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

• Published in: The Journal of computer information systems Vol. 59; no. 2; pp. 146 - 160
• Main Authors: Leong, Lai-Ying, Hew, Teck-Soon, Ooi, Keng-Boon, Lin, Binshan
• Format: Journal Article
• Language: English
• Published: Stillwater Taylor & Francis 04.03.2019; Taylor & Francis Ltd
• Subjects: Artificial neural networks; booking intention; Cloud computing; Decision making; demographic; Demographics; elaboration-likelihood model; eWoM; Hotels; Hotels & motels; Neural networks; Web 2.0; Word of mouth advertising
• ISSN: 0887-4417; 2380-2057
• DOI: 10.1080/08874417.2017.1320953

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.