Development and Assessment of a Digital Cost Management Tool for Renovation Projects

Thesis Abstract

Effective cost management remains a critical challenge in renovation projects, primarily due to the complexities associated with fluctuating material prices, unpredictable scope changes, and fragmented financial tracking systems. Traditional manual methods and isolated spreadsheet tools often lead to inaccuracies, delayed decision-making, and budget overruns, underscoring the need for integrated, real-time digital solutions tailored specifically for renovation contexts. This study aims to develop and empirically assess a comprehensive digital cost management tool designed to enhance accuracy, efficiency, and transparency in renovation project budgets. The specific objectives include designing a user-centric digital platform incorporating real-time cost tracking, automated budgeting, and predictive analytics; evaluating its usability and reliability in comparison with conventional cost management practices; and examining its impact on project cost control and stakeholder decision-making processes. The research adopts a mixed-methods approach, integrating both qualitative and quantitative techniques. The quantitative component employs a quasi-experimental research design involving a sample of 30 renovation projects drawn from a construction firm specializing in residential refurbishments in the region. A stratified random sampling technique ensures diverse project sizes and complexity levels are represented. Data collection instruments include pre- and post-intervention surveys targeted at project managers, financial officers, and site supervisors, complemented by system usage logs and financial records. Qualitative data are gathered through semi-structured interviews to explore user experiences, challenges, and contextual factors influencing tool adoption. Validity and reliability of the instruments are established via pilot testing, Cronbach’s alpha coefficients (exceeding 0.8), and expert reviews. Data analysis involves descriptive statistics, paired sample t-tests to evaluate differences in cost accuracy before and after implementation, and thematic analysis for qualitative insights. Additionally, regression analysis examines the relationship between tool deployment and project cost outcomes, guided by rational choice theory and technological acceptance model frameworks. Expected findings suggest that the digital tool significantly improves cost estimation accuracy and minimizes budget deviations, with users reporting higher satisfaction and confidence in financial decision-making. The tool's predictive features enable proactive adjustments to project plans, reducing delays due to financial misestimations. The comparative analysis indicates that projects utilizing the digital platform outperform traditional approaches in key performance indicators such as time efficiency, budget adherence, and stakeholder communication effectiveness. These results would demonstrate the strength of integrated digital solutions in addressing the inherent uncertainties of renovation projects. This research contributes to the existing body of knowledge by offering an innovative, scalable, and evidence-based digital framework specifically tailored for renovation cost management. It extends prior studies that predominantly focus on new construction, filling a critical gap regarding refurbishment projects with unique cost dynamics. The integration of user-centric design principles with empirical evaluation adds to both practical and theoretical understanding of technology adoption in construction finance. The findings are expected to influence industry practices by promoting digital transformation strategies and informing policy frameworks for project financial control. The study concludes that implementing a dedicated digital cost management tool facilitates greater precision, transparency, and responsiveness in renovation projects, ultimately reducing financial risks and enhancing project performance. Recommendations include advancing the tool's functionalities to incorporate AI-driven cost forecasting, expanding its applicability across various project types, and developing comprehensive training modules to foster widespread adoption. Further research is suggested to explore long-term impacts, integration with Building Information Modeling (BIM), and customization for different geographical and institutional contexts, thereby contributing to the evolution of digital financial management in the built environment.