On-Demand Service Platforms
By Terry A. Taylor. Manufacturing & Service Operations Management, 2018
509 citations recorded by the source corpus.
An on-demand service platform connects waiting-time sensitive customers with independent service providers (agents). This paper examines how two defining features of an on-demand service platform -- congestion-driven delay disutility and agent independence -- impact the platform's optimal per-service price and wage. Congestion reduces expected utility for customers and agents, which suggests that the platform respond by decreasing the price (to encourage participation of customers) and increasing the wage (to encourage participation of agents). These intuitive price and wage prescriptions are valid in a benchmark setting without uncertainty in the customers' valuation or the agents' opportunity costs. However, uncertainty in either dimension can reverse the prescriptions: Congestion increases the optimal price when customer valuation uncertainty is moderate. Congestion decreases the optimal wage when agent opportunity cost uncertainty is high and expected opportunity cost is moderate. Under agent opportunity cost uncertainty, agent independence decreases the price. Under customer valuation uncertainty, agent independence increases the price if and only if valuation uncertainty is sufficiently high.
Subfields: Automotive Engineering, Management Information Systems, Marketing
View Paper: https://www.semanticscholar.org/paper/7e38338b488e223f12af59c8c88e30cde482cf10
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Citations in the corpus
- Pricing and equilibrium in on-demand ride-pooling markets (2020, background): “Other specific research includes the coordination of demand and supply using price and wage ( Bai et al., 2018 ; Taylor, 2018 ); pricing and surge-pricing strategies ( Cachon et al., 2017 ; Castillo et al., 2017 ; Zha et al., 2016 ; Yang et al., 2020b ; Chen et al., 2020 ); government regulations…”
- Last-mile delivery concepts: a survey from an operational research perspective (2020, background): “2017), pricing for on-demand services (Taylor 2018), and (long-haul) freight pricing (Holguín-Veras 2011).”
- Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism (2020, unknown): “, 2016), consumer choice modeling (Xu & Yun, 2020), ride-sharing (Taylor, 2018; Gurvich et al., 2018; Bimpikis et al., 2019; Kanoria & Qian, 2019), healthcare operations (Shortreed et al.”
- Ridesourcing systems: A framework and review (2019, background): “Taylor (2018) also uses a queueing formulation with a two-point distribution for passenger and driver valuation, derives the demand rate and number of drivers in equilibrium for a given price and wage, and finds that uncertainty in passenger delay sensitivity and driver independence may change the…”
- Regulating TNCs: Should Uber and Lyft set their own rules? (2019, background): “It is shown in [21] that platform pricing can be more complicated when there is uncertainty in passenger’s valuation or driver’s opportunity cost.”
- Between autonomy and control: Strategies of arbitrage in the “on-demand” economy (2018, background): “…of “nudging” (cf. Scheiber, 2016, 2017); This is clear in the literature on workforce management in the on-demand economy (e.g. Cachon et al., 2016; Taylor, 2016), which frames the incentives and disincentives that companies have at their disposal as “control levers” that managers can push and…”
- Surge pricing and labor supply in the ride-sourcing market (2018, background): “However, current analytical models are mostly static and often consider a stationary state to simplify policy evaluation (He and Shen, 2015; Taylor, 2016; Wang et al., 2016; Zha et al., 2016).”
- Dynamic pricing and matching in ride‐hailing platforms (2019, unknown): “Such equilibrium analysis under a steady-state model setting has also been utilized by quite a few literature studying ride-hailing marketplace (Banerjee et al. 2015, Taylor 2017, Bai et al. 2017, Cachon et al. 2017).”