Control of Online-Appointment Systems When the Booking Status Signals Quality of Service

Isabel Kaluza (), Guido Voigt, Knut Haase and Antonia Dietze
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Isabel Kaluza: University of Hamburg
Guido Voigt: University of Hamburg
Knut Haase: University of Hamburg
Antonia Dietze: University of Hamburg

Schmalenbach Journal of Business Research, 2024, vol. 76, issue 3, 397-432

Abstract: Abstract We revisit a service provider’s problem to match supply and demand via an online appointment system such as a doctor in the health care sector. We identify in a survey that an extensive set of available appointments leads to significantly less demand because customers infer a lower quality of the service, as part of an observational learning process. We capture the quality inference effect in a multinomial logit framework and present a Markov decision process for solving the problem of releasing available slots of the appointment system to optimality aiming at maximizing the expected profits. We further evaluate several simple decision rules and provide management insights on which rule to apply under different generic scenarios. Different from current literature, offering all available appointments may lead to suboptimal results when accounting for the quality inference effect. The profit-maximizing strategy then is to offer a subset of the available appointments.

Keywords: Appointment System; Choice-Based Optimization; Discrete Choice; Stochastic Dynamic Programming; Markov; C61 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s41471-024-00188-0

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