Optimizing Monetization Strategies for Generative AI Firms: Implications for Search Engagement
通过四个实验（总样本1063人），研究了广告支持的盈利模式如何影响用户升级或降级决策，发现提供单一广告选项会增强折中效应，而提供两个选项则能平衡用户留存与升级。
ABSTRACT As Generative Artificial Intelligence (GenAI) platforms, such as ChatGPT, have transformed digital search querying behavior, mounting operational costs challenge firms to explore alternative monetization strategies beyond traditional subscription models. However, little is known about how alternative advertising‐supported monetization models can help GenAI firms recover costs while maintaining search query engagement. Drawing on the compromise effect and affective primacy theories, we develop a framework wherein the introduction of advertising‐supported monetization models influences user upgrading and downgrading decisions, contingent on the number of available monetization options. Across four experiments ( N = 1063), findings reveal that introducing a single advertising‐supported option enhances the compromise effect, encouraging free users to upgrade, but leading paid subscribers to downgrade. However, offering two advertising‐supported models mitigates the effect, maintaining subscriber retention while still motivating free users to upgrade. We show that affective and cognitive evaluations serially mediate preference for advertising‐supported models, with temporal intrusiveness, but not visual, moderating these effects. We provide actionable insights for GenAI firms on potentially optimizing revenue strategies while balancing user engagement with search queries on their platform.