Purpose Design changes significantly impact civil engineering projects, influencing their cost, schedule and overall success. Nonetheless, previous studies indicate limited systematic categorization and understanding of the multifaceted factors that drive these design changes. This paper aims to identify the recurring contributing factors leading to design change risks in civil engineering projects. Design/methodology/approach A comprehensive systematic literature review (SLR) was conducted, synthesizing over a decade of empirical and theoretical studies focused on design and engineering (D&E) management and change risk in civil engineering projects. Findings The research identifies eight principal contributing factors to design change risk: client-initiated changes, organizational systemic issues, resource and capacity constraints, construction-driven factors, environmental, sustainability and occupational health (ESOH) drivers, technical deficiencies and design quality issues, technological factors and regulatory and other external influences. A conceptual framework using a causal loop diagram (CLD) is also presented to illustrate the interrelationships among these factors. Practical implications The findings provide a foundational framework for project managers, risk professionals and D&E consultants to identify and proactively manage design change risks, strengthening the resilience and performance of civil engineering projects. The eight-factor framework can be applied during front-end engineering to systematically review client requirement changes, design–construction interfaces, regulatory updates and environmental constraints, helping teams map uncertainties to key factors, prioritize critical risk drivers, refine stage-gate reviews and target additional site investigation, stakeholder coordination or digital integration to prevent late design changes. Originality/value This paper extends the literature on design management and risk management in civil engineering by clarifying the multi-dimensional relationships among the factors driving design changes. Its novelty lies in the development of a comprehensive risk breakdown structure (RBS) that links theory with practice and can support future development of predictive tools such as key risk indicators (KRIs) and artificial intelligence (AI)-based analytics for proactive risk management.
Ismail et al. (Wed,) studied this question.