Comparative Analysis of Building Information Modeling Adoption in Construction Firms

Thesis Abstract

This study addresses the persistent lag in effective adoption of Building Information Modeling (BIM) within construction firms and its impact on project performance, information integration, and lifecycle efficiency. Despite widespread recognition of BIM benefits, firms show uneven uptake across project types and organizational levels, leading to suboptimal collaboration, increased rework, and fragmented data workflows. The aim is to compare BIM adoption across small, medium, and large construction firms and to identify determinants that explain variance in adoption intensity, utilization depth, and perceived value. Specific objectives are to (i) quantify BIM adoption using a multi-dimensional construct comprising organizational, project, and technical dimensions; (ii) examine the relationships between organizational readiness, stakeholder engagement, supply chain collaboration, and BIM utilization; (iii) assess the impact of BIM adoption on project performance indicators such as time, cost, quality, and change management; (iv) analyze differences in adoption drivers and outcomes across firm sizes and project typologies; and (v) develop a context-sensitive framework for enhancing BIM diffusion in practice. Methodologically, the study adopts a cross-sectional, mixed-methods design anchored in the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovations (DOI) theory to explain variance in BIM adoption. The population comprises construction firms active in the engineering, procurement, and construction (EPC) sector within a metropolitan region with a robust BIM ecosystem. A stratified random sample of 320 firms is targeted, with proportional allocation across small (n?120), medium (n?120), and large (n?80) firms to ensure representation of diverse capabilities. Data collection uses a two-tier instrument suite a structured survey for senior managers and BIM coordinators to capture adoption level, readiness, training, and perceived benefits, and semi-structured interviews with key informants (project managers, BIM managers, and IT directors) to contextualize quantitative results. Survey validity is established through content validity by a panel of five BIM scholars and industry practitioners, while reliability is assessed via Cronbach’s alpha (target ?0.70) for composite scales. In addition, project performance data are triangulated from firm-reported metrics and anonymized project records for a six-year window to mitigate single-project bias. Data analysis proceeds in three stages. First, descriptive statistics summarize adoption prevalence and intensity across firm sizes and project types. Second, inferential analyses employ multiple regression to test the hypothesized relationships between TOE factors, organizational readiness, and BIM utilization, with hierarchical regression to isolate the incremental variance explained by firm size. ANOVA and post-hoc tests examine differences in adoption outcomes across strata. Third, thematic analysis of interview transcripts identifies nuanced barriers and enablers, with rigorous coding conducted by two independent researchers and corroborated via triangulation with survey results. A structural equation model (SEM) is specified to examine the mediated pathways from environmental and organizational context to BIM uptake and project performance outcomes. Ethics approvals are secured, and data handling adheres to privacy and confidentiality standards. Key expected findings include (i) larger firms exhibit higher BIM adoption intensity due to greater capital capability and mature digital ecosystems, while small firms show selective, project-specific BIM use; (ii) organizational readiness—encompassing leadership commitment, training infrastructure, and standard operating procedures—strongly predicts BIM depth and integration within workflows; (iii) supply chain collaboration and early-stage BIM planning correlate positively with schedule predictability and cost containment; (iv) barriers such as insufficient skilled personnel, interoperability challenges, and ambiguous return on investment persist, but are mitigated where governance structures and standardized data protocols exist. The study anticipates that the SEM will reveal partial mediation by organizational readiness and project governance in translating environmental drivers into performance gains. The contribution to knowledge encompasses empirically grounded, cross-firm evidence on the determinants and consequences of BIM adoption, refinement of the TOE/DOI theoretical application in construction contexts, and a practical adoption framework tailored to firm size and project typology. The analysis will inform policymakers and industry bodies on scaling BIM adoption and guiding capability development, while offering firms a diagnostic tool to prioritize investments in training, process reengineering, and information governance. The main conclusion is that sustained BIM adoption hinges on integrated organizational readiness and governance, complemented by targeted supplier and client collaboration; recommendations include developing standardized BIM training pipelines, establishing common data environments, and implementing performance measurement dashboards to continuously monitor value realization across the project lifecycle.