What Drives Effective AI Use in the Newsroom? Communication Barriers, Organizational Support, and Journalist Performance in China

As artificial intelligence reshapes professional workflows, understanding what drives effective AI use among employees has become a critical concern for organizations. Moving beyond traditional technology acceptance frameworks, this study develops an integrative multi-level model to examine the behavioral determinants of AI use performance (AUP) among journalists. Drawing on the Technology Acceptance Model (TAM) and the Expectation Confirmation Model (ECM) and incorporating individual and organizational factors, a survey was conducted among 543 journalists in China. Hypotheses are tested via a hybrid PLS-SEM and artificial neural network (ANN) approach to capture both linear and non-linear relationships. The findings reveal that expectation confirmation significantly enhances AUP by driving perceived usefulness and satisfaction. Digital literacy, personal trust in AI, and organizational support positively influence AUP, whereas communication barriers exert the strongest negative effect. Demographic variables (gender, age, education) show no significant impact. Notably, the ANN sensitivity analysis identifies communication barriers as the most influential predictor overall, a finding not apparent from linear analysis alone. This study advances theoretical understanding of employee behavioral responses in AI-integrated professional contexts and offers practical insights into how organizations can foster effective employee–AI collaboration through targeted communication strategies and supportive infrastructure.