Ontological analysis and statistical modelling in construction workspace management: an integrated approach for enhanced efficiency and safety

Purpose Workspace management plays a major role in construction sites. However, existing methods remain conventional, addressing aspects in isolation, lacking flexibility and analytical depth. Hence, this study develops a holistic model that integrates path planning, equipment management, labour density, workspace parameters and safety. Design/methodology/approach Parameters influencing workspace management and efficiency were collected through a literature review. A quantitative research approach was adopted. Data were collected by circulating a questionnaire and analysed using the Statistical Package for the Social Sciences (SPSS). Partial least squares structural equation modelling (PLS–SEM) was applied to examine the structural relationships among the key workspace parameters. An ontological analysis examined the conceptual interdependencies between the identified parameters. Through statistical analysis, the Relative Importance Index (RII) was determined, and a regression model was developed and validated. Findings The identified parameters significantly impact workspace management and efficiency whereas, ontological analysis revealed interdependencies. PLS–SEM indicated that path planning, equipment hazards and site safety are key predictors, while labour density is a weak predictor. The final regression model achieved strong predictive performance, with path planning being the most influential factor. Practical implications The integrated framework, supported by regression and PLS–SEM, offers project managers a statistical decision-support tool to improve workspace planning, safety and operational efficiency under dynamic site conditions. Originality/value This study presents a novel, multidimensional, integrated data-driven framework that combines ontological analysis linking qualitative reasoning with quantitative validation and PLS–SEM to examine structural relationships among key workspace parameters. A predictive model for optimizing workspace management and enhancing safety is established, representing a methodological and conceptual advancement over existing fragmented approaches.