Search, Action, and Share: The Online Behaviour Relating to Mobile Instant Messaging App in the Tourism Context
Abstract
This study aims to assess the AISAS (attention – interest – search – action – share) model in a tourism setting. AISAS is considered as the most comprehensive model to illustrate one’s behaviour relating to modern media, particularly mobile instant messaging application. So far, the studies on AISAS in the tourism context are limited. Participants of this study were those who had a mobile instant messaging app, member of a chat group and had an experience holidaying after obtaining information from other group members and sharing their holidaying expertise in the same chat group. Data were collected by using an online instrument and attracted 408 participants consisting of 132 males and 276 females. This study found that the AISAS model can predict tourists’ behavioural relating to the use of mobile instant messengers as a medium for searching and sharing information. This study showed how tourists became a ‘cyborg’ when they attached to the internet of thing (IoT) in every stage of their behaviour relating to tourism activities.
References
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[2] Atzori, L., Iera, A., and Morabito, G. 2010. The internet of things: A survey. Computer networks, 54(15): 2787-2805.
[3] Ayuningtyas, R. 2017. LINE pockets 171 million monthly active users (in Bahasa Indonesia). Available at: http://tekno.liputan6.com/read/2997411/line-kantongi-171-juta-pengguna-aktif-bulanan
[4] Caputo, F., Scuotto, V., Carayannis, E., and Cillo, V. 2018. Intertwining the internet of things and consumers' behaviour science: Future promises for businesses. Technological forecasting and social change, 136: 277-284.
[5] Carvão, S. 2010. Embracing user generated content within destination management organizations to gain a competitive insight into visitors' profiles. Worldwide Hospitality and Tourism Themes, 2(4): 376-382
[6] Castelo, N., Fitz, N., Schmitt, B., and Sarvary, M. 2016. Cyborg consumers: When human enhancement technologies are dehumanizing. In P. Moreau & S. Puntoni (Eds.), NA - Advances in Consumer Research, 4: 42-47.
[7] Chan, L. S. 2016. Predicting the internet to use dating apps to look for romance and sex: Using the intergrative model of behavioural prediction.
[8] Chan, L. S. 2017. Who uses dating apps? Exploring the relationships among trust, sensation-seeking, smartphone use, and the intent to use dating apps based on the Integrative Model. Computers in Human Behavior, 72: 246-258.
[9] Chatterjee, S. 2019. Internet of things and social platforms: an empirical analysis from Indian consumer behavioural perspective. Behaviour & Information Technology, 1-17.
[10] Choe, Y., Kim, J., and Fesenmaier, D. R. 2017. Use of social media across the trip experience: An application of latent transition analysis. Journal of Travel & Tourism Marketing, 34(4): 431-443.
[11] Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3): 319-340. Available at: https://www.jstor.org/stable/249008?seq=1#page_scan_tab_contents
[12] Gao, L., and Bai, X. 2014. A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2): 211-231.
[13] Hair Jr., J. F., et al. 2006. Multivariate data analysis (6 ed.). New Jersey: Prentice-Hall, Inc.
[14] Heinonen, K. 2011. Consumer activity in social media: Managerial approaches to consumers' social media behavior. Journal of Consumer Behaviour, 10(6): 356-364.
[15] Hendriyani, J. J., et al. 2013. Online consumer behavior: Confirming the AISAS model on Twitter users. Paper presented at the the Proceedings of the International Conference on Social and Political Sciences, Tangerang Selatan, Indonesia.
[16] Hsu, C.-L., and Lin, J. C.-C. 2018. Exploring factors affecting the adoption of Internet of Things services. Journal of Computer Information Systems, 58(1): 49-57.
[17] Hu, L.-t., and Bentler, P. M. 1995. Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling. Concepts, issues, and applications (pp. 76-99). London: Sage.
[18] Hu, L.-t., and Bentler, P. M. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1): 1-55.
[19] Isada, F., and Isada, Y. 2013. An empirical study of the influence of trust and empathy in word-of-mouth communication on the Internet on consumer behaviour. Paper presented at the The 5th International Conference on Applied Economics, Business, and Development (AEBD '13) Chania, Greece. Available at: http://www.wseas.org/multimedia/books/2013/Chania/AEBDa.pdf
[20] Isada, F., Lin, H.-C., and Isada, Y. 2017. An empirical study regarding the satisfaction level of Taiwanese youth tourists to Japan.
[21] Kuang, J. Q. 2013. An application study of the AISAS model-based hotel e-marketing. Paper presented at the Applied Mechanics and Materials.
[22] Li, H. 2016. Research on the formation and development mechanism of brand loyalty based on social media. Revista Ibérica de Sistemas e Tecnologias de Informação(E5), 334.
[23] Li, Y., Hu, C., Huang, C., and Duan, L. 2017. The concept of smart tourism in the context of tourism information services. Tourism Management, 58: 293-300.
[24] Lin, C. H., Shih, H. Y., and Sher, P. J. 2007. Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24(7): 641-657.
[25] Merdiyantara, C. 2016. Influence of post tourist tourism post in Instagram on student's tourism interest: Survey on student of Fisip UPN Veneran Yogyakarta (in Bahasa Indonesia). (Bachelor), UPN Veteran Yogyakarta, Indonesia.
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[27] Nysveen, H., Pedersen, P. E., and Thorbjørnsen, H. 2005. Explaining intention to use mobile chat services: Moderating effects of gender. Journal of Consumer Marketing, 22(5): 247-256.
[28] Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., and Chang, Y. 2016. An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1): 34-47.
[29] Peng, X., Zhao, Y. C., and Zhu, Q. 2016. Investigating user switching intention for mobile instant messaging application: Taking WeChat as an example. Computers in Human Behavior, 64: 206-216.
[30] Peoples, C., et al. 2013. Performance evaluation of green data centre management supporting sustainable growth of the internet of things. Simulation Modelling Practice and Theory, 34: 221-242.
[31] Santosa, L. W., and Sidik, J. M. 2017. Most LINE users in Indonesia are teenagers (in Bahasa Indonesia). Retrieved from Antaranews.com website.
[32] Schermelleh-Engel, K., Moosbrugger, H., and Müller, H. 2003. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2): 23-74.
[33] Seock, Y. K., and Bailey, L. R. 2008. The influence of college students' shopping orientations and gender differences on online information searches and purchase behaviours. International journal of consumer studies, 32(2): 113-121.
[34] Solomon, M. R., and Lowrey, T. M. 2018. The quantified self: Self-regulation in cyborg consumers. In M. R. Solomon & T. M. Lowrey (Eds.), The Routledge companion to consumer behavior. New York: Routledge.
[35] Sugiyama, K., and Andree, T. 2010. The Dentsu way: Secrets of cross switch marketing from the world’s most innovative advertising agency: McGraw Hill Professional.
[36] Sumter, S. R., Vandenbosch, L., and Ligtenberg, L. 2017. Love me Tinder: Untangling emerging adults’ motivations for using the dating application Tinder. Telematics and Informatics, 34(1): 67-78.
[37] Tabachnick, B. G., and Fidell, L. S. 2007. Using multivariate statistics (5 ed.). Boston Pearson/Allyn & Bacon.
[38] Uckelmann, D., Harrison, M., and Michahelles, F. 2011. An architectural approach towards the future internet of things Architecting the internet of things (pp. 1-24): Springer.
[39] Wei, P.-S., and Lu, H.-P. 2013. An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Computers in Human Behavior, 29(1): 193-201.
[40] Wiyanto, T., Luck, E. M., and Mathews, S. W. 2011. From cyber to cyborg: The influence of motivation and personality traits on the merging of consumer and technology. Marketing in the Age of Consumerism: Jekyll or Hyde? Available at: https://eprints.qut.edu.au/48683/
[41] Xiang, Z., Magnini, V. P., and Fesenmaier, D. R. 2015. Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of Retailing and Consumer services, 22: 244-249.
[42] Xu, C., Hao, Q., and Han, G. 2017. Research on the marketing strategy of the new media age based on AISAS model: A case study of micro channel marketing. Paper presented at the Proceedings of the Fourth International Forum on Decision Sciences.
[43] Xu, Y., and Du, H. 2011. Empirical study on the evaluation of the advertising effectiveness. Paper presented at the 2011 International Conference on Electrical and Control Engineering (ICECE), Yichang, China.
[44] Internet Worlds Stats. 2018. Usage and population statistics. Available at: http://www.internetworldstats.com/top20.htm
Published
2020-06-30
How to Cite
SUHUD, Usep; ALLAN, Mamoon.
Search, Action, and Share: The Online Behaviour Relating to Mobile Instant Messaging App in the Tourism Context.
Journal of Environmental Management and Tourism, [S.l.], v. 11, n. 4, p. 903-912, june 2020.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/5216>. Date accessed: 27 dec. 2024.
doi: https://doi.org/10.14505//jemt.11.4(44).14.
Section
Article
Copyright© 2024 The Author(s). Published by ASERS Publishing 2024. This is an open access article distributed under the terms of CC-BY 4.0 license.