What question did this study set out to answer?

The study aims to explore how personality traits influence vocational interests and generate career pathways.

June 3, 2026Open Access

Decoding Decisions: Personality-Interest Motivational Sequences as Predictors of Career Paths

Key Points

The study aims to explore how personality traits influence vocational interests and generate career pathways.
Analyzed data from 504 final-year South African university students using the Townsend Personality Questionnaire and O*NET Interest Profiler.
Employed a multinomial logistic regression model to predict primary RIASEC categories based on 30 personality traits from 404 participants.
Tested the model's accuracy on a holdout set of 100 participants.
The PIMS model predicted primary RIASEC categories with 60.0% accuracy, surpassing the chance level of 16.7%.
Investigation interests were predicted more effectively, revealing specific personality trait sequences, such as low resilience and high competence.
No consistent personality profile was found for social interests, indicating heterogeneity within this category.

Abstract

Background Research on personality and vocational interests has reached a plateau, with correlations stabilising around r ≈ .48. This plateau occurs because dominant models, such as Holland’s RIASEC (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) theory, treat interests as fixed matches to environments, rather than as dynamic outcomes of an individual’s psychology. This study introduces the Personality-Interest Motivational Sequences (PIMS) framework, which reinterprets vocational interests as emergent properties of facet-level personality trait configurations. Our goal is not to exceed the modest domain-level correlations (r ≈ .48) that have long defined this literature, but rather to uncover the generative mechanisms underlying these associations. Methods We analysed data from 504 final-year South African university students assessed with the Townsend Personality Questionnaire (TPQ) and the O*NET Interest Profiler. A multinomial logistic regression model, trained on a subset of 404 participants and tested on a holdout set of 100, was used to predict primary RIASEC categories from 30 TPQ facet-level traits. Results The PIMS model predicted primary RIASEC categories with 60.0% accuracy, substantially exceeding the chance level of 16.7%. Investigative interests were predicted most effectively, with distinct facet-level sequences identified for this and other types. For example, an Investigative profile was characterised by low Resilience and Sociability facets coupled with high Thrill-seeking and Competence. Notably, the model found no single, consistent personality profile for Social interests, which reveals potential heterogeneity within this RIASEC category. Conclusions The findings support the PIMS framework, demonstrating that vocational interests are systematic outcomes of facet-level personality architecture. This moves the field from describing static correlations to modelling generative psychological mechanisms. The framework provides a foundation for moving beyond typological matching towards dynamic, personalised career pathwaying.

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