Presentation and Validation of an Artificial Intelligence Application Model for Optimizing the Bank Employee Performance Evaluation System: Frameworks and Validation

Abstract

The purpose of the present study was to present and validate a model for the application of artificial intelligence in optimizing the bank employee performance evaluation system: frameworks and validation. In terms of purpose, this study was applied research and was descriptive-analytical in nature. The research method was mixed-methods and was conducted in both quantitative and qualitative sections. In the qualitative section, the statistical population consisted of academic experts in the fields of human resource management and artificial intelligence, as well as experienced banking managers, of whom 13 individuals were selected using snowball sampling and the theoretical saturation criterion. The data collection instrument in the qualitative section was a semi-structured interview derived from the theoretical foundations. Data analysis in this section was conducted using grounded theory. In the quantitative section, a researcher-made questionnaire developed based on the qualitative model of the study was distributed after confirming its validity and reliability among the statistical population, which consisted of banking experts. The sample size was estimated at 213 participants using random sampling. The findings of the qualitative section indicated that the use of advanced artificial intelligence algorithms, the utilization of performance and financial data, and the need for improvement and optimization of evaluation processes were among the causal factors affecting the bank employee performance evaluation model based on artificial intelligence. The design of adaptive evaluation models, the use of artificial intelligence for simulation and prediction, personalization of evaluation criteria, and continuous feedback were identified as strategic factors influencing the bank employee performance evaluation model based on artificial intelligence. Organizational culture and innovation acceptance, organizational readiness for adopting artificial intelligence, and legal and privacy limitations were identified as intervening factors affecting the bank employee performance evaluation model based on artificial intelligence. Economic and competitive conditions, legal requirements, organizational changes, and the need for adaptation were identified as contextual factors influencing the bank employee performance evaluation model based on artificial intelligence. Improved accuracy and efficiency of evaluation, increased employee satisfaction and job motivation, enhanced productivity and organizational performance, and improved decision-making processes were identified as consequential factors influencing the bank employee performance evaluation model based on artificial intelligence. Performance indicators, skills and analysis of human results, data, algorithms and artificial intelligence technologies, and support, training, ethical considerations, and fairness in evaluation were identified as core factors influencing the bank employee performance evaluation model based on artificial intelligence. In the quantitative section of the study, the paths and causal relationships between external and internal constructs in the structural model were examined and confirmed using confirmatory factor analysis.