Description
This paper is intended to analyze the interrelationship between the textual comment and the itemized rating in consumers' online feedback, and to study differentiations among diversified segments of consumers who leave the feedback. The data were collected from both an international top tourism service website which operates hotel booking services internationally and offers online hotel reputation feedback services, and its China host website. Based on the coded comments and itemized ratings from the dataset, our study shows that different customer segments demonstrate varied interrelationship behavioral patterns, and factors like satisfaction and contextual backgrounds will matter. The findings from this research may help hotels to develop varied marketing strategies for different segment of customers, and help online reputation sites to improve their services by distinguishing dissimilar behavioral patterns in different customer segments.
Recommended Citation
Li, Hongxiu; Lin, Zhangxi; and Zhang, Xianfeng, "A Comparative Study on Hotel Consumers’ Online Feedback Behaviors" (2015). AMCIS 2015 Proceedings. 26.
https://aisel.aisnet.org/amcis2015/e-Biz/GeneralPresentations/26
A Comparative Study on Hotel Consumers’ Online Feedback Behaviors
This paper is intended to analyze the interrelationship between the textual comment and the itemized rating in consumers' online feedback, and to study differentiations among diversified segments of consumers who leave the feedback. The data were collected from both an international top tourism service website which operates hotel booking services internationally and offers online hotel reputation feedback services, and its China host website. Based on the coded comments and itemized ratings from the dataset, our study shows that different customer segments demonstrate varied interrelationship behavioral patterns, and factors like satisfaction and contextual backgrounds will matter. The findings from this research may help hotels to develop varied marketing strategies for different segment of customers, and help online reputation sites to improve their services by distinguishing dissimilar behavioral patterns in different customer segments.