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Understanding factors influencing traveler’s adoption of travel influencer advertising: an Information Adoption Model approach

Abstract

In the service sector, such as tourism and hospitality, a traveler often tries to find information about their destination digitally, comparing it to alternatives available to have the best option. This demand businesses that are in the tourism and hospitality sector to advertise their destination more creatively and informatively, such as using a travel influencer. The present research was conducted to explore how the public adopts information advertised by a social media influencer that promotes travel or leisure places, better known as a travel influencer. The Information Adoption Model (IAM) was used to explore the factors that could affect people’s perception of Information Usefulness (IU), which then affects Information Adoption (IA). Several hypotheses that were built from the IAM theoretical framework were tested using Partial Least Square Structural Equation Modelling (PLS-SEM) with 150 people as respondents; four out of the seven hypotheses were accepted. From the accepted hypotheses, it was revealed that more dimensions in the Source Credibility construct influence Information Usefulness compared to the dimensions from the Argument Quality constructs.

Keyword : information, Information Adoption Model, travel influencer, online reviews, tourism and hospitality, structural equation modelling, consumer behavior

How to Cite
Nadlifatin, R., Persada, S. F., Munthe, J. H., Ardiansyahmiraja, B., Redi, A. A. N. P., Prasetyo, Y. T., & Belgiawan, P. F. (2022). Understanding factors influencing traveler’s adoption of travel influencer advertising: an Information Adoption Model approach. Business: Theory and Practice, 23(1), 131–140. https://doi.org/10.3846/btp.2022.13149
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References

Apulu, I., & Latham, A. (2011). An evaluation of the impact of information and communication technologies: Two case study examples. International Business Research, 4(3), 3. https://doi.org/10.5539/ibr.v4n3p3

Ayeh, J. K. (2015). Travellers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48, 173–180. https://doi.org/10.1016/j.chb.2014.12.049

Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825. https://doi.org/10.2307/25148755

Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet – the state of eTourism research. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005

Cheung, R. (2014). The influence of electronic word-of-mouth on information adoption in online customer communities. Global Economic Review, 43(1), 42–57. https://doi.org/10.1080/1226508X.2014.884048

Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229–247. https://doi.org/10.1108/10662240810883290

Demoulin, N. T., & Coussement, K. (2020). Acceptance of text-mining systems: The signaling role of information quality. Information & Management, 57(1), 103120. https://doi.org/10.1016/j.im.2018.10.006

Esfandiar, K., Dowling, R., Pearce, J., & Goh, E. (2020). Personal norms and the adoption of pro-environmental binning behaviour in national parks: An integrated structural model approach. Journal of Sustainable Tourism, 28(1), 10–32. https://doi.org/10.1080/09669582.2019.1663203

Evans, C., & Erkan, I. (2015). The influence of electronic word of mouth in social media on consumers’ purchase intentions. In Managing intellectual capital and innovation for sustainable and inclusive society: Managing intellectual capital and innovation; Proceedings of the MakeLearn and TIIM Joint International Conference 2015 (pp. 2007–2007). ToKnowPress.

Filieri, R., & McLeay, F. (2014). E-WOM and accommodation: An analysis of the factors that influence travelers’ adoption of information from online reviews. Journal of Travel Research, 53(1), 44–57. https://doi.org/10.1177/0047287513481274

Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956–970. https://doi.org/10.1016/j.im.2018.04.010

Gross, J., & Wangenheim, F. V. (2018). The Big Four of influencer marketing. A typology of influencers. Marketing Review St. Gallen, 2, 30–38.

Guleria, S. (2018). An exploratory study of travel related decisions of foreign and domestic tourists visiting Himachal Pradesh. International Journal of Academic Research & Development (IJAR&D).

Guttmann, A. (2018). Global Instagram influencer marketing spending 2013–2020. https://www.statista.com/statistics/950920/global-instagram-influencer-marketing-spending/#statisticContainer

Hair, J., Black, W., Babin, B., Anderson, R., & Tathan, R. (2006). Multivariate data analysis. India. Pearson Education.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Ho, C.-I., & Lee, P.-C. (2015). Are blogs still effective to maintain customer relationships? An empirical study on the travel industry. Journal of Hospitality and Tourism Technology, 6(1), 5–25. https://doi.org/10.1108/JHTT-01-2015-0005

Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2019). Google trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden. Information Technology & Tourism, 21(1), 45–62. https://doi.org/10.1007/s40558-018-0129-4

Hussain, S., Ahmed, W., Jafar, R. M. S., Rabnawaz, A., & Jianzhou, Y. (2017). eWOM source credibility, perceived risk and food product customer’s information adoption. Computers in Human Behavior, 66, 96–102. https://doi.org/10.1016/j.chb.2016.09.034

Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 101736. https://doi.org/10.1016/j.jretconser.2019.01.005

Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45–59. https://doi.org/10.1002/dir.20113

Kim, W. G., Lim, H., & Brymer, R. A. (2015). The effectiveness of managing social media on hotel performance. International Journal of Hospitality Management, 44, 165–171. https://doi.org/10.1016/j.ijhm.2014.10.014

Kumar, P. K. (2006). Information System–Decision Making. Indian MBA. https://www.indianmba.com/Facul-ty_Column/FC307/fc307.html

Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452

Manthiou, A., & Schrier, T. (2014). A comparison of traditional vs electronic word of mouth in the Greek hotel market: An exploratory study. Journal of Tourism Research, 8, 125–134.

Matenga, C. T. (2019). The rise of virtual athletes: The influence of uses & gratification and para-social interaction on consumers’ attitudes towards high-involvement products endorsed by micro-celebrities [Doctoral dissertation, The University of Waikato].

Matsuno, K., Mentzer, J. T., & Rentz, J. O. (2005). A conceptual and empirical comparison of three market orientation scales. Journal of Business Research, 58(1), 1–8. https://doi.org/10.1016/S0148-2963(03)00075-4

Nardi, B. A., & Whittaker, S. (2002). The place of face-to-face communication in distributed work. In P. Hinds & S. Kiesler (Eds.), Distributed work (pp. 83–110). Boston Review.

Neeleman, A., & Van de Koot, J. (2016). Word order and information structure. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199642670.013.20

Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: An empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199–235. https://doi.org/10.1080/07421222.2005.11045823

Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135–146. https://doi.org/10.1086/208954

Reinstein, D. A., & Snyder, C. M. (2005). The influence of expert reviews on consumer demand for experience goods: A case study of movie critics. The Journal of Industrial Economics, 53(1), 27–51. https://doi.org/10.1111/j.0022-1821.2005.00244.x

Rios, S. A., Aguilera, F., Nuñez-Gonzalez, J. D., & Graña, M. (2019). Semantically enhanced network analysis for influencer identification in online social networks. Neurocomputing, 326, 71–81. https://doi.org/10.1016/j.neucom.2017.01.123

Sheth, J. N., & Uslay, C. (2007). Implications of the revised definition of marketing: from exchange to value creation. Journal of Public Policy & Marketing, 26(2), 302–307. https://doi.org/10.1509/jppm.26.2.302

Shu, M., & Scott, N. (2014). Influence of social media on Chinese students’ choice of an overseas study destination: An information adoption model perspective. Journal of Travel & Tourism Marketing, 31(2), 286–302. https://doi.org/10.1080/10548408.2014.873318

Sirithanaphonchai, J. (2017). Identifying consumers’ information adoption criteria on various online consumer review platforms: a case of Thai hospitality factor. Brunel University, London.

Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65. https://doi.org/10.1287/isre.14.1.47.14767

Wang, C. C., Lo, S. K., & Fang, W. (2008). Extending the technology acceptance model to mobile telecommunication innovation: The existence of network externalities. Journal of Consumer Behaviour: An International Research Review, 7(2), 101–110. https://doi.org/10.1002/cb.240

Wang, Y. (2016). Information adoption model, a review of the literature. Journal of Economics, Business and Management, 4(11), 618–622. https://doi.org/10.18178/joebm.2016.4.11.462

Willemsen, L. M., Neijens, P. C., Bronner, F., & De Ridder, J. A. (2011). “Highly recommended!” The content characteristics and perceived usefulness of online consumer reviews. Journal of Computer-Mediated Communication, 17(1), 19–38. https://doi.org/10.1111/j.1083-6101.2011.01551.x

Wood, V. R., & Robertson, K. R. (2000). Evaluating international markets. International Marketing Review, 17(1), 34–55. https://doi.org/10.1108/02651330010314704

Woods, S. (2016). # Sponsored: The emergence of influencer marketing. In Channcellor’s Honors Program Projects. School of Advertising & Public Relations.

Zietek, N. (2016). Influencer marketing: the characteristics and components of fashion influencer marketing [Dissertation, University of Borås, Faculty of Textiles, Engineering and Business]. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10721