The dynamics of knowledge transfer and retention in entrepreneurial education: a conceptual exploration of enabling technologies

Purpose This conceptual paper aims to examine Entrepreneurial Education (EE) in Higher Education Institutions (HEIs) through a Knowledge Management (KM) lens. It explains how enabling technology affordances, when aligned with the characteristics of entrepreneurial knowledge, shape Knowledge Transfer (KT) and Knowledge Retention (KR) under conditions of cognitive bias. It clarifies how KM processes support durable entrepreneurial capability development. Design/methodology/approach The study adopts a theory-driven conceptual methodology. Drawing on KM process theory and conceptual theory-building guidance, the paper develops a mechanism-based framework through six stages: problematisation, theoretical anchoring, construct selection, mechanism mapping, model integration and proposition development. The framework positions KT and KR as the analytical core, enabling technologies as differentiated KM affordances, and cognitive bias as an embedded condition that can distort knowledge interpretation, transfer, retention and reuse. Findings The framework proposes that enabling technologies can strengthen KT and KR when their affordances align with the knowledge’s tacitness, codifiability and contextual dependence, and are supported by interaction, reflection, curation and governance routines. Immersive/simulation, AI/analytics and collaborative/process technologies support KT/KR mechanisms. Cognitive bias can weaken or distort knowledge transfer, retention and reuse, whereas bias-aware routines and cohort-to-cohort feedback can improve knowledge fidelity, programme memory and entrepreneurial capability development. Originality/value The paper advances a KM-centred reframing of EE by positioning KT and KR as the focal mechanisms linking enabling technologies to durable entrepreneurial capability development. It differentiates technologies by their KM affordances and integrates cognitive bias, boundary conditions and programme-level feedback within a single conceptual architecture, offering testable propositions and design principles for technology-enabled EE.