Social media review rating versus traditional customer satisfaction: Which one has more incremental predictive power in explaining hotel performance?
Abstract
Purpose This paper aims to examine the effects of traditional customer satisfaction (CS) relative magnitude and social media review ratings on hotel performance and to explore which online travel intermediaries’ review ratings serve as the most reliable and valid predictor for hotel performance. Design/methodology/approach In 2014, CS and hotel performance data were collected from the internal database of full-service hotels operated and managed by a large hotel chain in the USA. Each property’s social media review ratings data were hand-collected from major online travel intermediaries and social media websites. Findings The results of this study indicate that social media review rating is a more significant predictor than traditional CS for explaining hotel performance metrics. Additionally, the social media review rating of TripAdvisor is the best predictor for hotel performance out of the other intermediaries. Research limitations/implications This research contributes to the hospitality literature because it examines the incremental explanatory power of social media review rating and traditional CS on hotel performance. Among the leading online travel intermediaries, the findings show that TripAdvisor’s social media review rating has the most salient effect on hotel performance. Practical implications The result of this study provides useful practical implications for hotel marketers and revenue managers. This study assists hotel marketers and revenue managers in better allocating their budget for marketing and suggests ways for channel optimization. Originality/value The finding of this study will help revenue managers, marketing managers, and hotel owners make decisions regarding their marketing budget allocation to their social media marketing campaign and select the optimal online travel intermediaries as part of their channel management strategies.
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In sectors like hospitality, online ratings are more reliable predictors of a positive CX compared to conventional satisfaction metrics.
“In sectors like hospitality, online ratings prove to bemore reliable predictors of a positive CX compared to conventional satisfaction metrics (Kim and Park, 2017).”
From Reviews, trust, and customer experience in online marketplaces: the case of Mercado Libre Colombia (2024) · cites this paper as Kim and Park (2017)
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- Authors
- Kim, W. G., & Park, S. A.
- Affiliations
- Florida State University (US)
- ORCID
- Kim, Woo Gon
- Venue
- International Journal of Contemporary Hospitality Management
- Publisher
- Emerald
- Published
- 2017-02-13
- Volume
- 29
- Issue
- 2
- Pages
- 784-802
- ISSN
- 0959-6119 (ISSN-L)
- OpenAlex
- W2584531798
- Cited by
- 162
- References
- 70
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 17 · 2023: 14 · 2022: 22 · 2021: 19 · 2020: 34 · 2019: 22 · 2018: 16 · 2017: 4
- FWCI
- 53.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Contemporary Hospitality Management (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Social Sciences › Sociology and Political Science › Diverse Aspects of Tourism Research
- Keywords
- customer satisfaction, hotel performance, TripAdvisor, full-service hotels, hotel marketing, channel management, social media marketing, hotel revenue management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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