Determinants of Electronic Word-of-Mouth among Line Users in Thailand
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Published
Sep 25, 2020
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Abstract
Electronic Word-of-Mouth (eWOM) plays an important role as a new marketing tool in many countries nowadays. eWOM has been used through social media such as Facebook, Instagram, etc. eWOM in social media allows a continuous connection to a broad audience. Line application is the latest social media introduced in 2011. This study thus explored factors impacting on the eWOM intention via Line application which is a famous social media used, especially in Thailand. The four factors (user preference, user similarity, user interaction and user concern for others) drawn from Consumer Behaviors theory and two factors (ease of use, and usefulness) drawn from Technology Acceptance Model (TAM) theory were used in this study. The samples of 196 respondents were collected by using convenience sampling method via Google form survey. Multiple regression with stepwise method was used to analyze the data. The results showed that user preference, Line ease of use and Line usefulness have positive relationship with eWOM, while user similarity, user interaction and user concern have no relationship with eWOM. For theoretical contribution, the study expanded the application of TAM to explore the changing consumer behaviors and eWOM. For managerial implication, this study could help marketer develop marketing strategies that allow businesses to build on communication platform.
Keywords: Electronic Word-of-Mouth, Consumer Behaviors Theory, Technology Acceptance Theory, Line Application
References
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Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Mis Quarterly, 13(3), 319-340. DOI: 10.2307/249008
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Engel, J. B. (1993). Consumer Behavior (8th ed.). Fort Worth: Dryden Press.
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Gatignon, H., & Robertson, T. S. (1985). A Propositional Inventory for New Diffusion Research. Journal of Consumer Research, 11(4), 849–867. DOI: 10.1086/209021
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Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51–90. DOI: 10.2307/30036519
Godes, D., & Mayzlin, D. (2004). Using Online Conversations to Study Word-of-Mouth Communication. Marketing Science, 23(4), 545-560. DOI: 10.1287/mksc.1040.0071
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis. Englewood Cliffs, NJ: Prentice Hall.
Halstead, D. (2002). Negative Word of Mouth: Substitute for or Supplement to Consumer Complaints? Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 15, 1-12.
Hambrick, D. C. (1994). What if the Academy Actually Mattered? Academy of Management Review, 19(1), 11-16. DOI: 10.5465/amr.1994.9410122006
Han, S. M. (2008). Motivations for Providing and Seeking eWOM: A Cross Cultural Comparison of U.S. and Korean College Students. (Unpublished doctoral dissertation). Department of Advertising, Michigan State University, East Lansing.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet? Journal of Interactive Marketing, 18(1), 38-52. DOI: 10.1002/dir.10073
Ho, J. Y. C., & Dempsey, M. (2010). Viral Marketing: Motivations to Forward Online Content. Journal of Business Research, 63(9-10), 1000-1006. DOI: 10.1016/j.jbusres.2008.08.010
Ho, K. (2019, April 30) Thais Spend Over a Quarter of their Day on Social Media. Retrieved from https://th.yougov.com/en-th/news/2019/04/30/thais-spend-over-quarter-their-day-social-media/
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Hogg, M. A., Cooper-Shaw, L., & Holzworth, D. W. (1993). Group Prototypicality and Depersonalized Attraction in Small Interactive Groups. Personality and Social Psychology Bulletin, 19(4), 452-565. DOI: 10.1177/0146167293194010
Horton, R. P., Buck, T., Waterson, P. E., & Clegg, C. W. (2001). Explaining Intranet Use with the Technology Acceptance Model. Journal of Information Technology, 16(4), 237-249. DOI: 10.1080/02683960110102407
Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of Management Information Systems, 16(2), 91-112. DOI: 10.1080/07421222.1999.11518247
Hutter, K., Hautz, J., Dennhardt, S., & Füller, J. (2013). The Impact of User Interactions in Social Media on Brand Awareness and Purchase Intention: The Case of MINI on Facebook. Journal of Product & Brand Management, 22(5/6), 342-351. DOI: 10.1108/JPBM-05-2013-0299
Ibarra, H. (1992). Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm. Administrative Science Quarterly, 37(3), 422–447. DOI: 10.2307/2393451
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How to Cite
VIJITKORNTHONG, Yossaya; ARYUPONG, Mayuree; KITSEREE, Suwanna.
Determinants of Electronic Word-of-Mouth among Line Users in Thailand.
Journal of Community Development Research (Humanities and Social Sciences), [S.l.], v. 13, n. 4, p. 59-72, sep. 2020.
ISSN 2985-0231.
Available at: <https://www.journal.nu.ac.th/JCDR/article/view/Vol-13-No-4-2020-59-72>. Date accessed: 25 apr. 2024.
doi: https://doi.org/10.14456/jcdr-hs.2020.36.