Predicting Intention to Continue Using E-Tourism Technologies amidst Covid-19 Endemic: A PLS-SEM Approach
Keywords:
Continuance Intention, Covid-19, E-tourism Technologies, Satisfaction
Abstract
After almost two years of stalling, the tourism sector is now thriving as we move into the Covid-19 endemic phase, making travel safer. Therefore, continuous use of personalized immersive technology that can deliver timely information and shield users from the outbreak is essential. This study looks at how travellers intend to continue using e-tourism technologies which include augmented reality, social media, smartphone apps, smart technologies, websites and reservation systems. Purposive sampling was used to select 200 respondents who were above 18 years old to participate in a survey that was based on the Expectation-Confirmation Model. The acquired data was then examined using PLS SEM for hypothesis testing. The results showed that satisfaction with e-tourism technologies was the most significant predictor of motivation to keep using such technologies. Further discussion includes contributions to the field of knowledge as well as for players, developers and service providers in the tourism sector.
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References
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Lee, Y. & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Application, 10, 342–357.
Liao, C., Chen, J. L. & Yen, D. C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23, 2804-2822
Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134–159.
Ministry of Health Malaysia. (2022, June 19) COVIDNOW in Malaysia. Retrieved from https://covidnow.moh.gov.my
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Ramayah, T., Cheah, J., Chuah, F., Ting, H. & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis (2nd Ed.). Kuala Lumpur, Malaysia: Pearson.
Ringle, C. M., Wende, S. & Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.smartpls.com
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Thominathan, S. & Ramayah, T. (2014). Explaining the e-government usage using Expectation Confirmation Model: The case of electronic tax filing in Malaysia. In L. G. Anthopoulos & C. G. Reddick (Eds.), Government e-Strategic Planning and Management (pp. 287–304). New York, NY: Springer Science+Business Media New York. http://doi.org/10.1007/978-1-4614-8462-2
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Wen, C., Prybutok, V. R. & Xu, C. (2011). An integrated model for customer online repurchase intention. Journal of Computer Information Systems, 52, 14-23.
Wilder-Smith, A. & Freedman, D.O. (2020) Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), 1–4.
Yuksel, A., Yuksel, F. & Bilim, Y. (2010). Destination attachment: Effects on customer satisfaction and cognitive, affective and conative loyalty. Tourism Management, 31(2010), 274–284.
Yusof, T. A. (2023, May 30). Covid-19: Health Ministry sticking to timeline for transition to endemic status. The Star. Retrieved from https://www.thestar.com.my/news/nation/2023/05/30/covid-19-health-ministry-sticking-to-timeline-for-transition-to-endemic-status
Al-Hattami, H. M., Abdullah, A. A. H. & Khamis, A. A. A. (2021). Determinants of intention to continue using Internet banking: Indian context. Innovative Marketing, 17(1), 40–52.
Anderson, J. C. & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Bernama. (2020, June 27). Covid-19: Malaysia's tourism industry hit with RM45 billion in losses. New Straits Times. Retrieved from https://www.nst.com.my/news/nation/2020/06/604012/covid-19-malaysias-tourism-industry-hit-rm45-billion-losses
Bhattacherjee, A. (2001). Understanding information systems continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370.
Bhattacherjee, A., Perols, J. & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17–26.
Bölen, M. C. & Özen, Ü. (2020). Understanding the factors affecting consumers’ continuance intention in mobile shopping: The case of private shopping clubs. International Journal of Mobile Communications, 18(1), 101–129. https://doi.org/10.1504/IJMC.2020. 104423
Cain, M. K., Zhang, Z. & Yuan, K. H. (2016). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716-1735.
Chin, W. W., Marcolin, B. L. & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion adoption study. Information Systems Research, 14(2), 189-217.
Chong, A. Y. L. (2013). Understanding mobile commerce continuance intentions: An empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22–30. https://doi.org/10.1080/ 08874417.2013.11645647
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https:// doi.org/10.2307/249008
Foroughi, B., Iranmanesh, M. & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015–1033. https:// doi.org/10.1108/JEIM-10-2018-0237
Franke, G. & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430-447.
Hahn, E. D. & Ang, S. H. (2017). From the editors: New directions in the reporting of statistical results in the Journal of World Business. Journal of World Business, 52(2), 125-126.
Hair, J. F., Thomas, G., Hult, M., Ringle, C. M. & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (2nd Ed.). Thousand Oakes, CA: Sage.
Hair, J., Risher, J., Sarstedt, M. & Ringle, C. (2019), When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
Henseler, J., Ringle, C. & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Hossain, M. A. & Quaddus, M. (2012). Expectation-confirmation theory in information system research: A review and analysis. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information Systems Theory: Explaining and Predicting Our Digital Society, 1, 441–469. New York, NY: Springer New York. http://doi.org/10.1007/978-1-4419-6108-2
Hsiao, C. H., Chang, J. J. & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342–355. doi:10.1016/j.tele.2015.08.014
Kennedy-Eden, H. & Gretzel, U. (2012). A taxonomy of mobile applications in tourism. E-review of Tourism Research, 10(2), 47-50.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.
Kock, N. & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.
Korzeb, Z. & Niedzió?ka, P. (2020). Resistance of commercial banks to the crisis caused by the COVID-19 pandemic: The case of Poland. Equilibrium Quarterly Journal of Economics and Economic Policy, 15, 205–234.
Kuc-Czarnecka, M. (2020). COVID-19 and digital deprivation in Poland. Oeconomia Copernicana, 11, 415–431.
Lee, Y. & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Application, 10, 342–357.
Liao, C., Chen, J. L. & Yen, D. C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23, 2804-2822
Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134–159.
Ministry of Health Malaysia. (2022, June 19) COVIDNOW in Malaysia. Retrieved from https://covidnow.moh.gov.my
Oghuma, A. P., Chang, Y., Libaque-Saenz, C. F., Park, M. C. & Rho, J. J. (2015). Benefit-confirmation model for post-adoption behavior of mobile instant messaging applications: A comparative analysis of KakaoTalk and Joyn in Korea. Telecommunications Policy, 39(8), 1–20. doi:10.1016/j.telpol.2015.07.009
Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F. & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34–47. doi:10.1016/j.tele.2015.05.006
Pourfakhimi, S., Duncan, T., Ould, L., Allan, K. & Coetzee, W. (2020). Acceptance and adoption of e-tourism technologies. In Z. Xiang et al. (Eds.), Handbook of e-Tourism (pp. 1 – 31). Switzerland: Springer Nature AG 2020. https://doi.org/10.1007/978-3-030-05324-6_58-1
Ramayah, T., Cheah, J., Chuah, F., Ting, H. & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis (2nd Ed.). Kuala Lumpur, Malaysia: Pearson.
Ringle, C. M., Wende, S. & Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.smartpls.com
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S. & Ringle, C. M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLS predict. European Journal of Marketing, 53(11), 2322-2347.
Thominathan, S. & Ramayah, T. (2014). Explaining the e-government usage using Expectation Confirmation Model: The case of electronic tax filing in Malaysia. In L. G. Anthopoulos & C. G. Reddick (Eds.), Government e-Strategic Planning and Management (pp. 287–304). New York, NY: Springer Science+Business Media New York. http://doi.org/10.1007/978-1-4614-8462-2
World Tourism Organization. (2020). International tourist numbers could fall 60-80% in 2020. UNWTO Reports. Retrieved from https://www.unwto.org/news/covid-19-international-touristnumbers-could-fall-60-80-in-2020
Wen, C., Prybutok, V. R. & Xu, C. (2011). An integrated model for customer online repurchase intention. Journal of Computer Information Systems, 52, 14-23.
Wilder-Smith, A. & Freedman, D.O. (2020) Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), 1–4.
Yuksel, A., Yuksel, F. & Bilim, Y. (2010). Destination attachment: Effects on customer satisfaction and cognitive, affective and conative loyalty. Tourism Management, 31(2010), 274–284.
Yusof, T. A. (2023, May 30). Covid-19: Health Ministry sticking to timeline for transition to endemic status. The Star. Retrieved from https://www.thestar.com.my/news/nation/2023/05/30/covid-19-health-ministry-sticking-to-timeline-for-transition-to-endemic-status
Published
2023-11-10
How to Cite
Luqman, A., Li, C. C., & Mohamad, S. S. (2023). Predicting Intention to Continue Using E-Tourism Technologies amidst Covid-19 Endemic: A PLS-SEM Approach. Information Management and Business Review, 15(4(SI)I), 77-85. https://doi.org/10.22610/imbr.v15i4(SI)I.3578
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Research Paper
Copyright (c) 2023 Azyanee Luqman, Cheam Chai Li, Siti Sarah Mohamad
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