The Role of Artificial Intelligence in Improving Recruitment and Selection Processes in Public Sector Organizations

The present study aimed to examine the role of artificial intelligence (AI) in improving recruitment and selection processes in public sector organizations. This research is quantitative and descriptive-correlational in nature. The goal of such research is to investigate the relationships and correlations between the independent variable (artificial intelligence) and the dependent variable (improvement of recruitment and selection processes). The statistical population of this study consisted of all human resource employees and managers in public sector organizations in Tehran. Considering the use of the minimum sample size required for structural equation modeling, ۲۵۶ respondents were included in the sample, and questionnaires were distributed among them. Six questionnaires were excluded due to incomplete responses, and statistical analyses were conducted on the remaining ۲۵۰ respondents, who were selected using a multi-stage cluster sampling method. To collect data, both library and field research methods were employed, using a standardized questionnaire as the data collection tool. To assess the validity of the questionnaire, face validity, content validity, and construct validity were employed. Furthermore, reliability was measured using Cronbach's alpha coefficient, composite reliability, and McDonald's omega. Descriptive and inferential statistical methods were used in this study. In the inferential statistics section, structural equation modeling was employed using SmartPLS V۳ software (۲۰۱۶). The findings indicated that artificial intelligence and its components (AI management, AI-based decision-making, AI infrastructure, AI skills, and AI adoption propensity) had a significant and positive effect on improving recruitment and selection processes in public sector organizations. The present study aimed to examine the role of artificial intelligence (AI) in improving recruitment and selection processes in public sector organizations. This research is quantitative and descriptive-correlational in nature. The goal of such research is to investigate the relationships and correlations between the independent variable (artificial intelligence) and the dependent variable (improvement of recruitment and selection processes). The statistical population of this study consisted of all human resource employees and managers in public sector organizations in Tehran. Considering the use of the minimum sample size required for structural equation modeling, ۲۵۶ respondents were included in the sample, and questionnaires were distributed among them. Six questionnaires were excluded due to incomplete responses, and statistical analyses were conducted on the remaining ۲۵۰ respondents, who were selected using a multi-stage cluster sampling method. To collect data, both library and field research methods were employed, using a standardized questionnaire as the data collection tool. To assess the validity of the questionnaire, face validity, content validity, and construct validity were employed. Furthermore, reliability was measured using Cronbach's alpha coefficient, composite reliability, and McDonald's omega. Descriptive and inferential statistical methods were used in this study. In the inferential statistics section, structural equation modeling was employed using SmartPLS V۳ software (۲۰۱۶). The findings indicated that artificial intelligence and its components (AI management, AI-based decision-making, AI infrastructure, AI skills, and AI adoption propensity) had a significant and positive effect on improving recruitment and selection processes in public sector organizations.

کلیدواژه ها:

Artificial Intelligence ، Human Resource Management ، Recruitment and Selection