Integrating Affective Computing and MCDM in Mobile Interactive Systems for Construction Project Manager Selection.

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Bibliographic Details
Title:	Integrating Affective Computing and MCDM in Mobile Interactive Systems for Construction Project Manager Selection.
Authors:	Hashim, Nurul Hafizah Hazwani¹ nurul_hafizah_h2@ahsgs.uum.edu.my, Nawi, Mohd Nasrun Mohd¹ nasrun@uum.edu.my, Omar, Mohd Faizal¹ faizal_omar@uum.edu.my
Source:	International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 9, p69-79. 11p.
Subjects:	Affective computing, Multiple criteria decision making, Emotion recognition, Decision support systems, Emotional intelligence, Interactive computer systems, Construction project management
Abstract:	Selecting an effective construction project manager is critical to project success, given the complex, high-risk, and people-intensive nature of construction environments. While existing selection frameworks emphasize technical expertise and managerial experience, behavioral and emotional factors that influence leadership and team performance are often assessed subjectively or overlooked. Moreover, conventional multi-criteria decision-making (MCDM) approaches remain largely static and are not designed to capture emotion-related dynamics during evaluation. To address these limitations, this study proposes an integrated framework that combines a mobile interactive system for affective computing with MCDM techniques for construction project manager selection. Facial expressions elicited during structured interview scenarios are analyzed using deep learning-based emotion recognition, incorporating both categorical emotions and pleasure-arousal-dominance (PAD)-based intensity measures to quantify emotional intelligence objectively. These affective indicators are then integrated with technical, managerial, and communication criteria within an MCDM framework to generate systematic and transparent candidate rankings. The proposed approach advances existing selection methods by operationalizing emotional intelligence as a measurable decision attribute and embedding it within a structured decision-support system. By enabling the joint evaluation of cognitive and affective competencies through a platform-independent framework, this study offers a more holistic, consistent, and context-aware basis for construction project manager selection, with practical implications for improving managerial effectiveness and project outcomes. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Selecting an effective construction project manager is critical to project success, given the complex, high-risk, and people-intensive nature of construction environments. While existing selection frameworks emphasize technical expertise and managerial experience, behavioral and emotional factors that influence leadership and team performance are often assessed subjectively or overlooked. Moreover, conventional multi-criteria decision-making (MCDM) approaches remain largely static and are not designed to capture emotion-related dynamics during evaluation. To address these limitations, this study proposes an integrated framework that combines a mobile interactive system for affective computing with MCDM techniques for construction project manager selection. Facial expressions elicited during structured interview scenarios are analyzed using deep learning-based emotion recognition, incorporating both categorical emotions and pleasure-arousal-dominance (PAD)-based intensity measures to quantify emotional intelligence objectively. These affective indicators are then integrated with technical, managerial, and communication criteria within an MCDM framework to generate systematic and transparent candidate rankings. The proposed approach advances existing selection methods by operationalizing emotional intelligence as a measurable decision attribute and embedding it within a structured decision-support system. By enabling the joint evaluation of cognitive and affective competencies through a platform-independent framework, this study offers a more holistic, consistent, and context-aware basis for construction project manager selection, with practical implications for improving managerial effectiveness and project outcomes. [ABSTRACT FROM AUTHOR]
ISSN:	18657923
DOI:	10.3991/ijim.v20i09.61575