Alexis Hanna

Objective Personality changes are related to successfully performing adult occupational roles which require teamwork, duty, and managing stress. However, it is unclear how personality development relates to specific job characteristics that vary across occupations. Method We investigated whether 151 objective job characteristics, derived from the Occupational Information Network (O*NET), were associated with personality levels and changes in a 12‐year longitudinal sample followed over the school to work transition. Using cross‐validated regularized modeling, we combined two Icelandic longitudinal datasets (total N = 1054) and constructed an individual‐level, aggregated job characteristics score that maximized prediction of personality levels at baseline and change over time. Results The strongest association was found for level of openness (0.25), followed by conscientiousness (0.16) and extraversion (0.14). Overall, aggregated job characteristics had a stronger prediction for personality intercepts (0.14) than slopes (0.10). These results were subsequently replicated in a U.S. sample using levels of the Big Five as the dependent variable. This indicates that associations between job characteristics and personality are generalizable across life stages and nations. Conclusions Our findings suggest that job titles are a valuable resource that can be linked to personality to better understand factors that influence psychological development. Further work is needed to document the prospective validity of job characteristics across a wider range of occupations and age.

Vocational interest measurement has long played a significant role in work contexts, particularly in helping individuals make career choices. A recent meta‐analysis indicated that interest inventories have substantial validity for predicting career choices. However, traditional approaches to interest inventory scoring (e.g., profile matching) typically capture broad, or average relations between vocational interests and occupations in the population, yet may not be accurate in capturing the specific relations in a given sample. Machine learning (ML) approaches provide a potential way forward as they can effectively take into account complexities in the relation between interests and career choices. Thus, this study aims to enhance the accuracy of interest inventory‐based career choice prediction through the application of ML. Using a large sample ( N = 81,267) of employed and unemployed participants, we compared the prediction accuracy of a traditional interest profile method (profile matching) to a new machine‐learning augmented method in predicting occupational membership (for employed participants) and vocational aspirations (for unemployed participants). Results suggest that, compared to the traditional profile method, the machine‐learning augmented method resulted in higher overall accuracy for predicting both types of career choices. The machine‐learning augmented method was especially predictive of job categories with high base rates, yet underpredicted job categories with low base rates. These findings have practical implications for improving the utility of interest inventories for organizational practice, contributing to areas such as employee development, recruitment, job placement, and retention.