Generative AI in construction risk management: a bibliometric analysis of the associated benefits and risks - Mohamed Abdelwahab Hassan Mohamed; M.K.S. Al-Mhdawi; Udechukwu Ojiako; Nicholas Dacre; Abroon Qazi; Farzad Rahimian (Urbanization, Sustainability and Society)
Purpose – The construction industry is under increasing pressure to improve risk management due to the complexity and uncertainty inherent in its projects. Generative artificial intelligence (GenAI) has emerged as a promising tool to address these challenges; however, there remains a limited understanding of its benefits and risks in construction risk management (CRM). This study aims to conduct a bibliometric analysis of current research on GenAI in CRM, exploring publication trends, citations, keywords, intellectual linkages, key contributors and methodologies. Design/methodology/approach – A review of Scopus publications from 2014 to 2024 identifies key categories of GenAI’s benefits and risks for CRM. Using VOSViewer, visual maps illustrate research trends, collaboration networks and citation patterns. Findings – The findings reveal a notable increase in research interest in GenAI for CRM, with benefits classified into technical, operational, technological and integration categories. Risks are grouped into nine areas, including social, security, data and performance. Research limitations/implications – Despite its comprehensive scope, this research focuses exclusively on peer-reviewed studies published between 2014 and 2024, potentially excluding relevant studies from outside this period or non-peer-reviewed sources. Additionally, the bibliometric analysis relied on a specific set of keywords, which may have excluded studies using alternative terminology for GenAI or categorised under related fields. Practical implications – The categorisation of GenAI risks in CRM provides a foundation for critical risk management processes, such as risk analysis, evaluation and response planning. Additionally, understanding the identified benefits, such as improved risk prediction, alongside associated risks, such as ethical and data security issues, enables practitioners to balance innovation with caution, ensuring effective and responsible adoption of GenAI technologies. Originality/value – This research offers a novel bibliometric analysis of the benefits and risks of GenAI in CRM, providing a comprehensive understanding of the field’s evolution and global research landscape. Through the categorisation of the benefits and risks of GenAI in CRM, the study lays the groundwork for developing comprehensive risk management models. Additionally, it identifies key methodologies and research trends, enabling academics and practitioners to refine approaches and bridge research gaps. This work not only enhances theoretical insights but also provides actionable strategies for integrating GenAI into CRM practices effectively and responsibly.