Do Electronic Word-of-Mouth and Elaboration Likelihood Model Influence Hotel Booking?

ABSTRACT

The emergences of Web 2.0 and cloud computing have contributed greatly to the prevalence of electronic Word-of-Mouth (eWoM). Unlike most of the existing studies which have used linear models, nonlinear relationships were discovered in hotel booking intention. So far, the effects of elaboration-likelihood model (ELM) and demographics have been largely overlooked, though studies have shown that ELM may explain consumers’ perception, behavior, and IS acceptance. Data were gathered from 497 patrons of 10 hotels in Golden Triangle, Kuala Lumpur, Malaysia. Using artificial neural network (ANN), we found that user involvement, positive eWoM, user expertise, perceived credibility, education, negative eWoM, and income are among the important predictors explaining 81% of variance in booking intention. The theoretical implications may further advance eWoM and ELM literatures, while the managerial implications may provide novel understandings to hotel operators, advertisers, and relevant hospitality policy makers in formulating effective decision making.

KEYWORDS:

• Artificial neural networks
• booking intention
• demographic
• elaboration-likelihood model
• eWoM

Acknowledgments

The authors would like to express their heartfelt gratitude to the junior researchers for their assistance in gathering the data required for this study. We would also like to thank the managers of 10 selected hotels in the Golden Triangle for their cooperation in allowing the survey to be administered to hotel patrons. Finally, our heartfelt appreciations to the Editor-in-Chief of Journal of Computer Information Systems (Prof. Dr. Alex Koohang) and the two anonymous reviewers for their thoughtful comments and insightful suggestions in helping us to further improve the quality of our research paper.