Purchase Behavior in AI-Enabled Livestream Commerce: Evidence from an Emerging Economy
Downloads
This study aims to examine consumer purchase behavior in AI-enabled livestream commerce as an emerging form of AI-driven digital commerce. Specifically, the research investigates how technological and social–psychological factors influence perceived ease of use, perceived usefulness, intention to use, and purchase behavior. Data were collected through an online survey using convenience and snowball sampling methods, resulting in 248 valid responses. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the proposed relationships. The findings indicate that compatibility and self-satisfaction positively influence both perceived ease of use and perceived usefulness, while perceived risk negatively affects these perceptions. Social influence significantly enhances perceived usefulness but does not have a significant effect on perceived ease of use. In addition, perceived ease of use and perceived usefulness significantly strengthen intention to use, which subsequently drives purchase behavior. This study contributes to the literature by extending the TAM–UTAUT framework within the context of AI-enabled livestream commerce and by offering new insights into how AI streamers reshape consumer–platform interactions. These findings provide both theoretical contributions and practical implications for AI-driven commerce in emerging markets.
Downloads
[1] Cunha, M. N., & Krupskyi, O. P. (2025). Transforming Online Retail: The Impact of Augmented and Virtual Reality on Consumer Engagement and Experience in E-Commerce. Current Progress in Arts and Social Studies Research, 10, 95–112. doi:10.9734/bpi/cpassr/v10/4425.
[2] Wang, L., Yeap, J. A. L., Liu, J., & Li, Z. (2026). From Avatars to Algorithms: Virtual Streamers and AI-Enabled Consumer Behavior in Live Streaming Commerce—A Systematic Review. Journal of Theoretical and Applied Electronic Commerce Research, 21(2), 57. doi:10.3390/jtaer21020057.
[3] Liu, M., Chen, X., Yang, B., & Gao, Y. (2025). The role of social presence in impulsive buying during live streaming E-commerce: exploring the mechanisms of customer inspiration and positive emotion. BMC psychology, 13(1), 1414. doi:10.1186/s40359-025-03743-4.
[4] Yuan, D. H., Deng, R., & Lin, Z. (2026). When the thrill lingers: how post-purchase flow consciousness shapes live-stream shopping engagement. European Journal of Marketing, 1-21. doi:10.1108/EJM-09-2024-0783/1336798.
[5] Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., Kumar, V., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59. doi:10.1016/j.ijinfomgt.2020.102168.
[6] Liu, Q., Ma, N., & Zhang, X. (2025). Can AI-virtual anchors replace human internet celebrities for live streaming sales of products? An emotion theory perspective. Journal of Retailing and Consumer Services, 82, 104107. doi:10.1016/j.jretconser.2024.104107.
[7] Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543–556. doi:10.1016/j.jbusres.2018.08.032.
[8] Wang, Z. (2025). The influence of AI on consumer behavior: Shaping choices and preferences in the digital marketplace. Systems and Soft Computing, 200397. doi:10.1016/j.sasc.2025.200397.
[9] Liang, T. P., Ho, Y. T., Li, Y. W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69–90. doi:10.2753/JEC1086-4415160204.
[10] Hajli, N., Sims, J., Zadeh, A. H., & Richard, M. O. (2017). A social commerce investigation of the role of trust in a social networking site on purchase intentions. Journal of Business Research, 71, 133–141. doi:10.1016/j.jbusres.2016.10.004.
[11] Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and tam in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. doi:10.2307/30036519.
[12] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. doi:10.2307/30036540.
[13] Venkatesh, V., Thong, J. Y. L., & 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. doi:10.2307/41410412.
[14] Ni, S., & Ueichi, H. (2024). Factors influencing behavioral intentions in livestream shopping: A cross-cultural study. Journal of Retailing and Consumer Services, 76, 103596. doi:10.1016/j.jretconser.2023.103596.
[15] Sun, L., & Tang, Y. (2024). Avatar effect of AI-enabled virtual streamers on consumer purchase intention in e-commerce livestreaming. Journal of Consumer Behaviour, 23(6), 2999–3010. doi:10.1002/cb.2389.
[16] Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. doi:10.1007/s11747-019-00696-0.
[17] Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. doi:10.1080/10864415.2003.11044275.
[18] Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37. doi:10.1016/j.elerap.2019.100886.
[19] Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing. Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 91(2), 174–181. doi:10.1016/j.jretai.2015.02.005.
[20] Yun, J., Lee, D., Cottingham, M., & Hyun, H. (2023). New generation commerce: The rise of live commerce (L-commerce). Journal of Retailing and Consumer Services, 74. doi:10.1016/j.jretconser.2023.103394.
[21] Seo, H., & Kim, S. Y. (2022). The Effect of Non-face-to-face Collaboration System Quality on Business Performance. 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science (BCD), 70–75. doi:10.1109/BCD54882.2022.9900701.
[22] Ruangkanjanases, A., & Hariguna, T. (2025). Investigating the Correlation between Bitcoin Trading Volume and Technical Indicators Using Data Mining Techniques. HighTech and Innovation Journal, 6(4), 1390–1400. doi:10.28991/HIJ-2025-06-04-015.
[23] Moloi, T., & Marwala, T. (2020). Artificial Intelligence in Economics and Finance Theories. Springer Nature, Cham, Switzerland. doi:10.1007/978-3-030-42962-1.
[24] Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, London, United Kingdom.
[25] Bickley, S. J., Chan, H. F., & Torgler, B. (2022). Artificial intelligence in the field of economics. Scientometrics, 127(4), 2055-2084. doi:10.1007/s11192-022-04294-w.
[26] Choi, P. M. S., & Huang, S. H. Fintech with Artificial Intelligence, Big Data, and Blockchain. Springer Nature, Cham, Switzerland. doi:10.1007/978-981-33-6137-9.
[27] Voskoglou, M. G., & Salem, A. B. M. (2020). Benefits and limitations of the artificial with respect to the traditional learning of mathematics. Mathematics, 8(4), 611. doi:10.3390/math8040611.
[28] Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114–141. doi:10.1080/14719037.2022.2048685.
[29] G. Harkut, D. (Ed.). (2019). Artificial Intelligence - Scope and Limitations. IntechOpen, London, United Kingdom. doi:10.5772/intechopen.77611.
[30] Madanchian, M., & Taherdoost, H. (2025). Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors. Information (Switzerland), 16(1), 51. doi:10.3390/info16010051.
[31] Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1–2), 641–652. doi:10.1007/s10479-020-03918-9.
[32] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. doi:10.2307/249008.
[33] Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926.
[34] Silva, P. (2015). Davis' technology acceptance model (TAM) (1989). Information seeking behavior and technology adoption: Theories and trends, 205-219, IGI Global Scientific Publishing, Hershey, United States. doi:10.4018/978-1-4666-8156-9.ch013.
[35] Phamthi, V. A., Nagy, Á., & Ngo, T. M. (2024). The influence of perceived risk on purchase intention in e-commerce Systematic review and research agenda. International Journal of Consumer Studies, 48(4), e13067. doi:10.1111/ijcs.13067.
[36] Turki, H. (2025). AI-Powered Personalization in E-Commerce: Governance, Consumer Behavior, and Explanatory Insights from Big Data Analytics. Technology in Society, 103033. doi:10.1016/j.techsoc.2025.103033.
[37] Li, L., Feng, Y., & Zhao, A. (2024). An interaction–immersion model in live streaming commerce: the moderating role of streamer attractiveness. Journal of Marketing Analytics, 12(3), 701-716. doi:10.1057/s41270-023-00225-7.
[38] Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. doi:10.1177/1094670517752459.
[39] Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. doi:10.1287/isre.6.2.144.
[40] Maidiana, K., & Hidayat, Z. (2021). Distributing Goods and Information Flow: Factors Influencing Online Purchasing Behavior of Indonesian Consumers. Journal of Distribution Science, 19(7), 5–17. doi:10.15722/jds.19.7.202107.5.
[41] Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies, 59(4), 451–474. doi:10.1016/S1071-5819(03)00111-3.
[42] Zhang, Q., Wang, Y., & Ariffin, S. K. (2024). Consumers purchase intention in live-streaming e-commerce: A consumption value perspective and the role of streamer popularity. Plos one, 19(2), e0296339. doi:10.1371/journal.pone.0296339.
[43] Sarstedt, M., Ringle, C.M., Hair, J.F. (2022). Partial Least Squares Structural Equation Modeling, Handbook of Market Research. Springer, Cham, Switzerland. doi:10.1007/978-3-319-57413-4_15.
[44] Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. doi:10.1016/j.rmal.2022.100027.
[45] Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. doi:10.1108/EJM-02-2019-0189.
[46] Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. doi:10.1016/j.chb.2010.01.013.
[47] Zhang, K. Z. K., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108. doi:10.1016/j.dss.2016.04.001.
[48] Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254. doi:10.17705/1jais.00122.
- This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
