Identity Transformation as a Driver of AI Adoption: The Role of Broad vs. In-Depth Thinking in Manager–AI Collaboration
(A2026-133938)

AUTHORS

Felix Schakols, University of St. Gallen; Jaeyeon Chung, Rice University; Reto Hofstetter, University of St.Gallen

ABSTRACT

Artificial intelligence (AI) is increasingly used to support creative work, yet many managers remain hesitant to adopt AI tools. Prior explanations focus on skill gaps or quality concerns, but adoption decisions may also depend on how AI influences managers’ self-perceptions and identity. This research introduces an identity-based account of managerial AI adoption and examines how different cognitive styles in human–AI collaboration shape managers’ willingness to adopt AI. Building on the theory of the extended self, we propose that effectively collaborating with AI can produce identity transformation, leading managers to experience themselves as more creative professionals, ultimately increasing AI adoption intention. Across three studies using managerial samples, we examine how different cognitive thinking styles when working with AI affect identity transformation and adoption intentions. Taken together, we establish identity transformation as a key driver of managerial AI adoption.