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Abstract

In the commercial air transport industry, numerous accidents have occurred because stakeholders have failed to learn lessons from the past. The reactive hazard identification (HI) methodology in the current regulatory safety management framework provides a fresh opportunity to negotiate the challenges of the air transportation sector's predicted growth and societal safety expectations. In the regulatory framework, ‘learning from the past’ is related to the reactive method of HI, and it is viewed as a data-driven decision-making process in this review. This literature review aims to investigate academic literature on ‘learning from the past’ in the aviation industry to comprehend an overview and identify the gaps in the scholarly approaches and regulatory provisions. The data sources for the review comprise the research articles indexed in Scopus and Web of Science databases from 2000 to July 2023. This review follows a mixed methodology consisting of a bibliometric analysis using an R tool to develop a broad understanding and, for specific gap identification, a systematic literature review in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All 283 studies are included in the bibliometric analysis, while only 24 studies are found eligible based on the predefined inclusion/exclusion criteria for systematic review. The results reveal that ‘learning from the past’ has not been explored as a data-driven decision-making process for HI in aviation industry settings. The stagewise barriers in the learning process and the impact of each stage on overall learning remain unexplored. The regulatory provisions associated with safety information from aviation activities' resilient (positive) outcomes for learning from the past purpose, organizational safety culture, and harmonization with the criminal justice system are vulnerable.

SCOPUS15.xlsx (736 kB)
SCOPUS15.xlsx

WOS15.xlsx (239 kB)
WOS15.xlsx

COMBINED.xlsx (737 kB)
COMBINED.xlsx

COMBINEDSR 27 Aug.xlsx (214 kB)
COMBINEDSR.xlsx

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