Enhancing the hiring process: A predictive system for soft skills assessment

Asmaa Lamjid, Ariss Anass, Imane Ennejjai, Jamal Mabrouki and Ziti Soumia

Data and Metadata, 2024, vol. 3, .387

Abstract: Human Resource Management faces the ongoing challenge of identifying top-performing candidates to enhance organizational success. Traditional recruitment methods heavily rely on assessing hard skills alone, overlooking the importance of soft skills in identifying individuals who excel in their roles. To address this, our paper introduces a novel predictive model that leverages Artificial Intelligence in the hiring process. By analyzing soft skills extracted from CVs, cover letters, websites, professional social media, and psychometric tests, the model accurately predicts potential candidates suitable for specific job roles. This system effectively eliminates poor hiring decisions, reduces time and effort, minimizes recruitment costs, and mitigates turnover risks. The implementation of our proposed model employs various predictive machine learning classifiers, with key input soft skills including creativity, collaboration, empathy, curiosity, and critical thinking. Notably, the Support Vector Machine classifier emerges as the top-performing model in terms of predictive accuracy

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.387:id:1056294dm2024387

DOI: 10.56294/dm2024.387

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