This study investigates the relationship between Building Information Modeling (BIM) and knowledge management in large‐scale construction projects, emphasizing risk management, and increasing productivity. This study aims to identify the most important and impactful aspects of modern management techniques in the large‐scale construction sector to improve construction quality; focusing on cost reduction, energy conservation, and pollution reduction during construction. Employing a validated questionnaire from experts and the Analytical Hierarchy Process (AHP), the research prioritizes key factors, elucidating the pivotal role of BIM in understanding project geometry, efficient documentation, design flexibility, and reduced project delays. Ascertained through data analysis employing the pairwise comparisons method of the AHP analysis method, the compatibility of all comparisons and matrices arising from the decision‐making process of experts was below 0.1. The results establish BIM's technical capabilities, economic considerations, and pollution reduction as paramount, contributing significantly to improve large‐scale construction project risk management. The prioritization of the main criteria reveals their significance in the following order: BIM technical capabilities (0.281), BIM economic capabilities (0.231), pollution reduction (0.228), knowledge management capabilities (0.096), BIM time capabilities (0.090), and human resources management capabilities (0.074). BIM can improve risk management for large‐scale construction projects by: using the BIM model to understand the geometry and features of large‐scale construction projects; digitizing and organizing contract documents, project data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis, and other project‐related documents for efficient project management; decreased duplication and project delays; reducing the need for on‐site personnel during implementation and CO₂ emissions reduction. According to the results of the BIM model utilization to better understand the geometry and characteristics of large‐scale construction projects, the item was identified as the most significant and influential among the main components.

This study rigorously investigates the influence of knowledge management infrastructures (KMI) on employees’ job satisfaction (JS) within the Iranian construction sector. It specifically investigates how structural, cultural, and technological facets of KMIs affect this satisfaction. The research adopts a quantitative methodology, utilizing established measurement tools from Gold et al. for KMIs and Hackman and Oldham for JS. The empirical Information was gathered via a survey distributed to stratified random sample of 150 employees and managers from five diverse construction firms in Iran. Examining the collected data with the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the study presents nuanced insights. It identifies that while cultural and technological infrastructures of KMIs significantly contribute to JS, the structural infrastructure does not exhibit a similar impact. Additionally, demographic factors such as age and professional experience were found to be non-contributory in the dynamics between KMIs and JS. However, gender and educational background emerged as significant moderating variables. Remarkably, employees with advanced academic qualifications reported higher satisfaction, likely due to the alignment of specialized knowledge with their professional roles. This research contributes to the current knowledge base by outlining the distinct components of KMIs that bolster JS in the construction industry, thereby offering a targeted framework for industry practitioners and policymakers to enhance employee well-being and organizational efficiency.