Unlocking lean success in megaprojects : mapping causal relationships among key enablers in a fuzzy-based environment

Abstract

Purpose: The purpose of this study is to propose a hybrid conceptual framework that systematically identifies, prioritizes and maps critical enablers of LC adoption in megaprojects, with a particular emphasis on their mutual dependencies and structural relationships. Design/methodology/approach: To achieve the research objectives, a mixed-method approach was employed. First, a systematic literature review was conducted to identify potential enablers of LC adoption. Second, semi-structured interviews were carried out with industry experts to validate and enrich the initial list of enablers. Finally, a fuzzy-based multi-criteria decision-making algorithm was applied to prioritize and map the enablers based on their influence and dependency, enabling a deeper understanding of their structural relationships. Findings: The analysis revealed six main enablers and thirty associated sub-enablers critical to LC adoption in megaprojects. Among these, “Strategic and Leadership” and “Stakeholder and Relationship Management” emerged as the most influential enablers. Key sub-enablers identified include government and top management support, early stakeholder engagement, resource readiness and lean proficiency, lean tools implementation, standardized performance metrics and BIM and technology adoption. These findings provide insight into the core drivers and their interdependencies, offering actionable intelligence for effective LC implementation. Originality/value: This study contributes to both academic literature and industry practice in two key ways. First, unlike previous studies that merely list enablers without exploring their interconnections, this research provides a detailed mapping of enabler dependencies and interactions, offering a more nuanced understanding of LC adoption dynamics. Second, it introduces a novel hybrid framework combining systematic literature review, expert validation and fuzzy logic to prioritize and structure LC enablers.