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Publication Detailed Description
Journal Title
Tourism Management Perspectives
Year (definitive publication)
2017
Language
English
Country
United States of America
More Information
Web of Science®
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Abstract
In the cluttered environment of online reviews, consumers frequently have to choose the most trustworthy reviewers to help them in their purchasing decision. Such reviewers are influential in their community and co-create value among their peers. The current research note studies the antecedents of fandom, particularly if contents of the message written by the reviewers predict the number of fans they might have in the future. 27,097 tips written by 16,334 users of Yelp are structured using text mining and a support vector machine algorithm is used to study the accuracy of such relation. Results show that tips which may help consumers to avoid the service and tips that highlight the positive elements of the service are the most relevant in predicting the number of fans. Findings may help managers to understand which type of messages may increase the reviewer's number of fans, thus increasing their influence in the network.
Acknowledgements
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Keywords
eWOM,Online reviews,Fandom,Text mining,Support vector machine
Fields of Science and Technology Classification
- Economics and Business - Social Sciences
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| UID/GES/00315/2013 | Fundação para a Ciência e a Tecnologia |
| UID/MULTI/0446/2013 | Fundação para a Ciência e a Tecnologia |
Contributions to the Sustainable Development Goals of the United Nations
With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific publications with the Sustainable Development Goals is now available in Ciência-IUL. These are the Sustainable Development Goals identified by the author(s) for this publication. For more detailed information on the Sustainable Development Goals, click here.
