Value-based or one-time? Optimal pricing modes for generative AI services in e-commerce platforms

Abstract

With the rapid advancement of generative artificial intelligence (GAI) technology, e-commerce platforms are increasingly integrating GAI services to enhance product design, manufacturing, and sales processes. Despite this trend, the extant literature lacks systematic investigation into platform pricing for such services, particularly in choosing between a one-time fixed fee mode (a fixed fee for adopting GAI services) and a value-based commission mode (a fixed fee plus a commission on sales above a threshold). To address this gap, this study develops a novel two-part tariff contract to optimize the pricing mode for GAI services. Incorporating factors such as platform investment in GAI, investment cost coefficient, commission rate, and sales quantity threshold, our game-theoretic analysis reveals nuanced insights. Interestingly, our findings reveal that a value-based commission mode may not always align with the platform's interest. As the investment cost coefficient increases, the platform tends to favor a one-time fixed fee mode. Moreover, under the value-based commission mode, a higher sales quantity threshold does not necessarily benefit the manufacturer. We also identify a win-win region in which the value-based commission mode benefits both the platform and the manufacturer. Finally, additional analyses extend our findings to scenarios involving enhanced GAI efficiency and competitive market settings. This research advances the literature on AI pricing and two-part tariff theory, while offering practical insights for platform operators and manufacturers.