1. bookTom 15 (2020): Zeszyt s1 (October 2020)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2069-8887
Pierwsze wydanie
30 Mar 2015
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
Otwarty dostęp

The Online Technology Acceptance Model of Generation-Z People in Thailand during COVID-19 Crisis

Data publikacji: 23 Oct 2020
Tom & Zeszyt: Tom 15 (2020) - Zeszyt s1 (October 2020)
Zakres stron: 496 - 512
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2069-8887
Pierwsze wydanie
30 Mar 2015
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

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, 221-230.10.1016/j.rai.2016.06.003Search in Google Scholar

Abu-Shanab, E., & Ghaleb, O. (2012). Adoption of mobile commerce technology: An involvement of trust and risk concerns. International Journal of Technology Diffusion, 3(2), 36-49.10.4018/jtd.2012040104Search in Google Scholar

Acharya, V., Junare, S. O., & Gadhavi, D. D. (2019). E-payment: Buzz word or reality. International Journal of Recent Technology and Engineering, 8(3S2), 397-404.Search in Google Scholar

Alraja, M. N., Hammami, S., Chikhi, B., & Fekir, S. (2016). The influence of effort and performance expectancy on employee to adopt E-government: Evidence from Oman. International Review of Management and Marketing, 6(4), 930-934.Search in Google Scholar

Alwahaishi, S. & Snasel, V. (2013). Consumers’ acceptance and use of information and communications technology: A UTAUT and flow based theoretical model. Journal of Technology Management and Innovation, 8(2), 61-73.10.4067/S0718-27242013000200005Search in Google Scholar

An, L., Han, Y., & Tong, L. (2016). Study on the factors of online shopping intention for fresh agricultural products based on UTAUT2. The 2nd Information Technology and Mechatronics Engineering Conference, 303-306.10.2991/itoec-16.2016.57Search in Google Scholar

Andrea, B., Gabriella, H. C., & Timea, J. (2016). Y and Z generations at workplaces. Journal of Competitiveness, 8(3), 90-106.Search in Google Scholar

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), 6471-6490.10.12973/ejmste/78076Search in Google Scholar

Betz, C. L. (2019). Generations X, Y, and Z. Journal of Pediatric Nursing, 44, A7-A8.10.1016/j.pedn.2018.12.013Search in Google Scholar

Bratianu, C., Hadad, S. & Bejinaru, R. (2020). Paradigm shift in business education: a competence-based approach. Sustainability, 12(4), 1348-1365.10.3390/su12041348Search in Google Scholar

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 Technologies: E Network, Web & Security, 17(5), 4-22.Search in Google Scholar

Centers for Disease Control and Prevention. (2020). Interim guidance on management of coronavirus disease 2019 (COVI-19) in correctional and detention facilities. Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/downloads/guidance-correctional-detention.pdfSearch in Google Scholar

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, 1-14.10.3389/fpsyg.2019.01652Search in Google Scholar

Cochran, W. G. (1977). Sampling techniques. (3rd ed.). New York: John Willey and Sons.Search in Google Scholar

Dajani, D., & Hegleh, A. S. A. (2019). Behavioral intention of animation usage among university students. Heliyon, 5(2019), e02536.10.1016/j.heliyon.2019.e02536Search in Google Scholar

Dolot, A. (2018). The characteristic of Generation Z. e-mentor, 2(74), 44-50.10.15219/em74.1351Search in Google Scholar

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), 3737-3751.10.15666/aeer/1702_37373751Search in Google Scholar

Emergency Operation Center, Department of Disease Control, Ministry of Public Health of Thailand. (2020). The coronavirus disease 2019 situation. Retrieved from https://ddc.moph.go.th/viralpneumonia/eng/file/situation/situation-no112-240463n.pdfSearch in Google Scholar

Farrell, W., & Phungsoonthorn, T. (2020). Generation Z in Thailand. Retrieved from https://www.researchgate.net/publication/339508604_Generation_Z_in_Thailand.Search in Google Scholar

Fonseca, L.M., Dominigues, J.P. & Dima, A.M. (2020). Mapping the sustainable development goals relationships. Sustainability, 12(8), 3359. Special Issue: Sustainable Business Models and Innovation in the Knowledge Economy/Business Revolution in the Digital Era – Selected Papers from the 13th and 14th International Conference on Business Excellence.Search in Google Scholar

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.Search in Google Scholar

Gao, S., Krogstie, J., & Siau, K. (2014). Adoption of mobile information services: An empirical study. Mobile Information Systems, 10(2014), 147-171.10.1155/2014/146435Search in Google Scholar

Ghani, M. A., Rahi, S., Yasin, N. M., & Alnaser, F. M. (2017). Adoption of internet banking: Extending the role of technology acceptance model (TAM) with e-customer service and customer satisfaction. World Applied Sciences Journal, 35(9), 1918-1929.Search in Google Scholar

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivaliate data analysis. (7th ed.). US: Pearson Education.Search in Google Scholar

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.Search in Google Scholar

Halasi, D., Vlacsekova, D., & Szobi, A. (2016). The generation Z on the labour market in South Alovakia. International Scientific Conference FERNSTAT 2016. Banska Bystrica, Slovakia.Search in Google Scholar

Henseler, J. F., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135.10.1007/s11747-014-0403-8Search in Google Scholar

Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information and Management, 45, 1-9.10.1016/j.im.2007.03.005Search in Google Scholar

Jaradat, M. I. R. M., & Al-Mashaqba, A. M. (2014). Understanding the adoption and usage of mobile payment services by using TAM3. International Journal of Business Information Systems, 16(3), 271-296.10.1504/IJBIS.2014.063768Search in Google Scholar

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, 33-52.10.28945/1994Search in Google Scholar

Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, 14(1), 21-38.10.4301/S1807-17752017000100002Search in Google Scholar

Lee, J. H. & Song, C. H. (2013). Effect of trust and perceived risk on user acceptance of a new technology service. Social Behavior and Personality, 41(4), 587-598.10.2224/sbp.2013.41.4.587Search in Google Scholar

Liebana-Cabanillas, F., Sanchez-Fernandez, J., & Munoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478.10.1016/j.chb.2014.03.022Search in Google Scholar

Liu, D., Maimaitijiang, R., Gu, J., Zhong, S., Zhou, M., Wu, Z., Luo, A., Lu, C., & Hao, Y. (2019). Using the unified theory of acceptance and use of technology (UTAUT) to investigate the intention to use physical activity apps: Cross-sectional survey. JMIR Mhealth Uhealth, 7(9), e13127.10.2196/13127Search in Google Scholar

Lwoga, E. T., & Lwoga, N. B. (2017). User acceptance of mobile payment: The effects of user-centric security, system characteristics and gender. The Electronic Journal of Information Systems in Developing Countries, 81(3), 1-24.10.1002/j.1681-4835.2017.tb00595.xSearch in Google Scholar

Maduku, D. K. (2014). Behavioral intention towards mobile banking usage by South African retail banking clients. Investment Management and Financial Innovations, 11(3), 37-51.Search in Google Scholar

Marchewka, J. T., & Kostowa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), 93-104.Search in Google Scholar

Mazhar, F., Rizwan, M., Fiaz, U., Ishrat, S., Razzaq, M. S., & Khan, T. N. (2014). An investigation of factors affecting usage and adoption of internet and mobile banking in Pakistan. International Journal of Accounting and Financial Reporting, 4(2), 478-501.10.5296/ijafr.v4i2.6586Search in Google Scholar

Nawaz, S. S., & Yamin, F. B. M. (2018). Sri Lankan customers’ behavioral intention to use mobile banking: A structural equation modelling approach. Journal of Information Systems and Information Technology, 2(2), 1-14.Search in Google Scholar

Onaolapo, S., & Oyewole,O. (2018). Performance expectancy, effort expectancy, and facilitating conditions as factors influencing smart phones use for mobile learning by postgraduate students of the University of Ibadan, Nigeria. Interdisciplinary Journal of E-Skills and Lifelong Learning, 14, 95-115.10.28945/4085Search in Google Scholar

Rahi, S., & Abd. Ghani, M. (2018). The role of UTAUT, DOI, perceived technology security and game elements in internet banking adoption. World Journal of Science, Technology and Sustainable Development, 15(4), 338-356.10.1108/WJSTSD-05-2018-0040Search in Google Scholar

Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.Search in Google Scholar

Rouibah, K., & Abbas, H. (2010). Effect of personal innovativeness, attachment motivation and social norms on the acceptance of camera mobile phones: An empirical study in an Arab country. International Journal of Handheld Computing Research, 1(4), 41-62.10.4018/jhcr.2010100103Search in Google Scholar

Sair, S. A., & Danish, R. Q. (2018). Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention though personal innovativeness among Pakistan consumers. Pakistan Journal of Commerce and Social Sciences, 12(2), 501-520.Search in Google Scholar

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), 92-105.10.5923/j.ijis.20120206.05Search in Google Scholar

Salleh, M. S. M., Mahbob, N. N., & Baharudin, N. S. (2017). Overview of “Generation Z” behavioral characteristic and its effect towards hostel facility. International Journal of Real Estate Studies, 11(2), 59-67.Search in Google Scholar

Shafinah, K., Sahari, N., Sulaiman, R., Yusoff, M. S. M., & Ikram, M. M. (2013). Determinants of user behavioral intention (BI) on mobile services: A preliminary view. Procedia Technology, 11(2013), 127-133.Search in Google Scholar

Singh, A. (2014). Challenges and issues of generation Z. IOSR Journal of Business and Management, 16(7), 59-63.10.9790/487X-16715963Search in Google Scholar

Siraye, Z. (2014). Customers’ adoption of electronic banking service channels in Ethiopia: An integration of technology acceptance model and perceived risk with theory of planned behaviour. International Journal of Electronic Finance, 8(1), 21-34.10.1504/IJEF.2014.063993Search in Google Scholar

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, 1-12.10.1177/2158244013503837Search in Google Scholar

Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology, 9(3), 71-85.Search in Google Scholar

Torocsik, M., Szucs, K., & Kehl, D. (2014). How generations think: Research on generation Z. Acta Universitatis Sapientiae, Communicatio, 1(2014), 23-45.Search in Google Scholar

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), 328-376.10.17705/1jais.00428Search in Google Scholar

Wong, K.K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 23. Retrieved from http://marketing-bulletin.massey.ac.nz/V24/MB_V24_T1_Wong.pdfSearch in Google Scholar

World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): Situation Report-97. Retrieved from https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200426-sitrep-97-covid-19.pdf?sfvrsn=d1c3e800_6Search in Google Scholar

Zuiderwijk, A., Janssen, M., & Dwivedi, Y. K. (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, 32, 429-440.10.1016/j.giq.2015.09.005Search in Google Scholar

Polecane artykuły z Trend MD

Zaplanuj zdalną konferencję ze Sciendo