Career Preference-Personality Mismatch: Leveraging the RIASEC Model in IT-Driven Career Guidance
Whereas choosing a career is a critical life decision, career decision-making process among secondary school students involves misalignment between students’ aspirations and their aptitudes. This study examines the mismatch between career preferences and personality profiles of 717 Ugandan Advanced...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Informatics Department, Faculty of Computer Science Bina Darma University
2025-06-01
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| Series: | Journal of Information Systems and Informatics |
| Subjects: | |
| Online Access: | https://journal-isi.org/index.php/isi/article/view/1121 |
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| Summary: | Whereas choosing a career is a critical life decision, career decision-making process among secondary school students involves misalignment between students’ aspirations and their aptitudes. This study examines the mismatch between career preferences and personality profiles of 717 Ugandan Advanced level and university students from 15 secondary schools and 1 university in Central and Western Uganda. Holland's RIASEC model was used to determine career preferences and determined personality through a 42-item inventory. Statistical analysis in SPSS indicated a substantial misalignment: while nearly 50% of students preferred Investigative or Realistic careers such as engineering and medicine, only 28% demonstrated personality congruence with their preferences. Conversely, students with Social-dominant personalities, rarely selected careers matching this orientation. The overall findings demonstrate a weak positive relationship (Kendall's τ = 0.394) between students’ career preferences and personalities. These results challenge conventional personality-driven career guidance systems, demonstrating their limited applicability in Uganda. Our key contribution lies in transforming mismatches into actionable insights, proposing a hybrid framework that dynamically weights RIASEC profiles against local opportunity data and student aspirations, offering a scalable solution for low-resource educational contexts. |
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| ISSN: | 2656-5935 2656-4882 |