Impact of AI-powered HR on Employee Engagement in Corporate Settings

Thesis Abstract

The rapid integration of AI-enabled HR tools—ranging from recruitment chatbots and algorithmic candidate screening to intelligent performance management and personalized learning—has transformed how employees perceive and experience organizational practices, with potential implications for engagement, trust, and retention. Despite growing adoption, empirical evidence connecting AI-powered HR practices to employee engagement remains fragmented across industries, cultures, and organizational maturity levels, raising questions about the conditions under which AI enhances or hinders engagement. This study aims to examine the relationship between AI-powered HR processes and employee engagement in corporate settings, with a focus on identifying mediating mechanisms and contextual moderators that shape outcomes. The objectives are to (1) quantify the association between AI-driven HR practices and overall employee engagement, (2) investigate mediating roles of perceived fairness, perceived transparency, and psychological safety, (3) assess moderating effects of organizational culture (innovative versus hierarchical), job complexity, and AI maturity, and (4) develop a parsimonious model linking specific AI HR interventions to engagement outcomes. The research adopts a multi-method, cross-sectional design to ensure both breadth and depth of understanding, combining quantitative analysis with qualitative validation. The study will be conducted in corporate settings across three sectors (financial services, technology, and manufacturing) to enhance generalizability. The quantitative phase will target a sample of 1,200 employees from 60 organizations, selected via stratified random sampling to ensure representation by sector and organizational size. Data will be collected through a structured online survey comprising validated scales AI HR Practice Intensity (custom scale measuring the extent and sophistication of AI use in recruitment, onboarding, performance management, learning and development), Utrecht Work Engagement Scale (UWES-9) for engagement, Organizational Justice Scale for perceived fairness, Ethical Transparency Perception Scale, and Psychological Safety Scale. Control variables will include age, tenure, education, job level, and prior experience with AI. Reliability will be assessed with Cronbach’s alpha and composite reliability, while convergent and discriminant validity will be evaluated via confirmatory factor analysis. Hypotheses will be tested using structural equation modeling (SEM) to estimate direct and indirect effects, complemented by hierarchical regression analyses to probe incremental validity. Moderation effects will be examined using multi-group SEM and interaction terms. The qualitative phase will involve 40 in-depth interviews with HR managers and 40 focus group discussions (6–8 participants each) with employees across the three sectors, exploring nuanced experiences of AI HR tools, perceived fairness and transparency, and contextual factors influencing engagement. Thematic analysis will be employed to identify patterns and compare cross-case differences, with triangulation against quantitative findings to enhance validity. Expected findings include a positive association between AI-powered HR practices and employee engagement when AI applications uphold fairness and transparency and when employees perceive psychological safety to be intact. Mediation analyses are anticipated to reveal that perceived organizational justice and psychological safety partially explain the relationship, while innovative organizational culture and higher AI maturity strengthen the positive effects. Conversely, findings may show that opaque decision logic, biased recommendations, or poor change management diminish engagement, particularly in hierarchical cultures or low AI maturity contexts. The study contributes to knowledge by operationalizing a comprehensive model that connects AI-driven HR interventions to engagement, clarifying the mediating mechanisms of justice and safety, and identifying contextual conditions under which AI enhances or undermines engagement. Theoretically, it integrates boundary conditions for social exchange theory and expectancy theory by incorporating AI transparency and perceived fairness as pivotal constructs, and aligns with the technology-organization-environment (TOE) framework to explain differential adoption effects. Practically, the results offer guidance for HR leaders on selecting and configuring AI tools, communicating decision processes, and implementing change management strategies to maximize engagement gains. The main conclusion is that AI-powered HR can enhance employee engagement in corporate settings when implemented with transparent algorithms, fair decision processes, and supportive organizational cultures; otherwise, engagement may deteriorate due to perceived opacity or bias. Recommended actions include establishing governance for AI transparency, designing bias-mitigation mechanisms, communicating AI-driven decisions clearly, and fostering continuous learning and psychological safety to sustain engagement in AI-enabled HR environments.