Structural equation modeling of data usage factors in the construction sector: A comprehensive validation of micro level data usage factors

Abstract

The construction industry's digitalization produces a large volume of data from sources like Building Information Modeling (BIM), IoT sensors, drones, real-time project monitoring, and resource tracking. However, only 1-2 % of this data is effectively utilized due to limitations in processing, analysis, and integration across platforms. These limitations are influenced by micro-level factors like syntactics (structure), empirics (accessibility), and semantics (meaning). Current literature highlights a gap in understanding the impact of these micro-level factors on data usability (pragmatics). This study explores the micro-level factors affecting the usability of highway infrastructure data. A survey was conducted among 105 highway stakeholders, and the data was analyzed using covariance-based structural equation modeling (CB-SEM). The findings show that structured data significantly improves both accessibility and interpretability, positively influencing real-world decision-making. Interestingly, the clarity of data (semantics) has a lesser direct impact on its practical use compared to structure and accessibility. The study's originality lies in its focus on the under-researched highway construction sector. It offers practical recommendations for project managers to prioritize data structure and accessibility, improving efficiency by reducing delays and optimizing resource allocation. Globally, these strategies can be applied to large infrastructure projects. The study also highlights the social implications of improving transparency and accountability in public infrastructure projects through better data-driven decision-making.