Impact of AI-powered HR on Employee Engagement and Retention Outcomes

Thesis Abstract

The rapid integration of artificial intelligence (AI) within human resource management (HRM) operations has transformed how organizations attract, develop, and retain talent, yet the implications for employee engagement and retention outcomes remain underexplored in empirical settings. This study addresses the gap by examining how AI-powered HR processes—such as recruitment and selection, learning and development, performance management, and employee sentiment monitoring—shape engagement levels and retention propensity in contemporary workplaces. The aim is to determine the mechanisms through which AI-enabled HR practices influence engagement drivers (perceived fairness, perceived support, perceived usefulness) and subsequent retention outcomes, while accounting for contextual moderating factors such as organizational culture and job complexity. Specific objectives are to (1) assess the relationship between AI-enabled recruitment reliability and new-hire engagement; (2) evaluate how AI-driven learning recommendations impact ongoing employee engagement and developmental satisfaction; (3) examine the effect of AI-assisted performance feedback on commitment and turnover intentions; (4) analyze the role of algorithmic transparency and trust in mediating engagement and retention; and (5) identify boundary conditions under which AI HR practices yield the strongest retention benefits. The study adopts a mixed-methods, explanatory sequential design guided by Social Exchange Theory and the Job Demands-Resources (JD-R) model to capture both quantitative associations and qualitative insights into employee perceptions of AI HR interventions. A multi-industry sample comprising 18 mid-to-large organizations across manufacturing, services, and technology sectors in a metropolitan region will be targeted. The quantitative phase will survey 600 full-time employees who have interacted with AI-enabled HR tools within the preceding 12 months, alongside 60 HR professionals overseeing AI systems to provide process-level context. Instruments will include validated scales for engagement (Gallup Q12 adapted), perceived organizational support, trust in AI, perceived fairness of AI decisions, job satisfaction, and turnover intentions, plus metrics extracted from HRIS on actual retention over a 12-month follow-up. Reliability will be assessed via Cronbach’s alpha and composite reliability, and construct validity through confirmatory factor analysis. The qualitative phase will involve semi-structured interviews with 40 employees and 6 HR managers to probe interpretations of AI transparency, algorithmic bias, and perceived value of AI-driven actions, with thematic analysis conducted to identify prevailing patterns and novel constructs. Data analysis will utilize hierarchical multiple regression to test direct and interaction effects of AI HR variables on engagement and turnover intentions, controlling for demographics and job characteristics. Mediation analyses will examine whether engagement mediates the relationship between AI-driven HR practices and retention outcomes, using bootstrapped confidence intervals. Moderation analyses will explore whether organizational culture (innovative vs. hierarchical) and job complexity moderate these relationships. For the qualitative data, thematic analysis will generate emergent themes related to trust, perceived fairness, and the ethical implications of AI in HR, with triangulation against quantitative findings to enhance validity. A structured model specification will be advanced to integrate the quantitative and qualitative results, drawing on the JD-R framework to interpret how AI HR tools function as job resources or demands. Key expected findings include (1) AI-enabled recruitment accuracy and speed are positively associated with initial employee engagement and reduced turnover intentions, particularly when transparency and candidate feedback are high; (2) personalized AI-based development plans enhance ongoing engagement and perceived career growth, attenuating burnout risk in high-demand roles; (3) performance feedback generated by AI is effective for increasing commitment when framed with human oversight and explainable rationales; (4) higher algorithmic trust and perceived fairness mediate the engagement–retention link, with stronger effects in innovative organizational cultures and lower job complexity; and (5) misalignment between AI recommendations and employee expectations dampens engagement and elevates turnover risk in rigid cultures. The study contributes to knowledge by delineating the conditions under which AI-powered HR enhances engagement and retention, clarifying the mediating role of trust and fairness, and offering a nuanced, theory-driven framework for designing ethically responsible, transparent, and effective AI HR interventions. Practical implications include guidelines for AI tool governance, transparency standards, and targeted interventions to sustain employee engagement and reduce unwanted turnover in diverse organizational settings. Recommendations for future research include longitudinal studies across different national contexts and industry-specific AI HR configurations to generalize findings.