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This paper aims to determine the hierarchical relationship between building partnership competencies for public sector educational leaders (ELs) administering and running the education system.

An interpretive structural modelling (ISM) technique was used to develop a hierarchical structural model for building partnership competencies. Nominal group technique (NGT) was used with the help of experts’ suggestions and opinions at the beginning of ISM to identify building partnership competencies. Also, the NGT was used to rank the competencies. A structural self-integration matrix was developed based on experts’ voting and agreement. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to analyse the relationship among the building partnership competencies. A total of 11 experts were chosen for NGT and ISM sessions.

A total of 16 building partnership competencies were identified for this study. The competencies were compartmentalised into four domains: creative collaboration, create network, develop collective culture and encouraging constructive dialogue. MICMAC analysis shows each domain of the model of its key competencies ranked at the highest level in the ISM model and dependent competencies.

ISM is a modelling approach that is based solely on expert opinions and responses. Its limitation can be overcome with the help of empirical analysis.

This study supports the public sector ELs’ professional development and upskilling. In addition, the model developed in the study will be helpful for stakeholders, human resources division and policymakers to incorporate building partnership competencies in the training and development of ELs.

This study helps to identify and prioritise building partnership competencies using NGT and ISM. Literature shows that numerous authors have used the ISM approach. Still, the combination of NGT approach is limited. Therefore, the model developed in the study was based solely on experts’ opinions and suggestion based on their experiences and knowledge.

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.