Abstract

Fatal accident is the most avoidable event in the high-hazard industries. As one of the most critical sectors in the industries, oil and gas possess an enormous risk associated with intrinsic hazards, potentially leading to a fatal accident. This accident can be reduced by understanding the underlying causes and the relationship among its causal factors. Current research investigates the interdependence relation of the fatal accident causal factors in the oil and gas industry from 2010 until 2018. The structural equation modeling approach is applied for determining the covariance and regression coefficient of latent causes variables contributing to the fatal accidents. It is critical to understand the quantified inter-dependence values and the degree of importance of the causal factors so that specific prevention measures can be implemented efficiently and the accident is reduced effectively. This effort will enable significant measures to prevent fatal accidents from recurring.

Original languageEnglish
Title of host publicationProceedings of the 30th International Conference of the International Association for Management of Technology, IAMOT 2021 - MOT for the World of the Future
PublisherUniversity of Pretoria
Pages1302-1315
Number of pages14
ISBN (Electronic)9781775922131
DOIs
Publication statusPublished - 2021
Event30th International Conference of the International Association for Management of Technology: MOT for the World of the Future, IAMOT 2021 - Cairo, Virtual, Egypt
Duration: 19 Sept 202123 Sept 2021

Publication series

NameProceedings of the 30th International Conference of the International Association for Management of Technology, IAMOT 2021 - MOT for the World of the Future

Conference

Conference30th International Conference of the International Association for Management of Technology: MOT for the World of the Future, IAMOT 2021
Country/TerritoryEgypt
CityCairo, Virtual
Period19/09/2123/09/21

Keywords

  • Causal factors
  • Fatal accident
  • Oil and gas industry
  • Structural equation modeling

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