Empirical investigation of building information modelling (BIM) staff's impact on construction project performance: evidence in Kenya

Introduction This study explores the impact of Building Information Modelling (BIM) staff on construction project performance, with a focus on the roles of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF). The research aims to understand how BIM personnel attributes influence project outcomes and how these effects are mediated by TTF and moderated by UTAUT.Methods Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze the data, allowing for the examination of complex interactions between BIM staff attributes, project characteristics, and outcomes. This method is particularly suited for handling smaller sample sizes and non-normal data distributions.Results The analysis revealed that BIM staff attributes-such as team size, expertise, training, and experience-significantly influence construction project performance. Key performance areas affected include design efficiency, error reduction, and adherence to schedules and budgets. TTF emerged as a critical mediator, enhancing performance metrics like stakeholder satisfaction and cost management. UTAUT further moderated the relationship between BIM staff, TTF, and project performance, highlighting the importance of technology acceptance and use within BIM contexts.Discussion The findings emphasize the need for organizations to strategically allocate BIM staff and invest in their professional development to optimize project outcomes. Recommendations include fostering supportive organizational structures and promoting a culture of collaboration and innovation to enhance project resilience and performance, particularly in the face of disruptive events. Future research should expand to diverse project types and scales, investigate emerging technologies, and consider cultural factors through cross-cultural studies. Longitudinal studies and cost-benefit analyses of BIM training and technology adoption are also suggested to provide deeper insights and inform strategic decision-making.