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

BACKGROUND

Research on personality and vocational interests has reached a plateau, with correlations stabilising around r ≈ .48. This plateau stems from a fundamental limitation in dominant models (e.g., Holland's RIASEC theory): they conceptualise interests as static matches between individuals and environmental categories, rather than as dynamic outcomes emerging from an individual's underlying psychological architecture. 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.