Module-Targeted AI Chatbot Deployment and Counselors’ Prosocial Contributions in Online Health Community

Strategically introducing artificial intelligence (AI) chatbots into online health communities (OHCs) to foster user engagement has become increasingly common, but it also creates competitive pressure and replacement risks for professional counselors who make similar prosocial contributions. We investigate how module-targeted AI chatbot deployment affects professional counselors’ prosocial contributions across a focal module (with AI deployment) and another neighbor module (without AI deployment). We developed a theoretical framework by integrating Protection Motivation Theory with the displacement effect, validating our conjectures using a longitudinal dataset from a leading Chinese OHC. Utilizing a difference-in-differences (DID) approach with rigorous robustness checks, we find that AI deployment significantly bolstered counselors’ prosocial contributions in the focal module, while neighbor module activity remained stable. The key mechanism underlying counselors’ heterogeneous responses lies in individual competence rather than economic incentives. Specifically, high-competence counselors exhibited a pronounced bifurcated response: they amplified both the quantity and semantic quality of their contributions in the focal module, while simultaneously retrenching along both dimensions in the neighbor module. Our findings reveal how counselors adapt their prosocial efforts when AI is deployed in a specific module and the displacement effects caused by such asymmetric intervention, providing practical insights for platform managers to deploy module-targeted AI chatbots effectively.