نوع مقاله : پژوهشی
تازه های تحقیق
عنوان مقاله English
نویسندگان English
The construction industry in Iran faces challenges such as delays, cost increases, and quality decline, a significant portion of which stems from weaknesses in communication management among project stakeholders. The aim of this research is to identify key factors affecting communication weaknesses and to present a hybrid model for analyzing and predicting their impact on the performance of construction projects. The present study employs a mixed exploratory-explanatory approach. In the qualitative phase, indicators related to communication weaknesses were extracted through a systematic review of sources and content analysis of documents. In the quantitative phase, data from 114 valid questionnaires from employees of construction projects in Zanjan city were analyzed using Structural Equation Modeling (SEM) and Random Forest (RF) algorithm. The SEM model results showed that information flow, project management, and alignment of human resources with organizational strategy have the greatest direct impact on project performance. The machine learning model also identified these three factors as the strongest predictive variables for communication weaknesses and achieved a prediction accuracy of 0.87 and an AUC value of 0.93. The combination of the two methods provided the possibility of simultaneous analysis of theoretical relationships and empirical prediction. The findings indicate that strengthening the project information management system, clarifying the managerial structure, and aligning human resources with organizational goals can significantly reduce the risk of poor communications. The main innovation of the research is the application of the hybrid SEM-ML approach in the context of national projects in Iran and presenting a model that, in addition to analyzing causal relationships, has the ability to rank and data-driven predict key factors.
کلیدواژهها English
Copyright © Hadi Shakibazahed, Naser Ahmadi
License
This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.
