RbQUAL: integrating SERVQUAL and BERT to analyse service quality dimensions in hotel online reviews

Purpose Service quality (SQ) evaluation is essential for competitiveness in hospitality, and SERVQUAL is its most widely used assessment framework. This study integrates SERVQUAL with NLP techniques to extract and interpret customer perceptions from unstructured online reviews automatically. Design/methodology/approach RbQUAL (review-based quality) uses BERT-based supervised learning, optimised with AutoML, to automatically identify, classify and quantify the SERVQUAL dimensions (SQDs) in reviews and map them to specific hotel service areas. An iterative two-phase training protocol was applied to 701 TripAdvisor reviews (3,798 sentences) of two comparable luxury hotels in Seville (Spain) to refine the model progressively. Phase 1 used 690 expert-annotated fragments on extreme ratings reviews; Phase 2 was expanded to 1,303 fragments with intermediate ratings included for error correction. Findings RbQUAL classifies SQDs with 73% accuracy and maps service areas with 83% accuracy. The hotel comparison shows that “tangibles” and “reliability” are the most influential dimensions, with statistically significant differences in positive evaluations (χ2 = 26.94, df = 4, p < 0.001), but convergent service failure patterns. Research limitations/implications RbQUAL demonstrates that classical theoretical frameworks remain essential in the big data era. By converting publicly available reviews into a continuous SERVQUAL-based diagnostic, it replaces periodic surveys with automated monitoring and shifts quality management from reactive gap-correction to proactive improvement. Its open-source, architecture-agnostic design ensures accessibility for SMEs and transferability across sectors. Originality/value RbQUAL advances evidence-based SQ management by transforming unstructured customer discourse into actionable, theoretically consistent insights, bridging classical SQ theory and contemporary AI-driven analytics into a scalable, replicable model for hospitality research.