exhibited the strongest associations, highlighting the role of user experience in enhancing productivity. Additionally, perceived risk demonstrated a meaningful correlation (ρ = 0.713), suggesting that employee concerns must be managed to optimize AI integration. The findings underscore the importance of addressing both functional and psychological factors in AI implementation strategies. These results contribute to the broader discourse on technology adoption by providing localized insights from Malaysia’s manufacturing industry, emphasizing the need for tailored approaches to maximize workforce performance. The study concludes that while AI adoption significantly enhances productivity, its success depends on balancing technological capabilities with employee perceptions and organizational support. Future research could expand these findings by exploring longitudinal effects across diverse industrial settings.