Mixed-methods approach shows improved comprehension in digital MBA programs, highlighting the role of SECI model and distributed leadership.
The evolving landscape of digital education, particularly in professional programs such as Master of Business Administration (MBA) programs, presents unique challenges in effectively transforming tacit professional knowledge into explicit academic understanding. While various pedagogical models address digital learning broadly, few offer structured frameworks that meet the specific needs of educators and students in digital MBA programs. This study addresses this gap by proposing an adapted integration of the Socialization, Externalization, Combination, and Internalization (SECI) model, originally developed by Nonaka and Takeuchi, with multi-layered ‘ba’ (shared context) and distributed leadership principles. The research problem centers on the lack of systematic models that bridge professional experience with academic theory in digital, collaborative environments. The originality of this work lies in the development of the Cadence of Course Design Model—a structured, iterative framework specifically designed to enhance knowledge creation in digital MBA programs. A mixed-methods approach evaluated this model’s effectiveness, combining pre- and post-course surveys with qualitative reflections. Results indicate significant improvement in students’ comprehension (from 3.1 to 4.3 on a five-point scale), strongly confirming hypothesis H1. While students reported high satisfaction with distributed leadership aspects, the correlation between perceived effectiveness and actual improvement was not statistically significant, and high satisfaction with distributed-leadership dynamics; however, the latter did not show a direct statistical link to learning gains, indicating partial support for H2. This study contributes to educational theory by demonstrating how knowledge management principles can complement existing pedagogical models, enhancing student engagement and professional readiness in digitally mediated learning environments.
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Dmitrij Żatuchin (2025) studied this question.
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