Relationships Between AI Tools, Social Media, and Performance via Ensemble Bayesian Network: A Survey Among Chinese Lawyers

Amidst the rapid digital transformation reshaping the legal profession globally, this study examines the interplay between AI tools, social media usage, and lawyer job performance in China. While prior research has extensively explored factors influencing the job performance of lawyers, due to the relatively small number of lawyers in China and the legal and ethical limitations in their use of social media and AI tools, systematic investigations into the roles of AI and social media in this context remain limited. We use an ensemble Bayesian network model to examine causal mechanisms, analyzing 313 questionnaires on their use of AI and social media. This study constructs a robust causal network to analyze the impacts of nine key variables, including excessive social use of social media at work, AI-supported employee training and development, AI-driven workload reduction for employees, and strain, among others. The findings reveal that AI-driven workload reduction, AI-supported leadership, and strain directly influence lawyer job performance. Notably, excessive cognitive use of social media at work (ECU) exerts the most significant impact, while strain and work–technology conflict serve as critical mediators in the relationship between ECU and performance. The ensemble Bayesian network framework not only enhances the methodological rigor of this research but also facilitates a comprehensive understanding of the complex interdependencies among the considered factors. Based on the results, practical recommendations are proposed for the optimization of the job performance of lawyers. This study contributes to the growing body of literature on lawyer job performance through the introduction of an advanced analytical approach, as well as offering actionable insights for law firms and informing legal technology legislation and policy development navigating the digital era.