Closing the technology-behavioural gap in human resource management: An IPRC framework for mitigating AI bias among Generation Z employees

Abstract

Artificial intelligence (AI) is increasingly being used in recruitment, employee assessment, workforce analytics, learning recommendations and performance management. Such applications may increase efficiency, but they may also replicate historical inequalities and shape how employees interpret and act on the automated recommendations. This study investigates the application of the Influencers-Processes-Responses-Contextualizers (IPRC) framework in understanding and addressing AI-related bias in the decision-making of the Generation Z workforce. The systematic literature review was supplemented with bibliometric analysis of the research indexed in Scopus. The original Scopus bibliographic export contained 1,712 records; 1,710 records fell within the stated 1990–2025 temporal window, and 224 studies were selected for critical thematic synthesis. VOSviewer was used to map the intellectual structure and thematic development of the field. Included studies were coded within the four IPRC dimensions. The evidence indicates that research is still centred on technical and algorithmic processes, but behavioural responses, organisational conditions and evidence specific to the Generation Z workforce are less developed. The adapted framework suggests that biased outcomes occur through complex interactions between influential information sources, data and model design, human cognitive processing, employee responses, and contextual factors, such as HR policy, organizational culture, AI literacy and regulation. For HRM practice, technical audits must be combined with transparent decision protocols, meaningful human oversight, employee capability development, feedback and appeal mechanisms, and continuous monitoring of unequal outcomes. The study recasts AI bias as a socio-technical HRM challenge and puts forward an integrative research agenda for responsible human–AI workforce decision-making.