Pricing decisions for service platform with provider’s threshold participating quantity, value-added service and matching ability

Liu, Weihua; Yan, Xiaoyu; Wei, Wenting; Xie, Da

doi:10.1016/j.tre.2018.12.020

Cited by 96 publications

(68 citation statements)

References 36 publications

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“…Sun et al [13] were the first to take into account both ride details and driver locations and assumed that drivers and customers maximize the utility to determine the optimal pricing strategy for online ride-hailing platforms. Liu et al [16] studied the pricing decisions of a profit-maximizing platform by considering the provider's threshold participating quantity, value-added service (VAS), and matching ability. Wu et al [50] considered spatial differentiation and network externality in the pricing mechanism of an online car-hailing platform and analyzed the game competition among platform users and the impact on the platform's optimal pricing.…”

Section: Ride-hailing Platformsmentioning

confidence: 99%

“…For simplicity, our model assumes away the spatial dimension (e.g., Cachon et al [8]; Hu and Zhou [4]). (3) Referring to Liu et al [16], the demand rate for ridehailing platforms is sensitive to price and service quality and is assumed to be a linear function of the price:…”

Section: Problem Description and Model Assumptionsmentioning

confidence: 99%

“…For simplicity, we assume the driver's minimum participation cost per time unit ε � 0. is assumption does not affect the results. As in [8] and [16], for the numerical analysis, we first provide the parameters (Table 5) used in the different scenarios. Sections 4.1, 4.2, and 4.3, we present the optimal solutions for the pricing decisions of three market demand situations: decreasing, increasing, and stable.…”

Section: Numerical Analysismentioning

confidence: 99%

“…Determining how to match passenger supply and demand by optimally setting platform prices is an important issue. In this study, we contribute to the literature on demand function and supply function (e.g., Cachon et al [8]; Hu and Zhou [4]; Liu et al [16]). In considering the scenarios of both decreasing and increasing passenger demand, we investigate the dynamic pricing policies of a ride-hailing platform and obtain the optimal pricing strategies under different market demand situations.…”

Section: Conclusion Managerial Insights and Future Researchmentioning

confidence: 99%

“…We incorporate the rider demand fluctuation into the common demand function (Desiraju and Moorthy [15]) and use the coefficient of demand fluctuation to describe different demand conditions. Unlike the previous studies (e.g., Cachon et al [8]; Hu and Zhou [4]; Liu et al [16]), we assume rider demand is an exponential function of time and is sensitive to price and service quality. is means rider demand changes dynamically over time.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Pricing of Ride-Hailing Platforms considering Service Quality and Supply Capacity under Demand Fluctuation

Sun

Shi

2020

Mathematical Problems in Engineering

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Increasing attention is being paid to the pricing decisions of ride-hailing platforms. These platforms usually face market demand fluctuation and reflect supply and demand imbalances. Unlike existing studies, we focus on the optimal dynamic pricing of the platforms under imbalance between supply and demand caused by market fluctuation. Dynamic models are constructed based on the state change of supply and demand by using optimal control theory, with the aim of maximizing the platform’s total profit. We obtain the optimal trajectories of price, supply, and demand under three ride demand situations. The effects of some key parameters on pricing decisions, such as coefficient of demand fluctuation, service quality, and fixed commission rate, are examined. We find the optimal dynamic price can improve the match of supply-demand in ride-hailing market and enhance the revenue of platform.

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Section: Ride-hailing Platformsmentioning

confidence: 99%

Section: Problem Description and Model Assumptionsmentioning

confidence: 99%

Section: Numerical Analysismentioning

confidence: 99%

Section: Conclusion Managerial Insights and Future Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Pricing of Ride-Hailing Platforms considering Service Quality and Supply Capacity under Demand Fluctuation

Sun

Shi

2020

Mathematical Problems in Engineering

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Impacts of differentiated services and competition on the pricing strategies of ride‐hailing platforms

Sun

Liu

2023

Manage Decis Econ

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Differentiated services and competition have made the operation of ride‐hailing platforms (i.e., Uber and Didi) more complex. Timely optimizing their pricing strategies will be conducive to the profitability of the platform enterprises. This paper considers the differentiation in service level of two ride‐hailing brands and the Bertrand price competition between platforms, and we propose dynamic, differentiated, and static pricing strategies based on the market demand fluctuation. The optimal prices in each case are derived, and the impacts of the Bertrand competition and differentiated services on the platform's optimal pricing strategies are analyzed. Our results show that competition between platforms will change the impact of the demand fluctuation on the optimal decisions of the platform. Additionally, we find that the optimal prices of two ride‐hailing brands all increase with the coefficient of service differentiation, but the optimal price of the brand with higher service level is increased faster. The numerical studies indicate that if one of the two platforms gives priority to offering differentiated services, the profit loss caused by competition can be attenuated to a great extent.

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Compatibility and distribution strategies of asymmetric competing digital platforms

Zhang,

Cai,

Wang

et al. 2023

Manage Decis Econ

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We develop a game theoretic model to study the compatibility and distribution decisions of two asymmetric competing platform owners. It is found that there are two equilibrium distribution structures for the two platform firms. Then a Pareto optimality distribution structure for the two firms is discovered. We proceed to discuss the platform's compatibility decision under the equilibrium distribution structures, and find that the equilibrium compatibility decisions depend on the average royalty. We further examine the impact of firm's distribution decisions over their compatibility decisions. Finally, we discuss how retailers will benefit from platform owner's compatibility decisions.

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Pricing decisions for service platform with provider’s threshold participating quantity, value-added service and matching ability

Cited by 96 publications

References 36 publications

Dynamic Pricing of Ride-Hailing Platforms considering Service Quality and Supply Capacity under Demand Fluctuation

Dynamic Pricing of Ride-Hailing Platforms considering Service Quality and Supply Capacity under Demand Fluctuation

Impacts of differentiated services and competition on the pricing strategies of ride‐hailing platforms

Compatibility and distribution strategies of asymmetric competing digital platforms

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