Bridging the Great Divide: HRM Strategies for Synthesizing, rather than Just Managing, Multigenerational Workforce Differences
Abstract
The contemporary workplace is increasingly defined by the simultaneous presence of multiple generational cohorts, Baby Boomers, Generation X, Millennials, and Generation Z whose values, work orientations, and communication preferences have been shaped by distinct socio-historical, economic, and technological conditions. While prior human resource management scholarship has extensively documented generational differences, much of this literature remains largely descriptive, stereotype-prone, and insufficiently connected to strategic HR system design. Consequently, organizations continue to struggle with translating generational insights into sustainable performance outcomes. Addressing this gap, this paper advances a generational synthesis framework that reconceptualizes multigenerational diversity not as a challenge to be managed, but as a strategic resource to be intentionally integrated through adaptive HRM systems. Drawing on foundational debates in generational theory, organizational behavior, and strategic HRM, the paper synthesizes empirical evidence to identify both points of friction and convergence across generational cohorts. It then articulates evidence-based HRM interventions across key functional domains including talent acquisition, learning and development, performance management, leadership, communication, and total rewards that facilitate intergenerational knowledge exchange, psychological alignment, and collective capability building. By moving beyond compliance-driven diversity approaches and static generational typologies, the study positions generational synthesis as a mechanism for enhancing organizational learning, employee engagement, and long-term competitive advantage. The paper concludes by proposing a forward- looking research agenda that emphasizes longitudinal designs, cross-cultural validation, life- course perspectives, and technology-enabled personalization to deepen theoretical precision and empirical relevance in multigenerational
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