JOURNAL ARTICLE
The Bright Side of Lower Quality: Evidence from Restaurant Exploration.
Published In: Management Science (INFORMS), 2026, v. 72, n. 5. P. 3847 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Carrera, Clara; Martínez-de-Albéniz, Victor; Sosa, Manuel E. 3 of 3
Abstract
This article investigates how quality reference effects influence consumer behavior in the two-stage consumption process of hedonic goods, specifically focusing on restaurant choices and dining satisfaction. Using a novel longitudinal dataset of online restaurant reviews from Barcelona and Paris, the study develops and empirically tests a framework grounded in prospect theory that distinguishes between the ex ante choice stage and the ex post outcome stage. Findings reveal that consumers exhibit loss aversion when selecting restaurants, disproportionately avoiding options below their quality reference formed mainly from public information, but experience less sensitivity to quality losses in satisfaction due to downward expectation adjustments based on their own past experiences. This “bright side of lower quality” suggests that satisfaction penalties for lower-quality choices are mitigated, implying that alternating between high- and low-quality experiences may enhance overall utility, challenging conventional recommendation systems that assume static expectations. The study controls for alternative explanations such as satiation and user constraints, and its methodology and insights have potential applications beyond restaurants to other hedonic consumption contexts.
Additional Information
- Source:Management Science (INFORMS). 2026/05, Vol. 72, Issue 5, p3847
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2026
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.04141
- Accession Number:193596720
- Copyright Statement:Copyright of Management Science (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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