Can AI-enabled moment capture platforms achieve product-market fit in knowledge-driven podcast and video consumption?
Year: 2026
Type: dissertation
Abstract: his thesis investigates whether an AI-enabled moment capture platform can achieve product-market fit in the growing market of knowledge-driven podcast and video consumption. The study addresses two research questions: (1) which theoretical evidence streams inform the development of such a platform, and (2) what pain points, customer values, and product features emerge from prototype-based user research, and how do they inform product-market fit. The literature review synthesizes insights from digital platform ecosystem theory, consumer adoption frameworks including TAM, habit, and incumbent system inertia, IS continuance research, and product-market fit literature grounded in Jobs-to-be-Done theory. Empirically, the thesis combines a competitive landscape analysis with prototype-based user research in which eight participants used PodShot for one week before completing semi-structured interviews. Thematic analysis reveals four core themes. The retention problem is real and pervasive but latent, as participants lose insights frequently yet had never sought a solution. PodShot's core value lies in its end-to-end capture, organization, and retrieval system. However, storage alone is insufficient for sustained use, with participants identifying active knowledge retention as the missing piece. The dominant adoption barrier is not a lack of perceived value but the habit of passive consumption, where doing nothing remains the zero-effort default. Willingness to pay clusters between two and five euros per month. The findings suggest that product-market fit is within reach but conditional, depending on closing the retention gap, overcoming behavioral inertia, and accommodating cyclical usage patterns.
Author: Gillgren, Simon Jonas
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7220197880