Online purchase intention supported by Artificial Intelligence: The role of usefulness and customization
Main Article Content
Abstract
The trend of applying Artificial Intelligence (AI) to e-commerce platforms is becoming popular to attract customers to purchase products and services (Wang et al., 2023). To contribute to this explanation, research was conducted to explain the factors that influence AI-powered online purchase intentions. Through PLS-SEM analysis results from 366 questionnaires, collected from people who have made online purchases in Ho Chi Minh City, show that perceived usefulness and perceived customization affect attitudes towards AI and online purchasing intention. The study also found that the moderating effect of perceived risk did not influence the relationships between perceived usefulness, perceived customization, and online purchase intention. This shows that in the context of AI-powered online purchases, perceived usefulness and perceived customization are still the most important factors influencing consumers' purchase intentions, even when consumers feel risky when purchasing online. Results from the research help add to the theoretical foundation of customer purchasing behavior and suggest management implications for businesses in the field of e-commerce.
Article Details
Keywords
Attitude toward AI, Online purchase intention, Perceived customization, Perceived usefullness, TAM
References
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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
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Ho, S. Y., & Bodoff, D. (2014). The effects of Web personalization on user attitude and behavior. MIS Quarterly, 38(2), 497-A507.
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Isaac, R. A., Chaturvedi, P., Gareja, P., & Grover, R. (2018). Secured E-Banking System using Artificial Intelligence. International Journal of Emerging Technologies in Engineering Research, 6(10).
Jain, S., & Gandhi, A. V. (2021). Impact of artificial intelligence on impulse buying behaviour of Indian shoppers in fashion retail outlets. International Journal of Innovation Science, 13(2), 193–204. https://doi.org/10.1108/ijis-10-2020-0181
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Kim, J., & Forsythe, S. (2007). Hedonic usage of product virtualization technologies in online apparel shopping. International Journal of Retail & Distribution Management, 35(6), 502–514. https://doi.org/10.1108/09590550710750368
Kim, J., & Gambino, A. (2016). Do we trust the crowd or information system? Effects of personalization and bandwagon cues on users’ attitudes and behavioral intentions toward a restaurant recommendation website. Computers in Human Behavior, 65, 369–379. https://doi.org/10.1016/j.chb.2016.08.038
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Lee, H.-H., & Moon, H. (2015). Perceived Risk of Online Apparel Mass Customization. Clothing and Textiles Research Journal, 33(2), 115–128. https://doi.org/10.1177/0887302x15569345
Lee, K. S., & Tan, S. J. (2003). E-retailing versus physical retailing: A theoretical model and empirical test of consumer choice. Journal of Business Research, 56(11), 877-885. https://doi.org/10.1016/S0148-2963(01)00274-0
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