Investigate Determinants and Configuration of Human AI Use: Combining PLS-SEM and fsQCA Analysis

This study proposes an integrated model to examine how personal beliefs, interactive qualities, and technical characteristics jointly shape the intention to use AI. Using data from 281 employees and applying a mixed-method approach (PLS-SEM and fsQCA), we found that personal beliefs, interactive qualities, and technical characteristics positively influence the intention to use AI, mediated by attitudes toward AI. Furthermore, trust in AI negatively moderates the attitude-intention relationship. The fsQCA results further revealed three distinct pathways to high usage intention: the Holistic & Trust-Driven Pathway, the Flexible & Interaction-Driven Pathway, and the Pragmatic & Mandated-Use Pathway. This study contributes to the AI acceptance literature by 1) extending the TAM by integrating personal beliefs and interactive quality into AI acceptance research, 2) demonstrating that trust negatively moderates the attitude–intention relationship, and 3) identifying three equifinal, nonlinear configurations that offer a nuanced, human-centered framework for contextualized AI design and organizational governance.