The major purpose of this research is to discover the influences of Active User of Social Media (AUSM), Innovativeness and Trust towards electronic word of mouth (eWOM) behaviour in Malaysia. Enormous past research on the electronic word-of-mouth, but only few have analysed the topic from Malaysian perspective. A quantitative method was adopted in this study and responses from 120 respondents in Malaysia that were analyzed using PLS-SEM. The result shows that there are significant relationship between active user of social media (AUSM), innovativeness and trust toward eWOM behaviour among Malaysian. Practically, the result indicate that electronic word of mouth behaviour via social media and mobile technology can be part of marketing strategy in promotion to influence buyer to increase sales of product of a company. This empirical study attempts to enrich the understanding of electronic word of mouth behaviour via social media and mobile technology in Malaysian context.
Anderson, J. and Gerbing, D. (1988). Structural Modeling in Practice: A Review and Recommended Two Step Approach. Psychological Bulletin, Vol. 103 No. 3, pp. 411
Avery, Chris, Paul Resnick, and Richard Zeckhauser (1999). The Market for Evaluations. American Economic Review, Vol. 89(3) 564-584.
Berezina, K., & Bilgihan, A. (2015). Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews. Journal of Hospitality Marketing & Management, 1-24
Cheung, M., Luo, C., Sia, C., & Chen, H. (2009). Credibility of electronic word-of-mouth: informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9-38. http://dx.doi.org/10.2753/JEC1086-4415130402
Chu, S.C., & Choi, M. S. (2011). Electronic Word-of-Mouth in Social Networking Sites: A Cross-Cultural Study of the United States and China. Journal of Global Marketing, 263-281.
Chu, S. C., & Kim, Y. (2011).Determinants of Consumer Engagement in Electronic Word-of-Mouth (e-WOM) in Social Networking Sites. International Journal of Advertising, 30(1) , 47-75.
Chua , A. Y., & Banerjee , S. (2013). Customer Knowledge Management Via Social Media: the Case of Starbucks. Journal of Knowledge Management, 237-249.
Dabholkar and Bagozzi (2002). An Attitudinal Model of Technology-based self-service: Moderating effect consumer traits and situational factors, Academy of Marketing Science Journal, 184
Dellarocas, C. (2003). The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms. Management Science, 49(10), 1407- 1424
Fergusson, R., 2008. Word of mouth and viral marketing: taking the temperature of the hottest trends in marketing. Journal of Consumer Marketing, 25 (3), pp.179-82
Fornell, C. and Larcker, D.F. (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research,Vol. 18 No. 1, pp. 39-50.
Goldsmith, R.E. & Hofacker, C.F. (1991). Measuring Consumer Innovativeness, Journal of the Academy of Marketing Science, 19, 209-221.
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis. 7th ed.,Prentice Hall, Englewood Cliffs
Hair, J.F., Sarstedt, M., Ringle, C.M. and Mena, J.A. (2012). An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research”, Journal of the Academy of Marketing Science, Vol. 40 No. 3, pp. 414-433
Heung, V.C. and Lam, T. (2003), “Customer complaint behaviour towards hotel restaurant services”, International Journal of Contemporary Hospitality Management , Vol. 15 No. 5, pp. 283-289
Hoffman, S., & Soyez, K. (2010).A Cognitive Model to Predict Domain-Specific Consumer Innovativeness. Journal of Business Research, 778-785
Hongwei, Y., Hui, L., & Liuning, Z., 2012. Predicting young Chinese consumers' mobile viral attitudes, intents and behavior. Asia Pacific Journal of Marketing and Logistics, 24 (1), pp.59 - 77
Hung, K. and Li, S.Y. (2007). The influence of eWOM on Virtual Consumer Communities: Social Capital, Consumer Learning, and Behavioral Outcomes”, Journal of Advertising Research, Vol. 47 No. 4, pp. 485-495
King, R.A., Racherla, P. and Bush, V.D. (2014), “What we know and don't know about online word-ofmouth: A review and synthesis of the literature”. Journal of Interactive Marketing, Vol. 28, No. 3, pp. 167-183. http://dx.doi.org/10.1016/j.intmar.2014.02.001
Kucukemiroglu, S., & Kara, A. (2015).Online Word-of-Mouth Communication on Social Networking Sites. pp.2-20.
Kwon T., (2011). MSblender: A Probabilistic Approach for Integrating Peptide Identifications from Multiple Database Search Engines. J Proteome Res. 10(7) 2949-2958.
Lee, W,.Tyrell, T., & Erdem, M., (2013). Exploring the Behavioral Aspects of Adopting Technology: Meeting Planners’ Use of Social Network Media and the Impact of Perceived Critical Mass. Journal of Hospitality and Tourism Technology, 6-22.
Li, Jie; Xue, Wenyi; Yang, F
In-Text Citation: (Marmaya, Balakrishnan, & Shuaib, 2018)
To Cite this Article: Marmaya, N. H., Balakrishnan, B. K. P. D., & Shuaib, A. S. M. (2018). Electronic Word-Of-Mouth (eWOM) Behavior in Malaysia: Successful Marketing Strategy. International Journal of Academic Research in Business and Social Sciences, 8(9), 1244–1255.
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