Assessing Behavioral Intention of Outbound Travelers’ Travel Bubbles Amid COVID-19 in Phnom Penh, Cambodia

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visothy so


Purpose: Travel Bubbles could be one of the solutions for outbound travelers in Cambodia during COVID-19, the research investigates the factors that influence the behavioral intention of outbound travelers to consider the travel bubbles amid COVID-19 in Phnom Penh. The key constructs are perceived usefulness, government support, innovativeness, trust, perceived risk, social influences, price value, and behavioral intention. Research design, data, and methodology: This study employs a quantitative method through the survey distribution to 500 participants. The sampling techniques involve judgmental, convenience, and snowball sampling. Before the data collection, the construct validity and reliability test were conducted by The Item Objective Congruence (IOC) Index and Cronbach’s Alpha coefficient value of the pilot test of 50 respondents. The data analysis was made in SPSS AMOS, applying Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: The findings present that perceived usefulness, trust, perceived risk, social influences, and price value significantly influence behavioral intention, whereas government support and innovativeness have no significant influence on behavioral intention to adopt travel bubbles. Conclusions: This study contributes to its significance for the Cambodian tourism industry to understand the right action and approach effectively to reengage with the travel savvy in the recovery period during and post-pandemic.


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so, visothy. (2023). Assessing Behavioral Intention of Outbound Travelers’ Travel Bubbles Amid COVID-19 in Phnom Penh, Cambodia . AU-GSB E-JOURNAL, 16(1), 131-139.
Author Biography

visothy so

Ph.D. Candidate in Technology Education and Management, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand.


Aldás‐Manzano, J., Lassala‐Navarré, C., Ruiz‐Mafé, C., & Sanz‐Blas, S. (2009). Key drivers of internet banking services use. Online Information Review, 33(4), 672–695.

Amin, H., Rahim Abdul Rahman, A., Laison Sondoh, S., & Magdalene Chooi Hwa, A. (2011). Determinants of customers’ intention to use Islamic personal financing: The case of Malaysian Islamic banks. Journal of Islamic Accounting and Business Research, 2(1), 22–42.

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.

Benjangjaru, B., & Vongurai, R. (2018). Behavioral Intention of Bangkokians to Adopt Mobile Payment Services by Type of Users. AU-GSB E-JOURNAL, 11(1), 34-46.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.

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.

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.

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.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

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.

Gupta, A., Dogra, N., & George, B. (2018). What determines tourist adoption of smartphone apps?: An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, 9(1), 50–64.

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

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th 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.

Husin, M. H., Loghmani, N., & Zainal Abidin, S. S. (2017). Increasing e-government adoption in Malaysia: MyEG case study. Journal of Systems and Information Technology, 19(3/4), 202–227.

Ivanov, S. H., Webster, C., Stoilova, E., & Slobodskoy, D. (2020). Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies – A conceptual framework. Tourism Economics, 28(1), 3-26.

Kaitlyn, F., & Jay, B. (2020, June 12). In hotels and beyond, UV light robots and lamps could help protect against coronavirus. AbcNEWS. hotels-uv-light-robots-lamps-protect-coronavirus/story?id=71205829

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.

Lifen Zhao, A., Koenig‐Lewis, N., Hanmer‐Lloyd, S., & Ward, P. (2010). Adoption of internet banking services in China: Is it all about trust? International Journal of Bank Marketing, 28(1), 7–26.

Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-891. j.chb.2004.03.003

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.

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.

Mandari, H. E., Chong, Y.-L., & Wye, C.-K. (2017). The influence of government support and awareness on rural farmers’ intention to adopt mobile government services in Tanzania. Journal of Systems and Information Technology, 19(1/2), 42–64.

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

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The Journal of Strategic Information Systems, 11(3-4), 297-323.

Ministry of Tourism Cambodia. (2020, October 11). Cambodia Annual Report of Tourism Statistic 2019.

Mom, K. (2022, October 24). Kingdom ranks fourth in latest Nikkei Covid-19 Recovery Index. The Phnom Penh Post.

Morgan, R. M., & Hunt, S. D. (1994). The commitment–trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38.

MPTC. (2021, March 2). Scan QR Code to help fight Covid-19.

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.

Rahimizhian, S., & Irani, F. (2021). Contactless hospitality in a post-Covid-19 world. International Hospitality Review, 35(2), 293-304.

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.

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.

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.

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.

Shin, H., & Kang, J. (2020). Reducing perceived health risk to attract hotel customers in the COVID-19 pandemic era: Focused on technology innovation for social distancing and cleanliness. International Journal of Hospitality Management, 91(2), 1-9.

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

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.

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.

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.

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.

Wen, Z., Huimin, G., & Kavanaugh, R. R. (2005). The impacts of SARS on the consumer behaviour of Chinese domestic tourists. Current Issues in Tourism, 8(1), 22-38.

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.

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.

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.