Predicting Soft Skills from Behavioral Data: A Framework for HR and Crisis Management

Abstract

As organizations increasingly operate in complex and high-pressure environments, the need for objective, real-time assessment of soft skills, such as adaptability, emotional regulation, and decision-making under stress, has become critical. Traditional HR methods fall short in capturing these competencies effectively, especially in dynamic contexts like hospitality, healthcare, or crisis management. This paper proposes a novel interdisciplinary framework that adapts behavioral modeling techniques, originally developed for driving behavior analysis, to the field of human capital management. Leveraging sensor-based data, simulators, and machine learning algorithms, the framework translates behavioral indicators (e.g., response latency, gaze shifts, erratic maneuvers) into actionable soft skill profiles. The study introduces a multi-layered predictive model, maps relevant behavioral signals to cognitive traits, and identifies key application domains including recruitment, personalized training, and proactive risk management. Ethical considerations related to data privacy, algorithmic bias, and user acceptability are addressed to ensure responsible deployment. The paper concludes by calling for deeper collaboration between HR professionals, data scientists, and behavioral engineers, and advocates for empirical validation through longitudinal and experimental research. This approach positions AI as a transformative enabler of anticipatory, data-driven human capital strategies.