Date

6-17-2026

Department

School of Engineering

Degree

Doctor of Philosophy in Engineering (PhD)

Chair

Diana Schwerha

Keywords

Artificial Intelligence, Smart Safety Devices, Workplace Injury Reduction, High-Risk Industries, Cameroon, Qualitative Phenomenology, Technology Acceptance, Organizational Safety Culture, IoT Monitoring Systems, Infrastructure Challenges, Sub-Saharan Africa, Worker Perceptions, Predictive Analytics, Safety Climate, Digital Transformation

Disciplines

Engineering

Abstract

Workplace injury has been a significant concern within high-risk industries in Sub-Saharan Africa, especially Cameroon, with weak worker safety standards, poor infrastructure, lack of compliance with regulatory requirements, and a limited level of digital infrastructure contributing to a high risk of worker injury. While artificial intelligence (AI) supported smart safety technologies have revolutionized the way companies conduct workplace safety management in developed nations, little research exists on the perception of these technologies and practicality among low-resource industrial operators in Sub-Saharan Africa. This qualitative phenomenological study investigated management and employee perceptions regarding the use of AI-based smart safety devices to reduce accident rates for workers in high-risk industries in Cameroon. The research used a qualitative phenomenological approach guided by reflexive thematic analysis as outlined by Braun and Clarke’s model (2006). Ten participants who worked in either construction, manufacturing, or oil refining industries in Cameroon were recruited through purposive sampling to explore participants’ use of AI-assisted workplace safety system technology-based tools (e.g., wearable devices, hazard detection technologies for workplace accidents, predictive monitoring systems, and how the company maintains a corporate safety policy-based approach). Volunteers participated in semi-structured interviews. Interview data was transcribed, coded, and themes developed to provide findings.

Results indicated six main themes. First, the “Effectiveness of AI Safety Technologies” theme revealed that participants perceive AI-driven devices as useful for detecting hazards, predicting safety problems, the coordination of emergency response and emergency reactions, and accident prevention. Second, the theme of “Technical and Infrastructure Challenges” reported variable electrification, inadequate internet access, inadequate maintenance systems, and lack of technical expertise as barriers to the reliability and sustainability of AI-powered technologies. Third, the “Employee Perceptions of AI Monitoring” theme identified mixed perception of the use of workplace surveillance systems (e.g., either protective or controlling). Fourth, the theme “Organizational Safety Culture and Leadership Commitment” emphasized the need for transparent communication, leadership support and worker engagement in developing trust and acceptance of technology. Fifth, the “Training awareness and Worker Preparedness” theme identified the importance of continual training, digital skills training and technical assistance to encourage employees’ confidence and continued use of technology. Sixth, the “Ethical, Regulatory and Governance Issues” theme identified participants’ concern about privacy, data ownership, informed consent, cybersecurity, and the absence of comprehensive law and regulations in implementation of AI in occupational safety s

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