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

Area of Science:

• Psychology
• Organizational Behavior
• Data Science

Background:

• The High Five Model (HFM) is a widely recognized framework for personality traits.
• Understanding the link between personality and employee functioning is crucial for organizational success.
• Machine learning offers novel approaches to analyze complex psychological data.

Purpose of the Study:

• To investigate the predictive power of the High Five Model traits for personal and work functioning.
• To apply machine learning algorithms to analyze employee data.
• To compare the effectiveness of machine learning in predicting personal versus work functioning.

Main Methods:

• Utilized machine learning algorithms including Support Vector Machines (SVM), random forest, and K-Nearest Neighbors (KNN).
• Collected data from 645 active workers using validated instruments like the High Five Inventory (HFI) and job performance surveys.
• Analyzed personality traits, mental health, symptoms, work engagement, job satisfaction, and job performance.

Main Results:

• All High Five Model traits, except honesty, strongly predicted optimal personal functioning.
• Erudition and tenacity were key predictors of high job satisfaction, performance, and work engagement.
• Machine learning algorithms demonstrated superior predictive accuracy for personal functioning over work functioning.

Conclusions:

• Positive personality traits are significant indicators of employee well-being and performance.
• Specific traits like erudition and tenacity are particularly valuable for work-related outcomes.
• Machine learning is a powerful tool for understanding personality's impact on employee functioning, with greater efficacy for personal outcomes.