Iterative Stakeholder-Driven Commercialization Pathways in Academic Entrepreneurship and MVP Learning

Abstract

Academic entrepreneurs (AEs) commercializing deep technologies operate under high uncertainty conditions, where technological advances precede clearly defined industrial applications. At low TRL, laboratory validation and demonstrations provide technical feasibility signals, yet problem definitions, user contexts, and system-level requirements remain narrow with limited perspective. Market information is fragmented and typically hard to translate into practical use. Under such conditions, commercialization unfolds as an iterative process of entrepreneurial framing, innovation learning, and external alignment. This study examines how AEs structure early commercialization through stakeholder engagement. Empirically grounded in a low TRL technology project, the research draws on expert interviews, internal team reflections, university support actors, workshops and a literature review.

The literature provides a conceptual baseline against which emerging empirical themes are compared, refined, and structured. The analysis applies an inductive qualitative coding process, grouping findings into preliminary thematic categories such as entrepreneurial framing, iterative experimentation, business model development, technological advancement, and stakeholder alignment. Early-stage interactions within the team and university ecosystem played a critical role in articulating preliminary benefit hypotheses and shaping initial customer-oriented narratives. As engagement expanded to external stakeholders, effective progress depended on identifying actors with contextual authority and commercialization literacy. Rather than eliciting predefined requirements, exploratory questioning surfaced existing customer technical constraints. These dialogues enabled iterative reframing of problem–solution configurations, aligning technological competitive capabilities with differentiated customer contexts and value creation. Technical development progressed from proof-of-concept experimentation toward validated lab-scale feasibility, allowing more credible hypothesis testing regarding application-specific value propositions. The study conceptualizes early deep-tech commercialization as dialogical validation under time pressure and strategic risk. By integrating narrative stakeholder inquiry, structured analytical intelligence, and iterative technical experimentation, AEs can accelerate learning while managing uncertainty.