Retailers’ behavioral intention and behavior in adopting e-commerce platforms

Thi Thuy Nguyen, Van Duong Ha, Linh Thi Thuy Nguyen

Article ID: 7257
Vol 8, Issue 9, 2024

VIEWS - 159 (Abstract) 70 (PDF)

Abstract


This study conducts research on retailers’ behavioral intentions and behavior in adopting e-commerce platforms (ECPs) and uses the unified theory of acceptance and use of technology (UTAUT2) model as well as add other factors such as Personalization Platform, Seamless Interaction. The findings show that Effort Expectancy, Social Influence, Hedonic Motivation, Retailers’ Capacity, Integration Strategies have a positive impact on retailers’ behavioral intention of adopting ECPs and Performance Expectancy has a negative impact on retailers’ behavioral intention of adopting ECPs. At the same time, Behavioral Intention, Facilitating Conditions have a positive impact on retailers’ behavior adopting ECPs and Seamless Interaction has a negative impact on retailers’ behavior adopting ECPs. With important implications, these findings are proposed to relevant parties, helping retailers and ECPs suppliers identify factors affecting retailers’ behavioral intention and behavior in adopting ECPs in Vietnam.


Keywords


behavioral intention; e-commerce; effort expectation; performance expectation; UTAUT2

Full Text:

PDF


References


Abubakar, F. M., & Ahmad, H. B. (2013). The Moderating Effect of Technology Awareness on the Relationship between UTAUT Constructs and Behavioural Intention to Use Technology: A Conceptual Paper. Australian Journal of Business and Management Research, 03(02), 14–23. https://doi.org/10.52283/nswrca.ajbmr.20130302a02

Agarwal, R. N. (2020). The Role of Effective Factors in Utaut Model on Behavioral Intention. Business Excellence and Management, 10(3), 5–23. https://doi.org/10.24818/beman/2020.10.3-01

Alshehri, A., Rutter, M. J., & Smith, S. (2019). An Implementation of the UTAUT Model for Understanding Students’ Perceptions of Learning Management Systems. International Journal of Distance Education Technologies, 17(3), 1–24. https://doi.org/10.4018/ijdet.2019070101

Aqeel, U. (2020). Analyzing the Impact of Economic Shock Due to Corona Viral Disease-19 on Consumer Behavior Pattern: A Cross Sectional Study Conducted in Delhi and National Capital Region. Bioscience Biotechnology Research Communications, 13(4), 1926–1937. https://doi.org/10.21786/bbrc/13.4/44

Byrne, B. M., & Campbell, T. L. (1999). Cross-Cultural Comparisons and the Presumption of Equivalent Measurement and Theoretical Structure. Journal of Cross-Cultural Psychology, 30(5), 555–574. https://doi.org/10.1177/0022022199030005001

Dougherty, S. (2024). Mastering e-commerce personalization for brand success. Available online: https://funnel.io/blog/e-commerce-personalization (accessed on 6 March 2024).

Dutta, S., & Shivani, S. (2020). Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. In: Sharma, S. K., Dwivedi, Y. K., Metri, B., et al. (editors). IFIP Advances in Information and Communication Technology. Springer International Publishing. https://doi.org/10.1007/978-3-030-64861-9

Ezennia, C. S., & Marimuthu, M. (2020). Factors that positively influence e-commerce adoption among professionals in Surulere, Lagos, Nigeria. African Journal of Science, Technology, Innovation and Development, 14(2), 405–417. https://doi.org/10.1080/20421338.2020.1840051

Gulbrandsen, J. (2024). How Seamless Interaction in Digital Marketing Drives Customer Engagement. Available online: https://www.linkedin.com/pulse/how-seamless-interaction-digital-marketing-drives-gulbrandsen-hae7f/ (accessed on 9 March 2024).

Hà, V. D. (2022), Digital currency and Blockchain technology textbook. Ho Chi Minh City Economic Publishing House, Ho Chi Minh City, Vietnam (Vietnamese).

Ha, V. D., & Nguyen, T. T. (2022). Behavioral Assessments of Using Fintech Services and E-Commerce. Eliva Press.

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.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a reliability coefficient be too high? Journal of Consumer Psychology, 10(1/2), 55-58.

Hungilo, G. G., & Setyohadi, D. B. (2020). Factors influencing acceptance of online shopping in Tanzania using UTAUT2. Journal of Internet Banking and Commerce, 25(1), 1-23.

Juaneda-Ayensa, E., Mosquera, A., & Murillo, Y. S. (2016). Omnichannel customer behavior: Key drivers of technology acceptance and use and their effects on purchase intention. Frontiers in Psychology, 7, 1-11. https://doi.org/10.3389/fpsyg.2016.01117

Kamalia, D., Djajadinata, M., Gunawan, F. H., & Gunadi, W. (2022). The Role of Hedonic Motivation and FOMO on the Impulsivity of e-Commerce Users during COVID-19 Pandemics in Indonesia. In: Proceedings of the International Conference on Industrial Engineering and Operations Management Istanbul, Turkey.

Kline, R. B. (2011). Principles and practice of structural equation modeling, 3rd ed. New York: The Guilford Press.

Kumar, M., & Ayedee, D. (2021). Technology Adoption: A Solution for SMEs to overcome problems during COVID-19. Academy of Marketing Studies Journal, 25(1).

Li, Y., & Li, Y. (2020). Study of Merchant Adoption in Mobile Payment System Based on Ensemble Learning. American Journal of Industrial and Business Management, 10(5), 861–875. https://doi.org/10.4236/ajibm.2020.105058

Mansur, D., Sule, E., Kartini, D., & Oesman, Y. (2019). Understanding Factors Influence E-Commerce Use Behavior in Indonesia. Opción: Revista de Ciencias Humanas y Sociales, 35(20), 742-754.

Misra, R., Mahajan, R., Singh, N., et al. (2022). Factors impacting behavioural intentions to adopt the electronic marketplace: findings from small businesses in India. Electronic Markets, 32(3), 1639–1660. https://doi.org/10.1007/s12525-022-00578-4

Mohd Ariffin, N. H., Ahmad, F., & Mohd Haneef, U. (2020). Acceptance of mobile payments by retailers using UTAUT model. Indonesian Journal of Electrical Engineering and Computer Science, 19(1), 149. https://doi.org/10.11591/ijeecs.v19.i1.pp149-155

Nguyen, A. T., Pham, T. T., Song, J., et al. (2023). Behavioral Intention and Behavior of Using E-Commerce Platforms for Online Purchases and Payments by Vietnamese Consumers. In: Contemporary Economic Issues in Asian Countries: Proceeding of CEIAC 2022. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-9669-6

Oliveira, T., Thomas, M., & Baptista, G. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030

Optimizely. (2023). Ecommerce platform. Available online: https://www.optimizely.com/optimization-glossary/ecommerce-platform/ (accessed on 6 March 2024).

Piotrowicz, W., & Cuthbertson, R. (2014). Introduction to the Special Issue Information Technology in Retail: Toward Omnichannel Retailing. International Journal of Electronic Commerce, 18(4), 5-16.

Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In: An integrated approach to communication theory and research. Routledge. pp. 432-448.

Sanan, E. (2024). 10 Best E-Commerce Personalization Platforms. Available online: https://popupsmart.com/blog/ecommerce-personalization-platform (accessed on 12 April 2024).

Setiyani, L., Natalia, I., & Liswadi, G. T. (2023). Analysis of Behavioral Intentions of ECommerce Shopee Users in Indonesia Using UTAUT2. ADI Journal on Recent Innovation, 4(2), 160–171.

Shadfar, M. & Malekmohammadi, I. (2013). Application of Structural Equation Modeling (SEM) in restructuring state intervention strategies toward paddy production development. International Journal of Academic Research in Business and Social Sciences, 3 (12), 576-618.

Singh, A., Alryalat, M. A. A., Alzubi, J. A., & Sarma, H. (2017). Understanding Jordanian consumers’ online purchase intentions: Integrating trust to the UTAUT2 framework. International Journal of Applied Engineering Research, 12(20), 10258-10268.

Subawa, N. S., & Mimaki, C. A. (2019). E-marketplace acceptance of MSMEs in Bali based on performance expectancy and task technology fit. In: Proceedings of the 2nd International Conference on E-Business, Information Management and Computer Science. pp. 1–4. https://doi.org/10.1145/3377817.3377838

Trenz, M., Veit, D., & Tan, C.W. (2020). Disentangling the Impact of Omni-Channel Integration Services on Consumer Behavior in Integrated Sales Channels. MIS Quarterly, 44(3), 1207-1258.

VECOM. (2024). Vietnam E-Commerce Business Index Report_EBI 2024. Available online: http://en.vecom.vn/vietnam-e-commerce-business-index-report-ebi-2024 (accessed on 13 March 2024).

Venkatesh, V., Thong, J. Y., & 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.

Wang, S., Zheng, S., Xu, L., Li, D., & Meng, H. (2008). A literature review of electronic marketplace research: Themes, theories and an integrative framework. Information Systems Frontiers, 10(5), 555–571. https://doi.org/10.1007/s10796-008-9115-2

Wijaya, K., & Handriyantini, E. (2020). Analysis of factors affecting behavioral intention on the online marketplace using the UTAUT model (Case Study: Shopee). Proceedings of SeNTI, 4(1), 323–332. https://doi.org/10.1007/s10916-021-01785-w

Wulandari, A., Junipriansa, D., Suryawardani, B., & Marcelino, D. (2022). Integrаtion the UTАUT2 Model: Аdoption of E-Commerce as Solution for Fаshion Industry in Bаndung Fаcing the COVID-19 Pаndemic. PalArch’s Journal of Archaeology of Egypt/Egyptology, 19(1), 1609-1630.

Yoga, I. M. S., & Triami, N. P. S. (2021). The Online Shopping Behavior of Indonesian Generation X toward E-Commerce. Journal of Economics, Business, and Accountancy Ventura, 23(3), 441-451.

Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902–1911. https://doi.org/10.1016/j.chb.2012.05.008




DOI: https://doi.org/10.24294/jipd.v8i9.7257

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Thi Thuy Nguyen, Van Duong Ha, Linh Thi Thuy Nguyen

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.