The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

By Gérard P. Cachon, Kaitlin M. Daniels, Ruben Lobel. Manufacturing & Service Operations Management, 2017

667 citations recorded by the source corpus.

Recent platforms, like Uber and Lyft, offer service to consumers via "self-scheduling" providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a "surge pricing" policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage or both, and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity. The e-companion is available at https://doi.org/10.1287/msom.2017.0618 .

Subfields: Automotive Engineering, Marketing, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/c19e091f6ceda2e0f27197fb222826f90a3a10dc

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Citations in the corpus

Dynamic pricing and matching in ride‐hailing platforms (2019, neutral): “…time horizon: experienced drivers on ride-hailing platforms eventually become cognizant about the time periods and areas of high earnings due to high demand and surge pricing, and ﬂexibly adjust their work schedule to drive during these time periods (Chen and Sheldon 2016, Cachon et al. 2017).”
Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers (2019, neutral): “max k,λ π(k, λ) ≡ λd [ F−1(1− λ λ̄ )− c d Wq −G−1( k K ) k λd ] , subject to λd kμ < 1, (8)”
Between autonomy and control: Strategies of arbitrage in the “on-demand” economy (2018, background): “For example, economic modeling of the on-demand business model suggests that relative to industry standard market wage competition, companies that adhere to various indicators of autonomy—worker self-scheduling, their right to accept or reject any work-order, and so on—can expect smaller profit marg”
Ridesourcing systems: A framework and review (2019, background): “In favor of dynamic or surge pricing, Cachon et al. (2017) study several pricing schemes and find that the optimal dynamic contract substantially increases the platform’s profit by comparison with contracts that have a fixed price or fixed wage, although surge pricing is not optimal, it generally ac”
Regulating TNCs: Should Uber and Lyft set their own rules? (2019, background): “A similar question is studied in [19], with a focus on the self-scheduling capacity of for-hire drivers.”
The Role of Inclusive Innovation in Promoting Social Sustainability (2019, background): “For instance, decisions on surge pricing of Uber (Cachon et al. 2017), designing crowdfunding rules for Kickstarter (Babich Kalkanci, Rahmani, and Toktay: Inclusive Innovation & Social Sustainability 2966 Production and Operations Management 28(12), pp.”
The organisation and experience of work in the gig economy (2019, background): “Other characteristics include dynamic prices and pay rates that aim at eliminating capacity rationing (Cachon et al., 2017).”
On-Demand Service Platforms (2018, neutral): “The reason for the difference in price findings is, in part, that in the newsvendor-style model of Gurvich et al. (2018) (and that of Cachon et al. 2017), demand does not depend on the service level, whereas in our queueing model it does; see Section 3.1.”
Socially and Environmentally Responsible Value Chain Innovations: New Operations Management Research Opportunities (2017, background): “Industry trends and emerging needs have motivated OM researchers to go beyond traditional OM areas like quality, inventory, production planning, queueing, and scheduling to examine healthcare management, service management, revenue management, design and product innovations, interfaces with marketin”