Drivers of AI Adoption for Project Risk Management in the UAE’s Oil and Gas Industry - AL HAMMADI, ALI HASAN AHMED (2025)
This study examines the use of Artificial Intelligence (AI) technologies in the United Arab Emirates oil and gas sector. AI has the potential to improve predictive analytics, decision making, and operational resilience. However, its integration into high-risk industries is still limited. This study examines the influence of technological, organization, and environmental factors, along with the moderating effect of perceived risk on AI adoption. Grounded in the Technology-Organization-Environment (TOE) framework, Diffusion of Innovations (DOI) theory, and Institutional Theory, a conceptual model was developed and tested using data from 268 professionals in the UAE oil and gas sector. Smart-PLS 4 was employed to conduct Structural Equation Modelling (SEM) and assess hypothesized relationships. The findings revealed that compatibility significantly influenced AI adoption intentions, while other factors, such as innovation culture, leadership support, and complexity, were not supported. Regulatory support and competitive pressure showed marginal effects. Perceived risk moderated the relationship between relative advantage and adoption intention but had no effect elsewhere. The study contributes theoretically by extending established adoption models to a high-risk industrial context, highlighting the selective influence of perceived risk. Practically, it offers industry-specific guidelines to support effective AI integration into project risk management processes. The results underscore the early stage of AI adoption in the sector and point to the need for enhanced technological readiness, regulatory clarity, and strategic alignment.