This study aims to address the conceptual understanding of Management Laboratories (MLabs) in literature by proposing an integrative conceptual-theoretical framework. The framework unifies MLabs’ composition, core functions and underlying mechanisms, bridging their research, educational and practical applications.
Drawing on a systematic synthesis of multidisciplinary literature, the study identifies five core dimensions of MLabs – simulations, experimental methods, gamification, physical models and case studies –and four key functions: education, research, assessment and consulting. The framework conceptualizes MLabs as dynamic systems, explicitly linking dimensions to functions through mechanisms like experiential learning, feedback loops, and data-driven iteration, while acknowledging boundary conditions such as resource availability, stakeholder alignment and institutional constraints.
The framework reveals MLabs as adaptive systems where dimensions interact dynamically to serve diverse functions. For example, simulations enable experiential training, experimental methods ensure research validity, and gamification enhances engagement in educational contexts. Interconnected mechanisms, such as iterative prototyping and real-time feedback, amplify MLabs’ adaptability across settings. Boundary conditions, including technological infrastructure and organizational culture, critically influence their effectiveness.
This study offers the first unified framework integrating MLabs’ fragmented conceptualizations, advancing theoretical coherence. By mapping dimensions, functions, mechanisms and boundary conditions, it provides a blueprint for designing context-specific MLabs. The framework’s novelty lies in its dynamic systems perspective, which fosters interdisciplinary collaboration. Practically, it delivers actionable strategies for deploying MLabs in education (e.g. immersive learning), research (e.g. controlled experimentation), assessment (e.g. competency evaluation and behavioural diagnostics) and consulting (e.g. scenario planning). This work consolidates disparate research streams, invites empirical validation and enhances MLabs’ strategic value for academics and practitioners.
