This study investigates the structural mechanism through which firms translate AI-enabled synergistic invention capabilities (AI-SIC) into exploratory innovation, addressing the governance challenges of algorithmic variance within digitalized R&D architectures.
Grounded in the dynamic capabilities view (DCV), the study employs a quantitative cross-sectional design. Data were collected from 328 high-tech R&D executives (CTOs, CIOs and R&D Directors) in Taiwan and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).
The empirical results reveal a full mediation mechanism. Raw AI-SIC does not directly yield exploratory innovation; rather, its strategic value is entirely channeled through R&D process agility. This agility acts as a crucial cognitive filter, enabling organizations to mitigate stochastic algorithmic variance and potential technological hallucinations.
This research extends the DCV into the algorithmic era by shifting the scholarly focus from mere digital technology adoption to internal variance governance. It conceptualizes the novel multidimensional AI-SIC construct and reconceptualizes R&D process agility as an indispensable dynamic capability for navigating generative AI. Crucially, this study contributes primarily to Dynamic Capabilities research while informing the digital innovation literature.
