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How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
Service failure is inevitable. Although empirical studies on the outcomes and processes of service failures have been conducted in the hotel industry, the findings need more exploration to understand how different segments perceive service failures and the associated emotions differently. This approach enables hotel managers to develop more effective strategies to prevent service failures and implement more specific service-recovery actions. For analysis, we obtained a nine-year (2010–2018) longitudinal dataset containing 1224 valid respondents with 73,622 words of textual content from a property affiliated with an international hotel brand in Canada. A series of text-mining and natural language processing (NLP) analyses, including frequency analysis and word cloud, sentiment analysis, word correlation, and TF–IDF analysis, were conducted to explore the information hidden in the massive amount of unstructured text data. The results revealed the similarities and differences between groups (i.e., men vs. women and leisure vs. business) in reporting service failures. We also carefully examined different meanings of words that emerged from the text-mining results to ensure a more comprehensive understanding of the guest experience.
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
Service failure is inevitable. Although empirical studies on the outcomes and processes of service failures have been conducted in the hotel industry, the findings need more exploration to understand how different segments perceive service failures and the associated emotions differently. This approach enables hotel managers to develop more effective strategies to prevent service failures and implement more specific service-recovery actions. For analysis, we obtained a nine-year (2010–2018) longitudinal dataset containing 1224 valid respondents with 73,622 words of textual content from a property affiliated with an international hotel brand in Canada. A series of text-mining and natural language processing (NLP) analyses, including frequency analysis and word cloud, sentiment analysis, word correlation, and TF–IDF analysis, were conducted to explore the information hidden in the massive amount of unstructured text data. The results revealed the similarities and differences between groups (i.e., men vs. women and leisure vs. business) in reporting service failures. We also carefully examined different meanings of words that emerged from the text-mining results to ensure a more comprehensive understanding of the guest experience.
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
Shuyue Huang (author) / Lena Jingen Liang (author) / Hwansuk Chris Choi (author)
2022
Article (Journal)
Electronic Resource
Unknown
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