Abstract
As people increasingly make hotel booking decisions relying on online reviews, how to effectively improve customer ratings has become a major point for hotel managers. Online reviews serve as a promising data source to enhance service attributes in order to improve online bookings. This paper employs online customer ratings and textual reviews to explore the bidirectional performance (good performance in positive reviews and poor performance in negative reviews) of hotel attributes in terms of four hotel star ratings. Sentiment analysis and a combination of the Kano model and importance-performance analysis (IPA) are applied. Feature extraction and sentiment analysis techniques are used to analyze the bidirectional performance of hotel attributes in terms of four hotel star ratings from 1,090,341 online reviews of hotels in London collected from TripAdvisor.com (accessed on 4 January 2022). In particular, a new sentiment lexicon for hospitality domain is built from numerous online reviews using the PolarityRank algorithm to convert textual reviews into sentiment scores. The Kano-IPA model is applied to explain customers’ rating behaviors and prioritize attributes for improvement. The results provide determinants of high/low customer ratings to different star hotels and suggest that hotel attributes contributing to high/low customer ratings vary across hotel star ratings. In addition, this paper analyzed the Kano categories and priority rankings of six hotel attributes for each star rating of hotels to formulate improvement strategies. Theoretical and practical implications of these results are discussed in the end.
Highlights
Unlike using recommendations of relatives and friends in the past, people increasingly make hotel booking decisions relying on online reviews on various online travel platforms in the modern era
The results indicate that the bidirectional performance of the same sub-attributes may affect customer satisfaction differently
The effect of good/poor performance concerning location, value, service on high/low customer ratings varies across hotel star ratings
Summary
Unlike using recommendations of relatives and friends in the past, people increasingly make hotel booking decisions relying on online reviews on various online travel platforms in the modern era. Hotel online reviews are posted by numerous customers according to their experiences in hotels, which are perceived as more objective, trustworthy and helpful than information provided by hotels [1,2]. Online ratings signal customer satisfaction or dissatisfaction with hotels. According to bounded rationality model, customers are unable to elaborate and extract useful information from numerous and heterogeneous data, driving them to prefer and rely more on ratings than on textual reviews [3]. Exploring what contributes to the difference in online ratings between satisfied and dissatisfied customers is important for hotels. For the purpose of being competitive sustainably in the hospitality industry, it is critical for hotels to understand the determinants of customer satisfaction and dissatisfaction which are proxied by online ratings [6,7]
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