Identifying Students at Risk of Poor Career Adaptation: A Personality-Based Cluster Analysis for Inclusive Career Development

Abstract

Career adaptability — the psychosocial readiness to navigate vocational transitions — is increasingly recognised as a pivotal graduate competency in today's disrupted labour market. Yet we still know relatively little about which students are most at risk of entering employment underprepared, and why. This study addresses that gap by asking a simple but practically important question: do graduating students cluster into meaningfully different personality profiles, and do those profiles predict who will struggle most with career adaptation?

A cross-sectional survey was administered to 500 final-year undergraduate students from five colleges in Thrissur District, Kerala, India. Students completed the Big Five Inventory-X (BFI-X) and the Career Adapt-Abilities Scale–Short Form (CAAS-SF). Pearson correlations established trait-level relationships, while K-means cluster analysis identified two distinct personality profiles. One-way ANOVA confirmed significant differences in career adaptability across these profiles. Conscientiousness emerged as the strongest positive predictor of career adaptability (r = .61, p < .001), followed by extraversion (r = .55), agreeableness (r = .48), and openness (r = .44). Neuroticism showed a significant negative association (r = −.42). Cluster analysis yielded two profiles — an at-risk group (Cluster 1, n = 242, M = 31.97) and a resilient group (Cluster 2, n = 258, M = 42.52) — with ANOVA confirming substantial between-group differences, F(1, 498) = 984.08, p < .001.

These findings suggest that personality-based profiling offers a practical, scalable tool for early identification of students who may need targeted career support before they graduate. The implications for inclusive career development practice are discussed.