Revisiting Talent Management and Retention of Engineers in the Era of Digital Transformation and AI: Insights from Malaysia’s Electrical & Electronics Manufacturing Sector
by Arnida Jahya, Noor Rafhati Romaiha, Nurul Ezaili Alias, Rozana Othman, Wei-Loon Koe
Published: November 12, 2025 • DOI: 10.47772/IJRISS.2025.910000343
Abstract
Digital transformation and artificial intelligence (AI) are redefining how organizations attract, develop, and retain talent, challenging the traditional foundations of human resource management (HRM). In Malaysia’s Electrical and Electronics (E&E) manufacturing sector, a key engine of export and innovation, engineers are vital to competitiveness, yet their retention has become increasingly difficult amid automation, evolving skill demands, and growing concerns over AI’s ethical use. This conceptual study revisits talent management and engineer retention through an integrated multitheoretical lens combining the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and Human-Centric Artificial Intelligence (HCAI). It advances a Human–AI Synergy Framework that links AI-driven Talent Management Practices (AITMP), such as recruitment analytics, adaptive learning, predictive retention, and data-informed performance management, to Employer Branding (EB) as a mediating mechanism that enhances engineers’ Intention to Stay (ITS). The relationship is moderated by HCAI principles of fairness, transparency, explainability, and privacy, and enabled by Dynamic HR Capabilities (DHC) that allow organizations to sense, seize, and reconfigure talent systems in response to digital change. Practically, the study offers actionable insights for Malaysia’s E&E companies, including skills personalization, ethical AI governance, augmented leadership, and the infusion of “technological empathy” into employer branding. Conceptually, it positions AI-enabled HRM as both a strategic asset and a moral commitment to human dignity and sustainable retention. The paper concludes with a structured agenda for empirical validation through PLS-SEM mediation and moderation analysis, longitudinal transformation studies, and cross-ASEAN comparative research on AI trust, ethics, and workforce engagement.