The study explores critical factors influencing the adoption of big data (BD) in construction projects, addressing challenges such as fragmented organizational dynamics, inadequate regulatory frameworks and limited technological infrastructure.
A comprehensive four-step methodology was employed, including an extensive literature review, expert validation through the Delphi method and the application of interpretive structural modeling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) techniques. These methods systematically mapped interdependencies among 16 critical factors affecting BD adoption.
The results identify incentive strategies, legal frameworks and technological access as foundational factors for BD adoption, while leadership support and stakeholder coordination serve as key linking elements. Dependent factors, such as productivity improvements and customer satisfaction, highlight the broader organizational benefits of BD integration. The study provides a hierarchical framework that offers actionable insights for improving BD adoption in the construction sector.
This research contributes to the existing body of knowledge by applying ISM–MICMAC to a novel context. It bridges theoretical gaps by mapping the interdependencies among BD adoption factors, offering a replicable framework for other sectors. The findings provide practical guidance for policymakers to design supportive policies and for practitioners to address critical adoption barriers effectively.
