AI and big data-driven social media recruitment: the mediating role of talent acquisition and employee engagement in bank performance

Abstract

Purpose This study investigates the impact of AI-driven social media recruitment (AI-SMR) on talent acquisition effectiveness (TAE), employee engagement (EE) and bank performance (BP) in the Jordanian banking sector. It examines how AI-powered recruitment tools enhance hiring efficiency, mitigate biases and bolster employer branding, while also assessing the mediating roles of TAE and EE. Design/methodology/approach A quantitative approach was applied using partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 283 HR professionals, recruiters and employees in commercial and investment banks. Stratified random sampling and Cochran’s formula determined the sample size. Reliability, validity and common method bias checks confirmed robustness. Findings Results indicate that AI-SMR is positively associated with enhanced TAE, faster hiring and improved candidate-job matching. EE mediates the AI-SMR–BP link, highlighting how AI-supported hiring fosters satisfaction, alignment and retention. For example, AI-SMR explained 42% of the variance in TAE (R2 = 0.42). Practical implications HR professionals should adopt AI-driven hiring tools, predictive analytics and chatbots to optimize recruitment and engagement while implementing governance mechanisms to ensure fairness, transparency and compliance. Originality/value This study contributes a dual-mediation model connecting AI recruitment, employee engagement and bank performance, offering context-specific insights for modernizing HRM in an emerging economy.

The authors' abstract, as published at the source. Digital Transformation and Society, 2025 · DOI ↗