F1000Research · 2026
Decoding Decisions: Personality-Interest Motivational Sequences as Predictors of Career Paths.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Townsend Gary Clifford, Webb Portia
Аннотация
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.
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.