Optimizing monetization strategies for generative AI firms: implications for search engagement

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.

More information

Identifiers

ORCID for Paurav Shukla: orcid.org/0000-0003-1957-8622

Catalogue record

Export record

Altmetrics