The present study aims to question the trend of relying heavily on online reviews as the primary indicator of customer satisfaction in the hotel industry, exposes the disadvantages of the suggested strategy and suggests a more credible and multifaceted approach as an alternative.
The comprehensive re-evaluation of the existing literature concerning online reviews, behavioural economics and service quality is conducted and through the use of a critical synthesis approach, the current situation is disrupted. To a greater extent, the paper deploys the conceptual model of the Critical Reflection Paper (CRP) to not only perceive the dominant school of thought from a critical perspective but also to offer a different perspective.
The study identifies three major blind spots in online reviews, namely, the issues of quantifying the qualitative nuances, the influence of the platform algorithms on the perceptions of the guests and the consideration of silent visitors. It contends that using these measures in totality will result in the management making bad decisions in matters concerning resource allocation and wrong ideals about quality service.
The hotel industry is believed to view the customer reviews as the primary variable to gauge its performance and to conduct business reviews and to this end, it is suggested that the hotel managers are advised to depend more on their in-house feedback developed based on diverse sources, diminish the influence of third-party review ratings in its internal evaluations and introduce real-time information capture as an investment to secure and maintain its real service excellence.
While Holistic Experience Analytics (HEA) integrates existing analytical components, its originality lies in three novel logics: (a) a temporal dissonance weighting rule that prioritises behavioural signals over review scores when they conflict, (b) a governance protocol for auditing third-party platform biases and (c) a prospective satisfaction assumption that enables real-time, pre-review intervention moving beyond mere data aggregation to a fundamentally different measurement logic.
