FACTORS INFLUENCING INTENTION TO USE E-COMMERCE AFTER COVID-19: THE CASE OF BRUNEI DARUSSALAM

Authors

  • Nuramalina Ainna Amirah Mohamad Esa Universiti Malaysia Sabah
  • Dodi Apriadi Chung Yuan Christian University
  • Ahmad Juliana Universitas Borneo Tarakan

DOI:

https://doi.org/10.51200/jaaab.7868

Keywords:

covid19, e-commerce, intention to use, perceived ease of use, perceived usefulness

Abstract

This study sought to identify the variables affecting Bruneian consumers' propensity to adopt e-commerce in the wake of the COVID-19 outbreak. The research looked at two important variables: perceived utility and perceived usability. The dependent variable, the intention to use e-commerce, was contrasted with these components. Based on the Technology Acceptance Model, the study aimed to forecast and comprehend the factors that will encourage Bruneians to embrace online shopping in the aftermath of the COVID-19 pandemic. Brunei Darussalam provided data for the study's analysis. On the data, this study applied Structural Equation Modeling (SEM) using Partial Least Squares (PLS). Given the study's recent appearance in the e-commerce scene, its conclusions might be useful to the government of Brunei as well as companies aiming to increase e-commerce acceptance and user base in the nation. Additionally, this study advances the field of e-commerce studies by offering new insights on the decision to embrace e-commerce during the COVID-19 epidemic, which was accompanied by a substantial shift in worldwide consumer behavior toward online shopping.

References

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice Hall.

Alagoz, S. M., & Hekimoglu, H. (2012). A study on TAM: Analysis of customer attitudes in online food ordering system. Procedia - Social and Behavioral Sciences, 62, 1138-1143. https://doi.org/10.1016/j.sbspro.2012.09.195

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. https://doi.org/10.1037/0033-2909.103.3.411

Anuar, A., Ravintharan, T., Rosli, N. A., & Ng, C. K. (2023). Factors influencing the intention to use e-commerce among Generation Y in Kota Bharu (Final year project thesis). Universiti Malaysia Kelantan.

Authority for Info-Communications Technology Industry of Brunei Darussalam. (2018). E-commerce survey for consumers.

Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737-746. https://doi.org/10.11118/actaun201866030737

Bazi, S., Filieri, R., & Gorton, M. (2022). Investigating the impact of situational influences and social support on social commerce during the COVID-19 pandemic. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 104-121. https://doi.org/10.3390/jtaer17010006

Bougie, R., & Sekaran, U. (2013). Research methods for business: A skill-building approach (6th ed.). John Wiley & Sons.

Bui, N., Nguyen, H. H., Le, T. T., & Nguyen, T. L. (2020). Intention to use mobile commerce. International Journal of Enterprise Information Systems, 16(1), 1-30. https://doi.org/10.4018/IJEIS.2020010101

Celik, H. E., & Yılmaz, V. (2011). Extending the technology acceptance model for adoption of e-shopping by consumers in Turkey. Journal of Electronic Commerce Research, 12(2), 152-164.

Cheung, C. M. K., Lee, M. K. O., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229-247. https://doi.org/10.1108/10662240810883290

Chevalier, S. (2022, September 21). Global retail e-commerce market size 2014-2021. Statista. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/

Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655-690). Springer.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

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

Dionysiou, G., Tsoumakas, G., & Vassiliadis, C. (2021). The impact of COVID-19 in e-commerce: Effects on consumer purchase behavior. In Strategic innovative marketing and tourism in the COVID-19 era (pp. 199-210). Springer. https://doi.org/10.1007/978-3-030-66154-0_22

Elwalda, A., Lü, K., & Ali, M. (2016). Perceived derived attributes of online customer reviews. Computers in Human Behavior, 56, 306-319. https://doi.org/10.1016/j.chb.2015.11.051

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. https://doi.org/10.3758/BRM.41.4.1149

Fayad, R., & Paper, D. (2015). The technology acceptance model e-commerce extension: A conceptual framework. Procedia Economics and Finance, 26, 1000-1006. https://doi.org/10.1016/S2212-5671(15)00922-3

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 1-30. https://doi.org/10.17705/1JAIS.00008

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519

Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-77. https://doi.org/10.17705/1CAIS.00407

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2016). Multivariate data analysis (7th ed.). Pearson.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202

Higueras-Castillo, E., Liébana-Cabanillas, F. J., Muñoz-Leiva, F., & Molinillo, S. (2023). Intention to use e-commerce vs physical shopping: Difference between consumers in the post-COVID era. Journal of Business Research, 157, Article 113622. https://doi.org/10.1016/j.jbusres.2022.113622

Jullie. (2017). Behavioral intention to use e-tax service system: An application of technology acceptance model. European Research Studies Journal, 20(2A), 48-64. https://doi.org/10.35808/ersj/628

Kawasaki, T., Wakashima, K., & Shibasaki, R. (2022). The use of e-commerce and the COVID-19 outbreak: A panel data analysis in Japan. Transport Policy, 115, 88-100. https://doi.org/10.1016/j.tranpol.2021.10.023

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003

Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(50), 752-780. https://doi.org/10.17705/1CAIS.01250

Liang, S. W. J., Ekinci, Y., Occhiocupo, N., & Whyatt, G. (2013). Antecedents of travellers’ electronic word-of-mouth communication. Journal of Marketing Management, 29(5-6), 584-606. https://doi.org/10.1080/0267257X.2013.771204

Lim, W. M., & Ting, D. H. (2012). E-shopping: An analysis of the technology acceptance model. Modern Applied Science, 6(4), 49-62. https://doi.org/10.5539/mas.v6n4p49

Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433-442. https://doi.org/10.1016/j.elerap.2007.02.002

Makame, W. H., Kang, J., & Park, S. (2014). Factors influencing electronic commerce adoption in developing countries: The case of Tanzania. South African Journal of Business Management, 45(2), 83-96. https://doi.org/10.4102/sajbm.v45i2.126

Nguyen-Viet, H., Tuyet-Hanh, T. T., Unger, F., Dang-Xuan, S., & Grace, D. (2017). Food safety in Vietnam: Where we are at and what we can learn from international experiences. Infectious Diseases of Poverty, 6(39), 1-6. https://doi.org/10.1186/s40249-017-0249-7

Nicewicz, R., & Bilska, B. (2021). Analysis of changes in shopping habits and causes of food waste among consumers before and during the COVID-19 pandemic in Poland. Environmental Protection and Natural Resources, 32(3), 8-19. https://doi.org/10.2478/oszn-2021-0010

Pham, L., Lim, D. H., & Hwang, Y. (2023). Factors influencing intention to use online consumer reviews. Journal of Global Information Management, 31(1), 1-22. https://doi.org/10.4018/JGIM.321642

Qiu, L., & Li, D. (2008). Applying TAM in B2C e-commerce research: An extended model. Tsinghua Science and Technology, 13(3), 265-272. https://doi.org/10.1016/S1007-0214(08)70043-9

Ramayah, T. (2014). SmartPLS 2.0. Institute of Postgraduate Studies, Universiti Sains Malaysia.

Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5-40.

Van, H. N., Nguyen, N., Nguyen, T. T. H., & Tran, T. V. (2021). Explaining intention to use mobile banking: Integrating perceived risk and trust into the technology acceptance model. International Journal of Applied Decision Sciences, 14(1), 55-79. https://doi.org/10.1504/IJADS.2021.112933

Warganegara, D. L., & Hendijani, R. B. (2022). Factors that drive actual purchasing of groceries through e-commerce platforms during COVID-19 in Indonesia. Sustainability, 14(6), Article 3235. https://doi.org/10.3390/su14063235

Yoon, C. (2009). The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Information & Management, 46(5), 294-301. https://doi.org/10.1016/j.im.2009.06.001

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Published

31-12-2025

How to Cite

Nuramalina Ainna Amirah Mohamad Esa, Dodi Apriadi, & Ahmad Juliana. (2025). FACTORS INFLUENCING INTENTION TO USE E-COMMERCE AFTER COVID-19: THE CASE OF BRUNEI DARUSSALAM. Journal of the Asian Academy of Applied Business (JAAAB), 10, 158–174. https://doi.org/10.51200/jaaab.7868
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