Dynamic control of pure content quality and recommendation intensity on UGC platform considering reference effect

Many User-Generated Content (UGC) platforms are transitioning towards a new business model where content creators engage users with pure content, while platforms’ revenue increasingly relies on shoppable content. This paper investigates the strategic balance between pure content and shoppable content on UGC platforms, and how this balance, along with dynamic pure content quality and consumer reference quality uncertainty, affects profit optimization for both UGC platforms and content creators. Employing a game-theoretic framework, we model three scenarios: (1) static pure content quality and recommendation intensity (i.e., the frequency of pure content recommendations or space allocation of pure content recommendations); (2) dynamic quality and recommendation intensity without uncertainty in reference quality; and (3) dynamic scenarios with uncertainty in reference quality. Our findings indicate that adopting dynamic strategies can enhance platform profits compared to the static scenario, but it may reduce content creators’ earnings relative to a static environment. Interestingly, while dynamic uncertainty leads to a decline in profits for the UGC platform, it also offers content creators opportunities to increase their profits. When facing higher uncertainty in reference quality, the UGC platform should increase recommendation intensity, while content creators will reduce the quality of their output.