Adoption of Artificial Intelligence (AI) in Small and Medium Enterprise E-Commerce to Improve Marketing Performance
DOI:
https://doi.org/10.37034/jems.v8i1.255Keywords:
AI Adoption, SMEs, E-commerce, Marketing Performance, TOE–TAMAbstract
This study investigates the key determinants influencing the adoption of Artificial Intelligence (AI) by Small and Medium Enterprises (SMEs) in the e-commerce sector and examines its impact on marketing performance. The research integrates the Technology–Organization–Environment (TOE) framework and the Technology Acceptance Model (TAM) to explain the relationships among technological readiness, organizational support, environmental pressure, perceived usefulness (PU), perceived ease of use (PEOU), and AI adoption level. A quantitative approach employing Partial Least Squares–Structural Equation Modeling (PLS-SEM) was conducted on 155 e-commerce SMEs operating in Jakarta, Indonesia. Empirical results reveal that technology, organization, and environment significantly affect perceived usefulness, which in turn strongly drives AI adoption. Perceived usefulness is identified as the most powerful determinant of adoption, whereas perceived ease of use exerts an indirect influence via usefulness perception. Furthermore, AI adoption has a positive and significant effect on marketing performance, particularly in enhancing digital campaign effectiveness, product innovation, and customer loyalty. The findings emphasize that internal readiness and external competitive pressures jointly foster AI-driven digital transformation within SMEs. The study provides both theoretical validation of the TOE–TAM integration and practical guidance for policymakers and business owners to design effective, ethical, and sustainable AI adoption strategies.
References
Baruah, A., & Chatterjee, D. (2024). Cognitive‐Affective‐Behavioral Themes in Post‐Purchase Attitudes Toward Virtual Tourism Experiences: A Mixed‐Method Approach. International Journal of Tourism Research, 26(6). https://doi.org/10.1002/jtr.2811
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Basri, W. (2020). Examining the impact of artificial intelligence (Ai)-assisted social media marketing on the performance of small and medium enterprises: Toward effective business management in the saudi arabian context. International Journal of Computational Intelligence Systems, 13(1), 142–152. https://doi.org/10.2991/ijcis.d.200127.002
Huang, Ming-Hui, & Rust, Roland T. (2020). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
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
Sharma, C., Kushwah, S. S., & Singh, P. (2025). Metaverse in marketing research: an overview and future research directions. Progress in Artificial Intelligence, 1-36. https://doi.org/10.1007/s13748-025-00375-y
Aduda, J., & Obondy, S. (2020). Credit Risk Management and Efficiency of Savings and Credit Cooperative Societies: A Review of Literature. Journal of Applied Finance & Banking, 11(1), 99–120. https://doi.org/10.47260/jafb/1117
Salju, S., Junaidi, J., & Goso, G. (2023). The effect of digitalization, work-family conflict, and organizational factors on employee performance during the COVID-19 pandemic. Problems and Perspectives in Management, 21(1), 107–119. https://doi.org/10.21511/ppm.21(1).2023.10
Borissov, D. (2024). Enterprises as Complex Systems: Navigating Challenges and Embracing Resilience. Business Ethics and Leadership, 8(4), 95–122. https://doi.org/10.61093/bel.8(4).95-122.2024
Zheng, B., Yuan, Y., Li, H., & Jiang, Y. (2023). A study of digital transformation and MSMEs performance from a spatial perspective: Evidence from China. Journal of Economics and Management (Poland), 45(1), 319–343. https://doi.org/10.22367/jem.2023.45.13
Rosita, J., Ihalauw, J. J. O. I., Abdi, A. S., & Sirine, H. (2023). The Effect of Entrepreneurial Orientation and Social Media Adoption on Marketing Performance of Culinary Start-up Business. Journal of System and Management Sciences, 13(3), 29–51. https://doi.org/10.33168/JSMS.2023.0303
Laser, J. (2021). The best equilibrium in organizational flexibility-stability continuums. International Journal of Organizational Analysis, 29(1), 172–193. https://doi.org/10.1108/IJOA-09-2019-1875
Teng, X., Wu, Z., & Yang, F. (2022). Research on the Relationship between Digital Transformation and Performance of SMEs. Sustainability (Switzerland), 14(10), 1–17. https://doi.org/10.3390/su14106012
Endeshaw, B. (2021). Healthcare service quality-measurement models: a review. Journal of Health Research, 35(2), 106–117. https://doi.org/10.1108/JHR-07-2019-0152
Hillen, J., & Fedoseeva, S. (2021). E-commerce and the end of price rigidity? Journal of Business Research, 125, 63–73. https://doi.org/https://doi.org/10.1016/j.jbusres.2020.11.052
Baker, J. (2012). The Technology–Organization–Environment Framework BT - Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1 (Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger, eds.). New York, NY: Springer New York. https://doi.org/10.1007/978-1-4419-6108-2_12
Kasalak, G., Özcan, M., & Dağyar, M. (2019). Relationship between pre-service teachers’ university image perceptions and student loyalty: A structural equation model. Universal Journal of Educational Research, 7(2), 480–489. https://doi.org/10.13189/ujer.2019.070221
Kevin, Z., Kraemer, K., & Xu, S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52, 1557–1576. https://doi.org/10.1287/mnsc.1050.0487
Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53, 102118. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2020.102118
Sözer, E. G. (2020). Relationship Marketing and Customer Based Brand Tolerance (CBBT): An Integrative Approach. International Journal of Economics and Management Studies, 7(1), 125–137. https://doi.org/10.14445/23939125/ijems-v7i1p116
Nuryakin, N., & Maryati, T. (2022). Do green innovation and green competitive advantage mediate the effect of green marketing orientation on SMEs’ green marketing performance? Cogent Business and Management, 9(1). https://doi.org/10.1080/23311975.2022.2065948
Jang, H., Song, J., & Kim, R. (2014). Does the offline bully-victimization influence cyberbullying behavior among youths? Application of General Strain Theory. Computers in Human Behavior, 31, 85–93. https://doi.org/https://doi.org/10.1016/j.chb.2013.10.007
Legimai, N. M. C., Saldanha, E. S., & Graciana, B. (2022). The Mediation Effect of Business Strategy on the Relations between External Factors, Internal Factors and Business Performance. Timor Leste Journal of Business and Management, 4, 34–47. https://doi.org/10.51703/bm.v4i0.66
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Mediation Analysis. https://doi.org/10.1007/978-3-030-80519-7_7
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM).
Nguyen, N. P., Hang, N. T. T., Hiep, N., & Flynn, O. (2023). Does transformational leadership influence organisational culture and organisational performance: Empirical evidence from an emerging country. IIMB Management Review, 35(4), 382–392. https://doi.org/10.1016/j.iimb.2023.10.001
Gupta, A., & Gupta, P. (2023). Overcoming Obstacles : Small and Medium-Sized Enterprises ( SMEs ) Embracing Digital Marketing in India. International Journal of Innovative Science and Research Technology, 8(6), 3403–3407.
Abbas, J. (2020). Service quality in higher education institutions: qualitative evidence from the students’ perspectives using Maslow hierarchy of needs. International Journal of Quality and Service Sciences, 12(3), 371–384. https://doi.org/10.1108/IJQSS-02-2020-0016
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Copyright (c) 2025 Shinta Rahmani, Ade Permata Surya, Muhammad Rifqi, Sigit Setiawan, Arien Arianti Gunawan

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