JOURNAL ARTICLE
Queue Configurations and Operational Performance: An Interplay Between Customer Ownership and Queue Length Awareness.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 6. P. 2284 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Song, Hummy; Armony, Mor; Roels, Guillaume 3 of 3
Abstract
This article investigates the operational performance differences between dedicated and pooled queue configurations in business-to-consumer (B2C) discretionary service settings, specifically within healthcare delivery. Through two preregistered online experiments involving fourth-year medical students and Amazon Mechanical Turk workers acting as physicians processing patient cases, the study finds that dedicated queues yield faster processing times without compromising quality compared to pooled queues. This performance advantage is partially mediated by increased queue length awareness—servers' more accurate perception of their workload—which motivates faster work, and partially suppressed by heightened customer ownership of those waiting in queue, which can distract servers and slow processing. Additionally, the benefits in processing speed and queue length awareness persist even after switching from a dedicated to a pooled queue configuration, suggesting practical implications for managers considering queue design changes in discretionary service environments.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/11, Vol. 26, Issue 6, p2284
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:1523-4614
- DOI:10.1287/msom.2023.0202
- Accession Number:180921142
- Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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