Chen, L., & Sheldon, R. (2016). Dynamic pricing in a ride-sharing platform. Management Science , 62(9), 2583–2608.
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collusion. American Economic Review , 110(10), 3267–3297. surfly pricing
The gap in literature is the convergence of surge timing with behavioral personalization—a gap this paper fills by defining Surfly Pricing as a distinct category. | Feature | Traditional Dynamic Pricing | Surfly Pricing | |---------|----------------------------|----------------| | Trigger | Aggregate demand (e.g., seats left, days to departure) | Individual behavior + device signals + real-time demand | | Update frequency | Daily or hourly | Sub-second (per click/refresh) | | Transparency | Fare rules published | Opaque; user cannot see why price changed | | Segmentation | Discrete fare classes (Y, B, M, etc.) | Continuous; each user sees a unique price | | Primary goal | Maximize load factor × yield | Maximize willingness-to-pay extraction per session | Chen, L
Chen, Y., & Zhang, J. (2024). When your phone battery sets the price: Device-state pricing in e-commerce. Journal of Marketing Research , 61(2), 210–228. Management Science , 62(9), 2583–2608