Emerging Risks and Human Factors in Industry 4.0: Toward a Hybrid Model of Occupational Safety

Abstract

The advent of Industry 4.0 has introduced profound transformations in industrial systems, characterized by the integration of cyber-physical systems (CPS), the Internet of Things (IoT), artificial intelligence (AI), and automation. While these technologies have optimized efficiency and flexibility, they have also generated a new generation of occupational risks that challenge traditional safety frameworks. This study aims to identify, characterize, and analyze emerging risks arising from Industry 4.0 technologies, focusing on their impact on occupational health, safety, and human–machine interaction. The research also seeks to propose proactive strategies for mitigating such risks and aligning technological innovation with worker well-being. A mixed-method approach was adopted, combining a systematic literature review with an empirical case study in a Brazilian automotive company. Quantitative and qualitative data were collected through online questionnaires distributed to operators, supervisors, and engineers using a five-point Likert scale. Descriptive and comparative analyses were conducted to assess variations in risk perception across hierarchical levels. Findings reveal that the most prevalent emerging risks are ergonomic, psychosocial, and cybernetic, resulting from digital surveillance, cognitive overload, and cybersecurity vulnerabilities. Participants expressed heightened concern about privacy, automation-related fatigue, and information stress. Statistical evidence supports that risk perception varies with digital literacy and organizational role, confirming the hybrid nature of Industry 4.0 risk ecosystems. The study demonstrates that Industry 4.0 reconfigures, rather than eliminates, occupational hazards, demanding interdisciplinary risk governance that integrates engineering, psychology, and ethics. It recommends continuous digital safety training, predictive analytics for early detection, and ethical frameworks for data-driven management.