Scaling Digital Platforms in Weak Network-Effects Environments

Licentiatavhandling, 2026

Dominant platform literature explains scaling through self-reinforcing network effects, capital-intensive user acquisition, and platform-led ecosystem orchestration. While persuasive in consumer-facing markets, this logic travels poorly to heavily regulated sectors where institutional forces, rather than the platform, determine who participates, at what pace, and on whose terms. This thesis introduces the concept of weak network-effects environments to describe settings in which the mechanisms through which platforms typically generate and appropriate value are structurally mediated by institutional forces beyond the platform's control, and in which value generation depends instead on institutional fit, accumulated legitimacy, and the capacity to operate within a governance system the platform does not own.

Drawing on two qualitative case studies of digital health platforms in Sweden, the thesis shows that scaling in such environments is governed by three interacting and interdependent mechanisms: scope configuration and reconfiguration, reusable and recursive learning, and provisional alignment with pre-existing governance structures. The thesis contributes to platform research by identifying weak network-effects environments as a boundary condition for dominant scaling theory and by proposing a processual, context-sensitive account of platform growth under institutional constraint.