This study aims to develop a data-driven framework for identifying a Competent Construction Manager by integrating artificial intelligence–based recruitment techniques with a rigorously validated competency assessment model in the context of developing countries. The research follows a three-phase methodology. In the first phase, 35 construction management competency skills are identified through a systematic literature review and validated by a panel of 10 domain experts using content validity, reliability, and construct validation techniques. In the second phase, a dataset of 250 candidate resumes collected from LinkedIn, Google Forms, and email submissions is analyzed using AI-based psychometric assessment (Pymetrics) and natural language processing techniques, including text mining and embedding models, to extract behavioral, semantic, and structured candidate attributes. In the third phase, a case study from India is conducted, where candidates are shortlisted based on owner-defined requirements, and validated competencies are mapped to shortlisted candidates (N = 53) using a structured questionnaire and statistical analysis in SPSS. The results demonstrate that the proposed framework effectively ranks and identifies the top five candidates for interview by integrating competency-based evaluation with AI-derived insights, significantly improving objectivity, decision quality, and recruitment efficiency while reducing screening time. The study also highlights limitations related to self-reported competency assessments and contextual biases in owner-defined requirements, suggesting the need for future validation across diverse projects and regions. Overall, the proposed framework provides a systematic, AI-enabled approach to construction manager selection aligned with industry competency standards and emerging intelligent recruitment practices.
Bharadwaj et al. (Sun,) studied this question.
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