FROM RIASEC TO ALGORITHMS: A LITERATURE REVIEW OF PERSON-VOCATION FIT (1990-2025)
Abstract
Purpose. This study provides a comprehensive review of empirical research on person-vocation fit conducted between 1990 and 2025, clarifying conceptual approaches, antecedent factors, and career-related outcomes of the phenomenon. Methods. A systematic search in Scopus, Web of Science, PsycINFO, and Google Scholar was followed by a two-stage screening of titles, abstracts, and full texts. The final corpus comprised 87 peer- reviewed empirical articles; for each, the country of the sample, research design, sample size, fit-measurement instrument, and investigated antecedents, mediators, moderators, and outcomes were coded. Content analysis and descriptive statistics were used to visualise chronological and geographical trends. Results. More than half of the studies were cross-sectional, although the share of longitudinal panels rose to 40 per cent. RIASEC interest-congruence indices remain the “gold standard” (62 per cent of studies), but algorithmic assessments are rapidly gaining ground. Professional interests, career adaptability, and proactivity emerged as primary antecedents; work engagement was the most frequent mediator, while gender, age, and organisational support served as key moderators. Subjective outcomes (job satisfaction, engagement, calling) were examined more often than objective indicators (grade-point average, income, employment speed), with needs- and values- based metrics proving the best long-term performance predictors. Model comparisons supported the flexible- fit hypothesis, indicating that moderate incongruence can be offset by adaptive role design and career self- regulation. Conclusions. Person-vocation fit is a dynamic process in which an initial alignment of interests triggers the development of competencies and resources. At the same time, a flexible environment allows high performance even when initial congruence is imperfect. Practical implications include combining interest testing with needs- and values-based scales in career guidance, implementing ethically transparent artificial- intelligence systems at the recruitment stage, and incorporating fit metrics into HR analytics.
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