Innovation diffusion research alternates between structured life cycle and evolutionary models and process perspectives that emphasize disruption, reversals, and contextual complexity. This thesis develops the Modular Non Linear Technology and Innovation Diffusion Model as an integrated analytical architecture that reconciles structural regularity with real world complexity.
The model consists of two tightly coupled components. The trajectory model is descriptive and answers how technology and innovation diffuse. It conceptualizes diffusion as an eight phase modular system consisting of Invention or Discovery, Innovation, Pilot, Adaptation, Acceleration, Stabilization, Decline, and Legacy. Phases are defined by functional purpose and empirically interpretable entry and exit conditions. By treating phases as modular states rather than as a fixed chronological sequence, diffusion histories are reconstructed as transition sequences instead of a single canonical path. This enables systematic representation of skipping, looping, regression, compression, overlap, and parallel progression within a bounded transition space. The navigation framework is explanatory and answers why diffusion unfolds along a specific pathway. It identifies and categorizes internal and external drivers and links them to transition sequences through a structured driver transition matrix. This framework clarifies how interacting technological, resource, market, institutional, organizational, and infrastructural conditions shape feasible pathways and constrain alternative trajectories.
To operationalize the model, a methodological framework was developed that integrates criteria driven phase reconstruction, transition space mapping, driver typology construction, and structured case validation. The architecture was subjected to breadth oriented micro validation across more than forty historical innovation and technology cases and depth validation through two longitudinal illustrative cases, namely mRNA COVID 19 vaccines and passenger airplanes. Across cases, diffusion histories mapped coherently onto the modular phase architecture without imposing artificial linearity. The bounded transition grammar proved sufficiently flexible to represent diverse diffusion patterns while preserving analytical comparability. Driver mappings provided systematic explanatory depth for pathway divergence, acceleration, regression, and stabilization.
The thesis contributes an integrated descriptive and explanatory language for analyzing innovation diffusion, enabling cross case comparison, cumulative theory building, and more structured reasoning for managerial and policy decision making.