‘Apparent’ and actual hotel scores under Booking.com new reviewing system

Leoni, Veronica; Boto‐García, David

doi:10.1016/j.ijhm.2023.103493

Cited by 9 publications

(2 citation statements)

References 73 publications

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“…In the new system, reviewers are asked to provide a numerical rating (from 1 to 10) of the overall experience. Unlike the previous system, which requested reviewers to only evaluate six specific hotel items through smileys (while the overall score was generated as a simple average), the current individual score is explicitly rated by the customer and could better reflect the influence of exogenous factors that are beyond the control of the hotel (for more details on the differences between the old and the new systems, we refer to Leoni and Boto-García (2023)). Moreover, we stopped the data collection in February 2020 to avoid any confounding effects generated by the COVID-19 pandemic and subsequent lockdown measures.…”

Section: Datamentioning

confidence: 99%

“…To this end, we analyze more than 300,000 online reviews for accommodation providers listed on Booking.com, the leading search engine for lodging services, in two popular Italian destinations (Milan and Venice), between September 2019 and February 2020. The period under investigation is chosen for consistency reasons, allowing us to analyze only ratings before the COVID-19 outbreak and after the reform of the reviewing system introduced by the platform in September 2019 (Leoni and Boto-García, 2023). With regard to weather surprises, we look at the potential mismatch between the weather forecasts posted days before traveling and the realised weather.…”

Section: Introductionmentioning

confidence: 99%

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And Suddenly, the Rain! How Surprises Shape Experienced Utility

2023

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

And Suddenly, the Rain! How Surprises Shape Experienced Utility

2023

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Exploring an incongruence frame for online reviews

Sugathan,

Sudhir,

Ramachandran

2024

J of Consumer Behaviour

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Given the growth in the internet and e‐retailing, consumers are extremely reliant on online user‐generated reviews (“reviews”) for decision‐making. Reviews often combine informational cues such as numeric (i.e., star) ratings and qualitative text, and these may not always be in alignment. To understand conflicting informational cues, this research conceptualizes and tests an incongruence frame that captures the inconsistency between a review's textual and numeric message cue components. Incongruence occurs when the valence of the review text is not in alignment with the product star rating given by the reviewer. To further qualify the findings, the paper introduces two types of incongruences: Type A (Type B) is categorized as involving low (high) star ratings alongside a positive (negative) review text. The research findings shed light on an underexplored dimension of review processing based on inconsistency between the textual and numeric components of a single review. Using primary and secondary data across four studies, the incongruence effect is shown to undermine review usage. Incongruence is found to influence both review diagnosticity and review authenticity. The initial heuristics of review evaluation generate differential effects between Type A and Type B. Incongruence in the review is also shown to influence product purchases. The incongruence frame, therefore, helps reconcile some of the inconsistencies in the extant literature and offers fruitful avenues of future research for both academics and practitioners.

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