Paper Type

ERF

Abstract

There is a lack of concise design knowledge for extracting innovation features that characterize diffusion levels while providing actionable managerial insights. Against this background, two approaches currently exist in the literature: On the one hand, studies applying diffusion theory provide interpretable, theory-aligned insights but neglect predictive accuracy. On the other hand, machine learning-driven approaches optimize predictive performance but disregard theoretical coherence and managerial interpretability. Consequently, there is a need for systematically derived design knowledge guiding researchers and practitioners in building theory-grounded, yet predictively powerful innovation analysis tools. Responding to that need, this article presents a design science artifact, Guided Innovation Feature Miner (GIFM). GIFM integrates Rogers' diffusion of innovations theory with graph convolutional networks and hierarchical attention mechanisms to extract theory-aligned features from unstructured patent texts. Evaluation across 31,804 cybersecurity patents demonstrates that complexity (simplicity) and observability contribute most to diffusion prediction, while compatibility has the least influence.

Paper Number

1799

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Aug 15th, 12:00 AM

Guided Innovation Feature Miner: A Design Science Artifact for Studying Innovation Diffusion with Deep Learning

There is a lack of concise design knowledge for extracting innovation features that characterize diffusion levels while providing actionable managerial insights. Against this background, two approaches currently exist in the literature: On the one hand, studies applying diffusion theory provide interpretable, theory-aligned insights but neglect predictive accuracy. On the other hand, machine learning-driven approaches optimize predictive performance but disregard theoretical coherence and managerial interpretability. Consequently, there is a need for systematically derived design knowledge guiding researchers and practitioners in building theory-grounded, yet predictively powerful innovation analysis tools. Responding to that need, this article presents a design science artifact, Guided Innovation Feature Miner (GIFM). GIFM integrates Rogers' diffusion of innovations theory with graph convolutional networks and hierarchical attention mechanisms to extract theory-aligned features from unstructured patent texts. Evaluation across 31,804 cybersecurity patents demonstrates that complexity (simplicity) and observability contribute most to diffusion prediction, while compatibility has the least influence.

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