Reasons for and against GenAI: Trait-driven adoption under open innovation dynamics

This study applies Behavioral Reasoning Theory (BRT) to examine how individual agility and resilience shape the adoption of Generative AI (GenAI). Specifically, it investigates how these psychological traits influence both reasons for and reasons against adoption, which subsequently affect attitudes and intentions, while also assessing the moderating role of reference-group influence. Data were obtained from 609 Thai respondents through a structured survey and analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that agility (β = 0.396, p < 0.001) and resilience (β = 0.311, p < 0.001) positively predict reasons for adoption, while both negatively predict reasons against adoption (agility: β = –0.260, p < 0.001; resilience: β = –0.185, p < 0.001). Reasons for adoption significantly enhance attitudes toward GenAI (β = 0.281, p < 0.001), whereas reasons against adoption do not exert a significant effect (β = –0.090, p = 0.079). Attitude strongly predicts intention to adopt (β = 0.558, p < 0.001). Mediation analysis indicates that reasons for adoption partially mediate the agility–attitude relationship and provide indirect-only mediation for resilience–attitude. Furthermore, reference-group influence moderates the attitude–intention link (β = 0.146, p = 0.007), amplifying the translation of positive attitudes into adoption intentions. Overall, the findings extend BRT by integrating psychological antecedents and social context into dual-path reasoning, thereby offering new theoretical and practical insights into technology adoption within open-innovation dynamics. The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy restrictions related to participant confidentiality. • Investigates generative AI adoption using Behavioral Reasoning Theory (BRT). • Explores dual-path reasoning through reasons for and reasons against adoption. • Emphasizes the role of individual agility and resilience in shaping adoption attitude. • Demonstrates reference group influence as a significant moderator of intention. • Provides insights for policymakers and designers of responsible AI ecosystems.