What is it about?
Online hotel reviews contain valuable feedback, but analyzing thousands of comments manually is impossible. This research introduces RbQUAL, an innovative tool that combines a traditional service quality framework (SERVQUAL) with an advanced Artificial Intelligence model (BERT). RbQUAL automatically scans and categorizes guest reviews into specific quality dimensions like cleanliness, staff behavior, or room comfort. This allows hotels to quickly understand guest satisfaction and fix issues efficiently.
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Why is it important?
Traditional methods for measuring hospitality quality rely on long, slow surveys that guests rarely want to fill out. Meanwhile, text-mining tools often fail to capture the true meaning and context of human language. By training the BERT AI model specifically on hotel reviews, RbQUAL bridges the gap between academic theory and real-time business management. It transforms thousands of unorganized online comments into clear, actionable data. This helps hotel managers make fast, data-driven decisions to improve guest experiences, protect their online reputation, and boost revenue in a highly competitive market.
Perspectives
As researchers, we wanted to solve a real-world problem: the overwhelming amount of unstructured data that hotels receive daily. Developing RbQUAL showed us that when traditional management frameworks meet modern Artificial Intelligence, we don't lose the human touch, we actually enhance it. This methodology opens up a new era for hospitality analytics, where machine learning can understand the nuances of guest sentiment more accurately than ever before. We believe this is just the beginning of AI's role in hyper-personalizing the tourism experience.
Vladimir Perdomo-Verdecia
Universidad de Sevilla
Read the Original
This page is a summary of: RbQUAL: integrating SERVQUAL and BERT to analyse service quality dimensions in hotel online reviews, Journal of Hospitality and Tourism Insights, June 2026, Emerald,
DOI: 10.1108/jhti-06-2025-0654.
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