Abstract On-demand ride-sourcing platforms have quickly emerged and become ubiquitous in our daily lives. Motivated by the rising public concern about service quality in the ride-sourcing market, this paper aims to examine the impact of exclusion policy that can serve as both quality management and supply regulation strategy. With an exclusion policy, the platform excludes low-quality service providers/drivers from the ride-sourcing market by setting a quality threshold of admission (QTA). We propose a model to describe and analyze the market equilibrium under the exclusion policy and present our analytical and numerical results – some of which are non-intuitive and intriguing. Considering network effects captured by both quality and quantity of service in the market, we find that both the realized supply and total labor welfare are not reduced but rather increase when the platform implements a well-designed exclusion policy. Whereas the platform’s profit, customer surplus, labor welfare, and social welfare are each non-monotonic in the QTA level – first increasing and then decreasing – a moderate level of QTA can benefit all stakeholders (platform, drivers, and passengers) in the market. We also analyze the model with alternative network effects and present the theoretical and numerical results. Using the model, we further identify the principal factors in determining the optimal exclusion policy; these include passengers’ quality preference, labor pool size, and quality differentiation among registered drivers.

    Highlights We examine the impact of supply regulation under an exclusion policy. A well-designed exclusion policy increases realized supply and total labor welfare. Platform profit, customer surplus and labor welfare are each non-monotonic in QTA. QTA relies on passenger quality preference and drivers’ size and quality difference.


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    Titel :

    Supply regulation under the exclusion policy in a ride-sourcing market


    Beteiligte:
    Li, Xiaonan (Autor:in) / Li, Xiangyong (Autor:in) / Wang, Hai (Autor:in) / Shi, Junxin (Autor:in) / Aneja, Y.P. (Autor:in)


    Erscheinungsdatum :

    2022-10-12


    Format / Umfang :

    26 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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