As online review platforms such as TripAdvisor increasingly share visitor experiences and shape institutional reputations, the influence of emotion in complaint reviews on guiding service recovery strategies remains inadequately understood. This study aims to explore the emotional dynamics between negative tourist reviews and museum responses.
Drawing on emotion regulation theory, we analyzed 615 negative reviews and their corresponding institutional responses from 32 internationally recognized museums using theory-informed content analysis and emotion mining based on machine learning.
Complaint emotions are categorized in terms of the intensity of feelings (e.g. fear, confusion, disappointment), and museum responses are grouped according to Gross’s emotion regulation strategy framework. The findings indicate that the emotions expressed in complaints are closely tied to the emotion regulation strategies used by museums in their responses (e.g. museums used cognitive change in response to feelings of sadness).
This study contributes to the literature on cultural tourism by illustrating not only the potential to discover fine-grained emotions in visitors’ complaints and group them into emotion profiles in a continuum of emotional negativity but also their relationship with emotion regulation strategies expressed in the museum’s responses. The results identify both the patterns of emotional fit between complaints and responses and the communicative effort of these strategies, thereby demonstrating the application of emotion regulation theory to explain digital service recovery in the context of cultural tourism.
