
doi: 10.30765/er.2598
This study aims to develop a model for assessing the efficiency of Sustainable Human Resources Management (SHRM) in construction projects. A mixed-methods approach was employed to analyze the factors influencing SHRM efficiency thoroughly. In the qualitative phase, semi-structured interviews were conducted with 15 experts from the Ministry of Energy and the Ministry of Roads and Urban Development in Tehran, Iran. The interviews were analyzed using thematic and inductive thematic analysis, identifying nine key components affecting SHRM performance. These components include employee selection, training, compensation, technical management, financial management, communication, and organizational HR management, each critical to achieving sustainable outcomes in construction projects. The quantitative phase involved administering a survey to 132 experts and engineers drawn from a population of 144 professionals working on construction projects overseen by the ministries. The data collected were analyzed using Confirmatory Factor Analysis (CFA) to validate the model. The results confirmed that all identified components significantly contributed to SHRM efficiency, with high reliability and validity achieved for the model. The Analytic Hierarchy Process (AHP) was integrated into the study to refine the evaluation process further. This method allowed for prioritizing the nine components based on their relative importance, providing construction managers with a structured decision-making framework to focus on the most impactful areas for improving SHRM productivity. The study's findings offer actionable insights into managing human resources in the construction industry, emphasizing the importance of targeted HR practices in achieving sustainable project outcomes and long-term success.
efficiency evaluation, sustainable development, content analysis, construction industry, human resources management, analytic hierarchy process
efficiency evaluation, sustainable development, content analysis, construction industry, human resources management, analytic hierarchy process
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