Abstract
Background: Drug addiction significantly impacts employee performance, leading to increased absenteeism, reduced productivity, and higher rates of occupational accidents. Therefore, identifying predictive factors for addiction potential is crucial in preventing drug dependence. Objectives: This study aimed to develop a group membership model using discriminant analysis to predict employees' potential for addiction based on key psychological variables. Methods: A correlational study was conducted with a statistical population consisting of all employees working in public departments in Bojnord City, Iran, in 2021 (N = 2,837). A random sample of 303 employees was selected to complete the Iranian Form of the Addiction Potential Scale, the Big Five Inventory, the Cognitive Emotional Regulation Questionnaire, and the Generalized Self-Efficacy Scale. Data analysis was performed using the discriminant analysis method in SPSS software version 23. Results: The findings revealed that employees' addiction potential can be predicted using personality and cognitive variables. The discriminant analysis equation effectively distinguishes employees with high and low addiction potential based on psychological variables, including extroversion, neuroticism, self-efficacy, and cognitive emotional regulation. Conclusions: The study concluded that self-efficacy, cognitive emotional regulation, neuroticism, and extroversion are significant predictors of addiction potential. It is recommended to incorporate these variables into training courses and personnel selection processes for government jobs.