Influential Factors of Travel Bubbles Intention During COVID-19 among Cambodians in Siem Reap and Preah Sihanouk

Visothy So

doi:10.14456/shserj.2023.35

Authors

Visothy So

DOI:

https://doi.org/10.14456/shserj.2023.35

CITATION

DOI: 10.14456/shserj.2023.35

Published: 2023-12-13

Keywords:

Travel Bubbles, Tourism, Behavioral intention, COVID-19, Cambodia

Abstract

Purpose: Travel bubbles, as the arrangements by the participant countries to open up their frontiers for travelers from partner destination or regions, has been widely implemented during COVID-19. Hence, this study aims to examine the influential factors of travel bubbles intention during COVID-19 among Cambodians in Siem Reap and Preah Sihanouk. The conceptual framework contains perceived usefulness, government support, innovativeness, trust, perceived risk, social influences, price value, and behavioral intention. Research design, data, and methodology: This quantitative study was distributed to 500 participants with a questionnaire. The sampling techniques involve judgmental, convenience, and snowball sampling. The Item Objective Congruence (IOC) Index and Cronbach’s Alpha coefficient value of the pilot test of 42 respondents were assessed before collecting the data. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) are applied. Results: The results are that government support, trust, perceived risk, and social influences significantly influence, whereas perceived usefulness, innovativeness, and price value have no significant influence on behavioral intention to adopt travel bubbles. Conclusions: Travel agencies and hospitality should strategize their businesses in response to the new type of measure due to the new normal for every individual being enforced and the new tourism trend being shaped.

References

Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102. https://doi.org/10.1108/JIBR-02-2014-0013

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238

Charag, A. H., Fazili, A. I., & Bashir, I. (2019). Determinants of consumer’s readiness to adopt Islamic banking in Kashmir. Journal of Islamic Marketing, 11(5), 1125-1154. https://doi.org/10.1108/JIMA-10-2018-0182

Chen, Y., Yu, J., Yang, S., & Wei, J. (2018). Consumer’s intention to use self-service parcel delivery service in online retailing: An empirical study. Internet Research, 28(2), 500-519. https://doi.org/10.1108/IntR-11-2016-0334

Dajani, D. (2016). Using the Unified Theory of Acceptance and Use of Technology to Explain E-commerce Acceptance by Jordanian Travel Agencies. Journal of Comparative International Management, 19(1), 99-118.

Davis, F., Bagozzi, R., & Warshaw, P. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982

DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Sage.

Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2013). Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management, 32(1), 58-64. https://doi.org/10.1016/j.jairtraman.2013.06.018

Fawaz, F., & Rahnama, M. (2014). An empirical refinement of the relationship between tourism and economic growth. Anatolia: An International Journal of Tourism and Hospitality Research, 25(3), 1-14.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312

Gao, L., & 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.

Gerrard, P., & Cunningham, J. B. (2003). The diffusion of internet banking among Singapore consumers. The International Journal of Bank Marketing, 21(1), 16-28.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.

Halassi, S., Semeijn, J., & Kiratli, N. (2019). From consumer to prosumer: A supply chain revolution in 3D printing. International Journal of Physical Distribution & Logistics Management, 49(2), 200-216. https://doi.org/10.1108/IJPDLM-03-2018-0139

Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: testing the UTAUT model, Information & Management, 48(1), 1-8. https://doi.org/10.1016/j.im.2010.09.001

Kaynak, E., & Kara, A. (2012). Assessing tourism market potential in a dynamic emerging economy: Theoretical and empirical insights from Cambodia. Asia Pacific Journal of Marketing and Logistics, 24(2), 199-221.

https://doi.org/10.1108/13555851211218020

Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing, 30(4), 303-322.

https://doi.org/10.1108/ 02652321211236923

Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.

Li, G. (2022, February 4). 'Living with COVID' Cambodia jumps to 2nd in Nikkei Recovery Index: Omicron-hit Philippines down 45 places as ASEAN members diverge. NIKKEI Asia. https://asia.nikkei.com/Spotlight/Coronavirus/COVID-19-Recovery-Index/Living-with-COVID-Cambodia-jumps-to-2nd-in-Nikkei-Recovery-Index

Luo, J. M., & Lam, C. F. (2020). Travel Anxiety, Risk Attitude and Travel Intentions towards “Travel Bubble” Destinations in Hong Kong: Effect of the Fear of COVID-19. International Journal of Environmental Research and Public Health, 17(21), 1-11. https://doi.org/10.3390/ijerph17217859

Madan, K., & Yadav, R. (2018). Understanding and predicting antecedents of mobile shopping adoption: A developing country perspective. Asia Pacific Journal of Marketing and Logistics, 30(1), 139-162. https://doi.org/10.1108/APJML-02-2017-0023

Mandrik, C. A., & Bao, Y. (2005). Exploring the concept and measurement of general risk aversion. ACR North American Advances, 32(1), 531-539.

Martins, C., Oliveira, T., & Popovim, C. A. (2014). Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002

Min, Y., Huang, J., Varghese, M. M., & Jaruwanakul, T. (2022). Analysis of Factors Affecting Art Major Students’ Behavioral Intention of Online Education in Public Universities in Chengdu. AU-GSB E-JOURNAL, 15(2), 150-158.

https://doi.org/10.14456/augsbejr.2022.80

Mom, K. (2022, October 24). Kingdom ranks fourth in latest Nikkei Covid-19 Recovery Index. The Phnom Penh Post. https://www.phnompenhpost.com/national/kingdom-ranks-fourth-latest-nikkei-covid-19-recovery-index

Ndubisi, N. O., & Sinti, Q. (2006). Consumer attitudes, system’s characteristics and Internet banking adoption in Malaysia. Management Research News, 29(1/2), 16-27.

Parasuraman, A., & Colby, C. L. (2015). An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59-74. https://doi.org/10.1177/1094670514539730

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.

Phonthanukitithaworn, C., Sellitto, C., & Fong, M. W. L. (2016). An investigation of mobile payment (m-payment) services in Thailand. Asia-Pacific Journal of Business Administration, 8(1), 37-54. https://doi.org/10.1108/APJBA-10-2014-0119

Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224-235.

Sakshi, S., Tandon, U., Ertz, M., & Bansal, H. (2020). Social vacation: Proposition of a model to understand tourists’ usage of social media for travel planning. Technology in Society, 63, 101438. https://doi.org/10.1016/j.techsoc.2020.101438

Saleh, R. M., & Al-Swidi, A. (2019). The adoption of green building practices in construction projects in Qatar: A preliminary study. Management of Environmental Quality: An International Journal, 30(6), 1238-1255. https://doi.org/10.1108/MEQ-12-2018-0208

San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350.

Schubert, S. F., Brida, J. G., & Risso, W. A. (2011). The impacts of international tourism demand on economic growth of small economies dependent on tourism. Tourism Management, 32(2), 377-385.

Selvanathan, S., Selvanathan, E. A., & Viswanathan, B. (2012). Causality between foreign direct investment and tourism: empirical evidence from India. Tourism Analysis, 17(1), 91-98.

Shao, Z., Guo, Y., Li, X., & Barnes, S. (2020). Sources of influences on customers’ trust in ride-sharing: Why use experience matters?. Industrial Management & Data Systems, 120(8), 1459-1482. https://doi.org/10.1108/IMDS-12-2019-0651

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286.

Sharma, S., Singh, G., Pratt, S., & Narayan, J. (2021). Exploring consumer behavior to purchase travel online in Fiji and Solomon Islands? An extension of the UTAUT framework. International Journal of Culture, Tourism and Hospitality Research, 15(2), 227-247. https://doi.org/10.1108/IJCTHR-03-2020-0064

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). Nova.

Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust: Consumers’ Adoption Intentions of Remote Mobile Payments. Psychology & Marketing, 32(8), 860-873. https://doi.org/10.1002/mar.20823

Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping: Evidences from India. Journal of Indian Business Research, 9(3), 248-264. https://doi.org/10.1108/JIBR-11-2016-0132

Tang, S., Selvanathan, E. A., & Selvanathan, S. (2007). The relationship between foreign direct investment and tourism: empirical evidence from China, Tourism Economics, 13(1), 25-39.

tom Dieck, M. C., Jung, T., Kim, W., & Moon, Y. (2017). Hotel guests’ social media acceptance in luxury hotels. International Journal of Contemporary Hospitality Management, 29(1), 530-550.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.

Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139.

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412

Walrave, M., Waeterloos, C., & Ponnet, K. (2020). Ready or Not for Contact Tracing? Investigating the Adoption Intention of COVID-19 Contact-Tracing Technology Using an Extended Unified Theory of Acceptance and Use of Technology Model. Cyberpsychology, Behavior, and Social Networking 24(6), 1-7. https://doi.org/10.1089/cyber.2020.0483

Wang, W. T., Wang, Y. S., & Liu, E. R. (2016). The stickiness intention of group-buying websites: the integration of the commitment-trust theory and e-commerce success model. Information and Management, 53(5), 625-642.

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002

Yee‐Loong Chong, A., Ooi, K., Lin, B., & Tan, B. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267-287. https://doi.org/10.1108/02652321011054963

Yueh, H. P., Huang, J. Y., & Chang, C. (2015). Exploring factors affecting students’ continued wiki use for individual and collaborative learning: an extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1), 16-31.

Zhong, K., Feng, D., Yang, M., & Jaruwanakul, T. (2022). Determinants of Attitude, Satisfaction and Behavioral Intention of Online Learning Usage Among Students During COVID-19. AU-GSB E-JOURNAL, 15(2), 49-57. https://doi.org/10.14456/augsbejr.2022.71