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  1. Observational learning in large-scalecongested service systems
    Autor*in: Jin, Chen; Debo, Laurens; Iravani, Seyed
    Erschienen: [2017]
    Verlag:  [Tuck School of Business at Dartmouth], [Hanover, NH]

    We study the impact of observational learning in large scale congested service systems with servers having heterogenous quality levels and customers that are heterogonously informed about the server quality. Providing congestion information to all... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    We study the impact of observational learning in large scale congested service systems with servers having heterogenous quality levels and customers that are heterogonously informed about the server quality. Providing congestion information to all customers allows them to avoid congested servers, but, also implies that less informed customers learn about the quality from observing the choices of other customers. Due to an exponentially growing state space in the number of servers, identifying Bayesian equilibria is intractable with a large, discrete number of servers. In this paper, we develop a tractable model with a continuum of servers. We find that the impact of observational learning on the customers' choice behavior may lead to severe "imbalance" of server load in the system, such that a decentralized system significantly under-performs in terms of the social welfare, compared with a centralized system. The decentralized system performs well only when (a) either the congestion costs are high and there are sufficient informed customers, or (b) when the congestion costs are medium or low and the aggregate capacity of high-quality servers matches the aggregate demand of informed customers. We also find situations in which making more customers informed about service quality leads to a decrease in social welfare. Our paper highlights the tension between observational learning and social welfare maximization and thus observational learning in large-scale service systems might require intervention of the platform manager

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: [Tuck School of Business working paper ; no. 3054728]
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  2. Under-Promising and Over-Delivering to Improve Patient Satisfaction at Emergency Departments
    Evidence from a Field Experiment Providing Wait Information
    Autor*in: Ansari, Sina; Debo, Laurens; Ibanez, Maria; Iravani, Seyed; Sanjeev Malik, M.D.
    Erschienen: 2022
    Verlag:  SSRN, [S.l.]

    Overcrowded Emergency Departments (EDs) across locations struggle to improve patient experience while dealing with long waits, which erodes medical and financial performance. We investigate whether and how managers could improve patient satisfaction... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Overcrowded Emergency Departments (EDs) across locations struggle to improve patient experience while dealing with long waits, which erodes medical and financial performance. We investigate whether and how managers could improve patient satisfaction by communicating waits to patients.We conduct a field experiment at an urban ED. We develop a machine learning-based wait time prediction application and implement it within the electronic medical records system. Our treatments provide patients with personalized estimated waits with no overestimation (the median), moderate overestimation (70th-percentile), or high overestimation (90th-percentile). Patients report higher satisfaction when receiving their estimated waits, but the effect vary widely depending on the announcement. Drawing on Prospect theory, we hypothesize that the announced wait acts as a reference point against which patients compare the actual wait and that patients are lossaverse (end effect): Waits longer than announced will lower satisfaction more than waits shorter than announced will increase satisfaction. Overestimating waits will then improve satisfaction. At the same time, however, rising the announced wait will reduce satisfaction initially and while waiting (initial effect), and this effect will dominate over the end effect for high levels of overestimation. Accordingly, we hypothesize and show that patients are more satisfied when they are told an estimate based on the 70 or 90th-percentiles, with the benefit being the largest for the 70thpercentile announcement. Wait estimates based on the median have a null effect.Despite the benefits from wait announcements in settings where queues are unobservable, less is known about their effects in EDs, where queues are partially observable. With the Centers for Medicare & Medicaid Services tying reimbursements to patients’ ratings, our research suggests a cost-effective lever to improve patients’ satisfaction and hospitals’ financial performance: under-promising and over-delivering by providing moderately overestimated wait information

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: Tuck School of Business Working Paper ; No. 4135705
    Schlagworte: Wait; Patient Satisfaction; Emergency Department; ED; Prospect Theory; Healthcare
    Umfang: 1 Online-Ressource (33 p)
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    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 13, 2022 erstellt