Earticle

초록

영어

We aim to develop and provide initial psychometric validation of a multidimensional Career Competency Scale (CCS) integrating personality, interests, work values, core competencies, and AI-adaptability, and to demonstrate its practical utility for generating data-driven career roadmaps for college students. Rapid advances in artificial intelligence and automation have reshaped the skills that university students need for future careers. Traditional career counselling tools often focus on interests or personality alone, overlooking trainable competencies such as analytical thinking, problem solving, communication, collaboration, digital literacy, and adaptability. We therefore developed a five-domain scale—personality (10 items), interests (30), work values (18), twenty-first-century core competencies (36), and career adaptability/AI competencies (35)—totaling 129 items. The instrument was administered online to 387 Korean undergraduates, and its reliability, factor structure, correlation patterns, and cluster profiles were examined Exploratory factor analysis extracted 36 sub‑factors; the scree plot showed an elbow around five factors, corresponding to the conceptual domains relationships [8]. Cronbach’s α ranged from 0.43 (TIPI) to 0.86 (RIASEC) across sections, while subscale alphas ranged from 0.79 to 0.91 relationships [1]. K-means clustering revealed three profiles—balanced, interpersonal–adaptive, and technical–logical. These findings provide initial validity evidence for the multidimensional scale and offer an empirical basis for designing personalised career roadmaps in higher-education settings.

목차

Abstract
1. Introduction
2. Theoretical Background and Research Model
2.1 Personality and Interests
2.2 Work Values and Core Competencies
2.3 Career Adaptability and AI Competence
2.4 Conceptual Framework and Hypotheses
3. Method
3.1 Participants
3.2 Measures
3.3 Procedures and Analytic Strategy
4. Results
4.1 Reliability of Subscales
4.2 Exploratory Factor Analysis
4.3 Correlation Structure
4.4 Cluster Analysis
5. Results
6. Discussions
7. Conclusion
References

키워드

저자

• Hyunsang Lee [ PhD Candidate, Graduate School of Convergence Technology and Energy, Tech University of Korea, Siheung, Republic of Korea ]
• Jeonghak Lee [ PhD Candidate, Graduate School of Convergence Technology and Energy, Tech University of Korea, Siheung, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380