Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews

Abstract

Online customer reviews have become a significant information source for scholars and practitioners to understand customer experience and its association with their satisfaction to maintain the sustainable development of relative industries. Thus, this study attempted to find the underlying dimensionality in online customer reviews reflecting customers experience in the Hong Kong Disneyland hotel and identified its relationship with customer satisfaction. Semantic network analysis by Netdraw and factor analysis and linear regression analysis by SPSS 26.0 (IBM, New York, NY, USA) were applied for data analysis. As a result, 70 keywords with high frequency were extracted, and their connection to each other was calculated based on their centralities. Consequently, seven factors were explored by exploratory factor analysis, and moreover, three factors, “Family Empathy”, “Value”, and “Food Quality”, were testified to be negatively related to customer satisfaction. The findings of this study, to a great extent, could be utilized as a research scheme for future research to investigate theme hotels with big data analytics of online customer reviews. More importantly, some new insights and practical implications for the future research and industry development were provided and discussed as well.

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