An AI-enhanced bibliometric analysis of customer feedback and hotel performance in hospitality

Abstract

In the digital era, customer feedback has evolved into a strategic asset influencing hotel performance, competitiveness, and investment decisions. This study reviews the relationship between online reviews and key performance indicators (KPIs) in the hospitality sector and proposes an AI-enhanced literature review framework. By integrating bibliometric analysis, Natural Language Processing (NLP), and Large Language Models (LLMs), the approach enables scalable, context-aware synthesis of research on customer perceptions and their financial implications. The findings show that customer feedback significantly influences hotel performance, particularly affecting pricing strategies, occupancy rates, and revenue outcomes. However, the literature remains heavily reliant on rating-based proxies and makes limited use of real operational data, thereby constraining the accuracy of financial assessments. Additionally, gaps persist in data integration, geographic representativeness, and the translation of feedback into actionable investment decisions. From a practical perspective, the results highlight the need for more advanced, data-driven frameworks that integrate textual feedback with financial metrics. The proposed methodology offers a replicable approach to support more accurate and strategically relevant analyses in hospitality management.