The High Five Model as a Predictor of the Optimal Occupational and Personal Functioning of Workers Through Machine Learning Algorithms

Abstract

In this study, the predictive power of positive personality traits from the High Five Model (HFM) for optimal personal and work functioning in employees was analyzed via machine learning algorithms. A total of 645 active workers participated (409 women and 236 men). Data were collected through the High Five Inventory (HFI), the Mental Health Continuum-SF (MHC-SF), the Symptoms Checklist-27 (SCL-27), the Argentine Work Engagement Scale (AWES), a job satisfaction survey, and a job performance survey. With respect to optimal personal functioning, all the HFM traits (except honesty) were strong predictors. For optimal work functioning, erudition and tenacity predicted high levels of job satisfaction, job performance, and work engagement. ML algorithms (SVM, random forest, KNN) predict personal functioning more effectively than work functioning.