AI-ENHANCED RECRUITMENT AND ITS IMPACT ON TALENT ACQUISITION AND WORKFORCE OUTCOMES IN BUSINESS ORGANIZATIONS

Abstract

Purpose:

The study focuses on the impacts of AI-enhanced recruitment on the talent acquisition outcomes through the review of the effects of AI system accuracy, AI efficiency and speed, and perceived ease of use by the recruiter. Business analytics techniques are employed to evaluate these relationships and support evidence-based recruitment decision-making.  It also considers the mediating role of the quality of the recruiter decision in these associations.

Design/methodology/approach:

The type of research design was quantitative and cross-sectional. Business analytics methods, including reliability analysis, validity testing, Pearson correlation, and multiple regression analysis, were applied to examine the relationships among the study variables. A sample of 380 HR professionals was used to gather data using a structured questionnaire that contained 25 Likert-scaled items. Statistical and acceptable validity. There were normal distributions of data, and the correlations between variables were positive and significant. Regression analysis demonstrated that accuracy of the AI system, AI efficiency, and the ease of use were major predictors of talent acquisition outcomes. Recruiter decision quality was also a significant mediator that enhanced the relations between AI features and recruiting effectiveness.

Implications:

The research paper emphasizes the need to incorporate AI technologies into the recruitment processes to improve the quality of the decisions and fairness, as well as the quality of the hiring. Companies implementing AI-based recruitment systems will have higher chances of attaining faster, more uniform, and successful talent acquisition results. Business analytics techniques, including reliability analysis, validity testing, normality testing, Pearson correlation, and multiple regression analysis, were used to test the proposed relationships and examine the mediating effect.

Findings:

The outcomes reveal that all the variables associated with AI were found to have very high internal reliability ( α > .70).

Results:

The values of reliability were all above 0.70, which proved high internal consistency. Sampling adequacy was supported by KMO (0.87), and a significant Bartlett test. The variables exhibited normal distribution. The findings of correlation indicated that all constructs exhibited strong, positive correlation. Regression analysis indicated that the IV1, IV2 and IV3 were the significant predictors of DV with IV3 being the strongest predictor. MV played an important role in mediating the relationship between the independent variables and DV.