Integrating Behavioral and Cognitive Psychological Theories with AI for Employee Performance Evaluation - Vosoughzadeh, Shahdad (AI and Tech in Behavioral and Social Sciences, 2025)
This study aims to explore the integration of behavioral and cognitive psychological theories with artificial intelligence (AI) in employee performance evaluation. A qualitative research approach was adopted, utilizing semi-structured interviews to gather in-depth insights from 27 employees in Tehran across various industries. Participants were selected using purposive sampling, ensuring relevance to the research objectives. Data collection continued until theoretical saturation was achieved. The collected data were analyzed thematically using NVivo software to identify key themes related to employee perceptions, cognitive and behavioral responses, and the ethical implications of AI in performance evaluations. The study identified several key themes, including the impact of AI evaluations on motivation and engagement, cognitive load, trust and acceptance, ethical considerations, and job security concerns. While AI-based performance evaluation systems were perceived to enhance objectivity and decision-making efficiency, participants raised concerns about fairness, emotional well-being, and the lack of human oversight. Transparency and explainability emerged as critical factors influencing employees' trust in AI-generated feedback. Furthermore, the study found that AI evaluations contributed to cognitive overload and stress, highlighting the need for balanced AI-human collaboration in performance assessment processes. The findings suggest that AI-driven performance evaluations offer significant benefits in terms of data accuracy and efficiency; however, their successful implementation requires careful consideration of psychological and ethical factors. Organizations should integrate behavioral and cognitive principles into AI evaluation systems to enhance acceptance, trust, and overall effectiveness. A hybrid approach that combines AI insights with human judgment is recommended to ensure fair and comprehensive employee evaluations.