Developing a Big Data Analytics Adoption Framework for the Construction Industry: A Grounded Theory Approach
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Abstract
Big data analytics (BDA) offers transformative potential for decision-making and operational efficiency in the construction industry, yet its adoption remains limited. This study addressed this gap by identifying key determinants and developing a comprehensive framework tailored to the industry’s unique dynamics. Using a qualitative grounded theory approach, 16 semi-structured interviews were conducted among construction organisations to uncover inter- and intra-organisational factors influencing BDA adoption. The study outlined three progressive adoption stages of creating big data, big data buy-in and revolutionising through big data. The study further identifies seven critical determinants, with collaboration emerging as a pivotal enabler. Grounded in the technology-organisation-environment (TOE) framework, the proposed framework offered actionable guidance for construction organisations to navigate the adoption journey, from infancy to maturity. This study bridges data science and construction, advancing theoretical understanding and providing practical insights to foster digital transformation and establish a robust foundation for a data-driven construction industry.
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References
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