Toward developing a predictive model for interpersonal communication quality in construction projects: an ensemble artificial intelligence-based approach

Abstract

Purpose: Following the job demands-resources theory, this study investigates the role of female managers in enhancing employee well-being in terms of psychological health via workplace resources. Design/methodology/approach: To accomplish this objective, we conducted a comprehensive literature review to identify key IPS. Subsequently, a fuzzy-based algorithm was employed to prioritize these skills. Following this, we developed an algorithm based on Extreme Gradient Boosting (XGBoost) to predict the quality of workers’ IC. The efficacy of the XGBoost model was assessed by applying it to three real-life construction projects. Findings: Upon application of the model to the case studies, we made the following conclusions: (1) “Leadership Style,” “Listening,” “Team Building” and “Clarifying Expectations” emerged as significant skills and (2) the model accurately predicted workers’ IC quality in over 78% of the cases. This algorithm has the potential to preempt interpersonal conflicts, enhancing job-site productivity, team development and human resources management. Furthermore, it can guide construction managers in designing IPS training programs. Originality/value: This study contributes to the existing knowledge by addressing the crucial connection between IPS and IC quality in construction projects. Additionally, our novel approach, integrating fuzzy logic and XGBoost, provides a valuable tool for IC prediction. By identifying significant IPS and offering predictive insights, this research facilitates improved communication and collaboration in the construction industry, ultimately enhancing project outcomes.

Publication DOI: https://doi.org/10.1108/ECAM-09-2023-0958
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
College of Engineering & Physical Sciences
Aston University (General)
Additional Information: Copyright © 2025 Emerald Publishing. This AAM is deposited under the CC BY-NC 4.0 licence. Any reuse is allowed in accordance with the terms outlined by the licence. To reuse the AAM for commercial purposes, permission should be sought by contacting permissions@emeraldinsight.com
Uncontrolled Keywords: Communication quality,Construction,Interpersonal skills,Machine learning,Civil and Structural Engineering,Architecture ,Building and Construction,General Business,Management and Accounting
Publication ISSN: 0969-9988
Last Modified: 01 May 2025 08:43
Date Deposited: 24 Apr 2025 14:40
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.eme ... -0958/full/html (Publisher URL)
PURE Output Type: Article
Published Date: 2025-02-14
Accepted Date: 2024-04-09
Authors: Rahimian, Ali
Sadeghzadeh, Keivan
Mohandes, Saeed Reza
Martek, Igor
Manu, Patrick
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Mirvalad, Sajjad
Odeh, Ibrahim

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Version: Accepted Version

License: Creative Commons Attribution Non-commercial


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