Towards a Unified Framework for Knowledge Worker Roles: A Systematic Literature Review

Purpose – This study tackles the absence of a unified framework for categorizing knowledge workers, a gap constraining effective management and strategic alignment in knowledge-driven organizations. While these workers are central to innovation and value creation, existing definitions and taxonomies remain inconsistent and fragmented. Design/methodology/approach – A systematic literature review of 26 peer-reviewed articles from Scopus and Web of Science was conducted. The study applied structured keyword searches and inclusion criteria to identify thematic patterns, classification criteria, and conceptual models relevant to knowledge workers and their roles. Findings—The analysis uncovers a broad set of classification dimensions, including task complexity, educational background, organizational roles, and engagement with knowledge processes. It introduces a typology of eleven functional roles—knowledge Handlers—anchored in how individuals create, apply, and transfer knowledge. The study also delineates ten core processes and thirteen activities integral to knowledge work. These findings reflect a shift from static categorizations toward dynamic, context-sensitive interpretations of knowledge workers' roles. Research limitations/implications – The scope is limited to published literature, which may omit practitioner insights and sector-specific nuances. The absence of empirical validation also constrains immediate practical application. Practical implications – The synthesized framework offers a structured basis for human resource management, knowledge management, and leadership strategies, enabling more precise role alignment, task design, and integration with digital tools in diverse organizational settings. Originality/value – By consolidating dispersed research into an integrative taxonomy, this study advances theory and practice in knowledge management. It provides a foundation for future empirical studies and supports the development of adaptive classification systems for managing knowledge-intensive roles.