Conceptualizing the Impact of Organizational and Human Factors on Drilling Safety Performance: The Role of AI as a Moderator

Ahmed Ali, Maniyarasi Gowindasamy and Noor Adila Abd. Raub

Information Management and Business Review, 2025, vol. 17, issue 3, 305-329

Abstract: The oil and gas industry is one of the most complex and hazardous industries in the world, with operations that span across upstream, downstream, different environments, and countries. Drilling Activities are considered the most capital-intensive stage in upstream oil and gas sector. Despite improvements in safety performance between 2018 and 2023, the International Association of Drilling Contractors (IADC) highlighted that the industry still faces challenges of incidents and their adverse financial impact. Previous studies have used incident causation methods to identify causes and various factors that lead to the accidents, a “reactive approach”. However, there is a gap in research that explores the numerical relation between Safety Performance and each of Human, Organization factors, and Artificial Intelligence (AI) through a quantitative approach. The research objective was to explore underpinning theories, identify incidents and their human and organizational factors in industry, examine the impact of Artificial Intelligence on that relation, construct a Conceptual Framework that describes that relation, and develop robust control measures considering the AI to minimize chances of accidents in the industry. The research methodology will incorporate a stratified sampling mechanism, involving distribution of questionnaies to 425 employees across various drilling crews within drilling companies in the Middle East. Both validity and reliability will be implemented, and hypotheses will be tested. The correlation between dependent and independent variables will be analysed using quantitative method SPSS and AMOS for data visualization, concluding in the development of a comprehensive conceptual framework, and the best robust control measures will be driven qualitatively.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:rnd:arimbr:v:17:y:2025:i:3:p:305-329

DOI: 10.22610/imbr.v17i3(I).4689

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