A Study of Negative Customer Online Reviews and Managerial Responses on Social Media— Case Study of the Marriott Hotel Group in Beijing

W. Chen, S. Tabari


Putting customers at the center of business is the priority for the global hospitality and tourism companies. The importance of customer satisfaction and perceived higher quality of service dictate the branding images for global hotels within this era of social media. Managers need to understand the nature of online customers' reviews, which can help hotels to reflect on their operations and improve their service quality. The purpose of this paper is to explore the major reasons for hotel customers’ complaints on social media, and discuss how hotel managers respond to these. Researches have showed that negative online reviews have much greater impacts on customers decision making compared with positive reviews in hospitality industry. Limited negative reviews can influence hotels business performance in a long period of time, while many hospitality organisations ignore this challenging issue or not pay enough attention. This study chooses the Marriott Hotel Group in Beijing as a case study. A quantitative content study of customers' online reviews and hotel managers’ responses to these on TripAdvisor was conducted through a netnographic analysis process. The authors aim to analyze in greater detail the effects of hotel attributes on hotel business performance considering customers’ voice as expressed through their negative reviews. It was found that employees' lack of empathy is the main factor underlying customer complaints, followed by reliability, tangible factors, amenities and availability. The managerial responses were analyzed to identify the patterns and key words. The outcome provides a guidance tool for hotel management regarding which service elements to improve and how to respond appropriately to negative reviews.

Keywords: Customer satisfaction, e-WOM, Service failure, Social media, Negative Reviews, TripAdvisor, Hospitality management, qualitative content analysis

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