2025/05/01 by Yulong Liu, Yulong David Liu, Justin Zuopeng Zhang +2 · 25 citations
Business, Management and Accounting · #Business #Business Strategy and Innovation #Collaboration in agile enterprises #Computer science #Industrial organization #Innovation and Knowledge Management #Knowledge management #Process management
paper · doi:10.1016/j.indmarman.2025.04.003
published in Industrial Marketing Management 127, 164-174 (Elsevier BV)
openalex publication_date 2025/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
In today's volatile business environment, B2B enterprises are increasingly relying on artificial intelligence-enabled information systems to support strategic responsiveness and enhance innovation outcomes. Drawing on dynamic capability theory, this study examines how AI-enabled systems improve decision-making performance and, in turn, foster innovation. Using data from 246 B2B firms in Australasia, we find that decision-making performance significantly mediates the relationship between AI adoption and innovation performance. Our findings reveal that strategic agility significantly moderates this mediated relationship, amplifying innovation performance when agility is present at moderate levels but plateauing when agility becomes excessive. We also explore the moderating roles of decision-making styles (intuitive, experience-based, rational), though these effects were not statistically significant. Nonetheless, both rationality and experience-based processing show significant direct effects on decision-making performance, highlighting the relevance of cognitive traits in digitally enabled decision contexts. By unpacking the complex interactions between digital technologies, cognitive styles, and organizational agility, this study advances a more nuanced understanding of innovation enablers in B2B settings. The findings offer theoretical and practical insights into the alignment of technological, cognitive, and strategic capabilities to drive innovation outcomes.