Leveraging AI, Knowledge Search, and Organizational Learning for Innovation: A Multi-Mediation Model of Dynamic Capabilities and Absorptive Capacity in Medium-Sized Manufacturing Firms

บทคัดย่อ

The proliferation of Artificial Intelligence (AI) and Big Data Analytics (BDA) has significantly transformed innovation processes across industries. However, medium-sized enterprises (MSEs) often face challenges in leveraging these technologies due to resource constraints and limited organizational readiness. This study investigates how AI-driven big data capability, knowledge search, and organizational learning affect innovation performance, mediated and moderated by absorptive capacity and dynamic capabilities. Drawing on the Resource-Based View (RBV), Dynamic Capabilities View (DCV), and absorptive capacity theory, a sequential mixed-methods longitudinal design was applied. Survey data were collected from 204 MSEs across Southeast Asia, South Asia, and Eastern Europe, with a one-year longitudinal sub-sample of 93 firms. Data were analyzed using SmartPLS 4.0. The results demonstrate that AI-driven big data capability, knowledge search, and organizational learning significantly enhance innovation performance, both directly and indirectly, through absorptive capacity and dynamic capabilities. Moreover, dynamic capabilities serve as both mediator and moderator, while absorptive capacity strengthens the knowledge–innovation link. Importantly, an inverted U-shaped relationship was confirmed between AI intensity and innovation, highlighting risks of over-reliance. This research advances theory by integrating technological and organizational mechanisms in a unified framework and provides practical guidance for MSEs on balancing AI adoption with organizational readiness.