Investigating <scp>SME</scp> ‐Inclusive Public‐Private <scp>AI</scp> Innovation Network: Evidence From U.S. Small Business Administration (1991–2023)

ABSTRACT Understanding innovation policy is pivotal to enhancing national competitiveness for global value chain. This research investigates an SME‐Inclusive public‐private AI innovation network over time in the U.S. context (1991–2023), revealing how a government's demand‐based innovation policy bolsters technological prowess through isomorphism and technology maturity. Process research grounded in an interpretive approach was conducted by using topic modeling, an unsupervised machine learning technique to uncover latent themes. The results uncovered the evolution of the constituting actors, resource flows, and thematic priorities of the public‐private AI innovation network over time. The findings from process research complement existing literature rooted in causality‐seeking variance research with this methodological innovation, uncovering actor constitution, resource arrangements, and thematic priorities within the public‐private AI innovation network. The findings provide theoretical insights into institutional theory and organizational change, as well as practical implications for public policy to promote social impacts and drive economic empowerment.