Chatbot Integration on a Knowledge Sharing Platform: A Competitive Analysis

This study examines the strategic challenges facing knowledge sharing platforms competing with general Large Language Models (LLMs) service providers. We investigate how platforms can leverage their user-generated content (UGC) while balancing traditional content against AI chatbots. Using a game-theoretic model based on a spoke network structure, we reveal that knowledge sharing platforms should balance direct competition effects between UGC and chatbots against indirect effects where UGC enhances chatbot quality. We find that a platform's optimal chatbot quality investment strategy depends critically on its UGC integration capability. With low integration capability, the knowledge sharing platform increases chatbot quality as UGC sensitivity increases to compensate for competition effects. Market value significantly affects outcomes—increasing UGC sensitivity strengthens platforms in low-value markets but creates vulnerabilities in high-value markets where LLM providers gain advantage through aggressive quality investments. These findings provide strategic guidance for platforms navigating the AI transition while highlighting market concentration concerns.