Abstract :
Due to the widespread infection of the coronavirus (COVID-19) over the world, people were forced to stay at home, and technology has been increasingly used in communication, entertainment, and work. This research emphasized the study on the technology usage of Generation Z who are ready and highly skilled in using technology. The purpose of the study was to investigate the influence of key factors affecting the intended use and practical application of Generation Z technology in Cambodia during the COVID-19 pandemic. The researcher chose to use the UTAUT Model to test the research hypothesis. A questionnaire is a research tool used to collect data online. It was found that the sample size was 212 respondents. Descriptive analysis and the partial least square structural equation model (PLS-SEM) evaluation were performed. The results revealed that (1) most of the respondents were male, had under a bachelor’s degree, and monthly income was equal to or more than 312 US dollars, (2) the performance expectancy significantly influenced the behavioral intention, but effort expectancy, social influence, and facilitating conditions did not affect the behavioral intention of technology use, and (3) the behavioral intention to use technologies significantly influenced the actual use behavior during the covid-19 situation. This study suggests that technology organizations or businesses should pay attention to the potential benefits of technology for spurring the technology adoption and use of Generation Z people in Cambodia.
Keywords :
Cambodia, COVID-19, Generation-Z, Technology Use, UTAUT ModelReferences :
1. Abrahao, R. S., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the unified theory of acceptance and use of technology (UTAUT). Revista de Administracao e Inovacao, 13, 221-230.
2. Betz, C. L. (2019). Generations X, Y, and Z. Journal of Pediatric Nursing, 44, A7-A8.
3. Bervell, B., & Umar, I. N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogenous variables in higher education technology acceptance research. EURASIA Journal of Mathematics Science and Technology Education, 13(10), 6471-6490.
4. Boakye, E. A. & Meng, Q. (2019). Service quality and customer loyalty in the Ghanaian banking sector: The mediating role of customer satisfaction. International Journal of Business and Management Invention, 8(8), 78-84.
5. Catherine, N., Geofrey, K. M., Moya, M. B., & Aballo, G. (2017). Effort expectancy, performance expectancy, social influence and facilitating conditions as predictors of behavioral intentions to use atms with fingerprint authentication in Ugandan banks, Global Journal of Computer Science and Technology: E-Network, Web & Security, 17(5), 5-22.
6. Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1-14.
7. Cochran, W. G., 1977. Sampling techniques. 3rd ed. New York: John Willey and Sons.
8. Duzenli, T., Alpak, E. M., & Yilmaz, S. (2019). The correlation between urban open space occupation differences among generations X, Y, and Z occupant well-being. Applied Ecology and Environmental Research, 17(2), 3737-3751.
9. Gaidhani, S., Arora, L, & Sharma, B. K. (2019). Understanding the attitude of generation Z towards workplace. International Journal of Management, Technology, and Engineering, 9(1), 2804-2812.
10. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E., (2014). Multivaliate data analysis. 7th ed. US: Pearson Education.
11. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. (2nd ed.). Thousand Oaks: Sage.
12. Khechine, H., Lakhal, S., Pascot, D., & Bytha, A. (2014). UTAUT model for blended learning: The role of gender and age in the intention to use webinars. Interdisciplinary Journal of E-Learning and Learning Objects, 10, 33-52.
13. Kurt, O. E., & Tingoy, O. (2017). The acceptance and use of a virtual learning environment in higher education: An empirical study in Turkey, and the UK. International Journal of Educational Technology in Higher Education, 14(26), 1-15.
14. Mensah, I. K. (2019). Factors influencing the intention of university students to adopt and use e-government services: An empirical evidence in China. SAGE Open, April-June, 1-19.
15. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.
16. Sair, S. A., & Danish, R. Q. (2018). Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers, Pakistan Journal of Commerce and Social Sciences, 12(2), 501-520.
17. Salim, B. (2012). An application of UTAUT model for acceptance of social media in Egypt: A statistical study. International Journal of Information Science, 2(6), 92-105.
18. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English w-learning websites in Taiwan. SAGE Open, October-December, 1-12.
19. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
20. World Health Organization. (2021a). Weekly epidemiological update on COVID-19 – 22 June 2021. Retrieved from https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19—22-june-2021
21. World Health Organization. (2021b). COVID-19 Joint WHO-MOH Situation Report 50 – 14 June 2021. Retrieved from https://www.who.int/cambodia/internal-publications-detail/covid-19-joint-who-moh-situation-report-50
22. Zhou, L. L., Owusu-Marfo, J., Antwi, H. A., Antwi, M. O., Kachie, A. D. T., & Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electric information management systems (HEIMS) in Ghana: Using the unified theory of acceptance and use of technology (UTAUT) model. BMC Medical Informatics and Decision Making, 19, 1-9.