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Deployment of health and safety standards in extremely hazardous work environments such as oil and gas sector, is essential to minimise accidents leaving employees permanently or temporarily incapacitated. The purpose of this research, is to understand why there are frequent accidents in case country's oil and gas sector, with a view to recommend solutions to mitigate problems.

Research methodology involved extensive review of the literature to appreciate background, and current research on typical accidents and safety measures taken at oil and gas construction sites to minimise accidents in a middle east country. Interviews, questionnaires and case stidies were deployed to acquire data which highlighted major reasons for accidents occurrence at oil and gas construction sites, and safety tools and techniques that could reduce accident rate if adopted by companies.

Findings, showed that oil and gas construction projects in case country were prone to health and safety related risks, challenges and accidents due to failure to comply with standards and legislation. Construction site teams and shop floor staff were rarely involved in development of safety policies, and some had no understanding of requirements and procedures underpinning safety during operations. Research recommended rapid application/adoption of international standards underpinned by ISO 45000 series and staff training at all levels. Deployment of robots and use of machine learning technology were suggested to implement risky tasks in the sector.

Research was based on rampant accidents occurring in hazardous oil and gas sector in country studied. Enforcement of health and safety standards, and use of modern tools and techniques were recommended to minimise accident rate.

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.

This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.

The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.

The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.