Information Systems Faculty Publications
Document Type
Article
Publication Date
5-20-2026
Abstract
In the rapidly evolving generative artificial intelligence (GenAI) ecosystem, downstream application providers face persistent challenges in designing optimal pricing strategies. These challenges arise from dual cost pressures: application programming interface (API) costs imposed by upstream model platforms and psychological frictions experienced by end consumers. This study develops a game-theoretic model to analyze application providers’ strategic choice between subscription-based and usage-based pricing under both monopolistic and duopolistic market structures. The analysis reveals that the optimal strategies are shaped by a nonlinear interplay among psychological costs, model API costs, and consumer valuation. Under low psychological-cost conditions, API costs exert an inverted -shaped effect on competing providers’ profits. By contrast, under high API-cost regimes, subscription-based and usage-based models exhibit asymmetric sensitivities to psychological costs: profits under usage-based pricing decline with rising psychological costs, whereas subscription-based profits stabilise. Fthermore, elevated API and psychological costs attenuate competitive intensity, driving duopolistic equilibria toward monopoly-like outcomes. These findings remain robust after incorporating a broad set of realistic market frictions. The results provide actionable insights for GenAI application providers balancing cost pass-through, user engagement, and competitive positioning in pricing decisions.
Recommended Citation
Li, L., Zhang, Y., Sun, J. and Yang, Z., 2026. Nonlinear trade-offs in GenAI application pricing: a game-theoretic analysis of API and psychological costs. International Journal of Production Research, pp.1-22. https://doi.org/10.1080/00207543.2026.2674300
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Publication Title
International Journal of Production Research
DOI
10.1080/00207543.2026.2674300
Comments
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on May 20, 2026, available at: https://doi.org/10.1080/00207543.2026.2674300