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REVIEW ARTICLE
AI adoption in small and medium-sized
e-commerce enterprise: current state
and future research agenda
1
Management, Symbiosis Institute of Business Management, Pune [SIBM- Pune], India
Submission date: 2026-03-27
Final revision date: 2026-05-26
Acceptance date: 2026-06-12
Online publication date: 2026-08-11
Publication date: 2026-08-11
Corresponding author
Prashant Bari
Management, Symbiosis Institute of Business Management, Pune [SIBM- Pune], Pune, India
Management, Symbiosis Institute of Business Management, Pune [SIBM- Pune], Pune, India
Management 2026;(1):822-843
KEYWORDS
JEL CLASSIFICATION CODES
M15; O33; L25
TOPICS
ABSTRACT
Research background and purpose:
Artificial intelligence (AI) is being increasingly used in digital commerce. It helps small and medium-sized enterprises (SMEs) improve efficiency, enhance customer engagement, and support data-based decisions. However, research on AI adoption in SME e-commerce is still in emerging stage. This study aims to synthesize existing work and identify key applications, drivers, and constraints.
Design/methodology/approach:
The study uses a systematic literature review (SLR) based on the PRISMA framework. Relevant studies were identified, screened, verified, and included from major databases using PRISMA. A total of 92 peer-reviewed articles were selected and analysed using thematic analysis.
Findings:
The findings show that SMEs have yet leveraged very limited capabilities of AI for business operations. Common applications include chatbots, recommendation systems, predictive analytics, and AI-enabled CRM tools. Key contributor to adoption is technological, organizational, and environmental factors. While AI offers benefits such as cost efficiency and improved decision-making, challenges such as limited infrastructure, skill gaps, and governance concerns persist.
Value added and limitations:
The study aims integrating diverse literature and presents in unified format in context of AI adoption in case of SMEs in e-Commerce. Key theories such as TOE, Dynamic Capabilities, Diffusion of Innovation, and Resource Dependency Theory and their interrelation is discussed. Finally, research gap and scope for Future research is presented.
Artificial intelligence (AI) is being increasingly used in digital commerce. It helps small and medium-sized enterprises (SMEs) improve efficiency, enhance customer engagement, and support data-based decisions. However, research on AI adoption in SME e-commerce is still in emerging stage. This study aims to synthesize existing work and identify key applications, drivers, and constraints.
Design/methodology/approach:
The study uses a systematic literature review (SLR) based on the PRISMA framework. Relevant studies were identified, screened, verified, and included from major databases using PRISMA. A total of 92 peer-reviewed articles were selected and analysed using thematic analysis.
Findings:
The findings show that SMEs have yet leveraged very limited capabilities of AI for business operations. Common applications include chatbots, recommendation systems, predictive analytics, and AI-enabled CRM tools. Key contributor to adoption is technological, organizational, and environmental factors. While AI offers benefits such as cost efficiency and improved decision-making, challenges such as limited infrastructure, skill gaps, and governance concerns persist.
Value added and limitations:
The study aims integrating diverse literature and presents in unified format in context of AI adoption in case of SMEs in e-Commerce. Key theories such as TOE, Dynamic Capabilities, Diffusion of Innovation, and Resource Dependency Theory and their interrelation is discussed. Finally, research gap and scope for Future research is presented.
REFERENCES (43)
1.
Akpan, I. J., Soopramanien, D., & Kwak, D. H. (2021). Cutting-edge technologies for small business and innovation in the era of COVID-19 global health pandemic. Journal of Small Business & Entrepreneurship, 33(6), 607-617.
2.
Arora, A., Kaur, R., Vats, P., Gupta, M., Chopra, G., Mehmi, S., & Khanna, T. (2023, August). A study on optimizing the personalization of recommendations and customer services using artificially intelligent neural networks to improve electronic commerce. In International Conference on WorldS4 (pp. 1–9). Springer Nature Singapore.
3.
Arroyabe, M. F., Arranz, C. F., de Arroyabe, I. F., & de Arroyabe, J. C. F. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, 102733.
4.
Chauhan, J., Prathibha, M. D., Singh, P., Choyal, P., Mishra, U. N., Saha, D., ... & Singhal, R. K. (2023). Plant photosynthesis under abiotic stresses: Damages, adaptive, and signaling mechanisms. Plant Stress, 10, 100296. https://doi.org/10.1016/j.stre....
5.
Cooper, R. G., & Brem, A. M. (2024). The adoption of AI in new product development: Results of a multi-firm study in the US and Europe. Research-Technology Management, 67(3), 44–53.
6.
Faiz, F., Le, V., & Masli, E. K. (2024). Determinants of digital technology adoption in innovative SMEs. Journal of Innovation & Knowledge, 9(4), 100610.
7.
Gallastegui, L. M. G., & Forradellas, R. R. (2024). Optimization of the educational experience in higher education using predictive artificial intelligence models. Revista de Gestão Social e Ambiental, 18(5), e07111.
8.
Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107.
9.
Han, E., Yin, D., & Zhang, H. (2023). Bots with feelings: Should AI agents express positive emotion in customer service? Information Systems Research, 34(3), 1296–1311.
10.
Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362–372. https://doi.org/10.1016/j.jmsy....
11.
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.
12.
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of predictors in IT innovation adoption research. Journal of Information Technology, 21(1), 1–23.
13.
Joshi, S., Bhattacharya, S., Pathak, P., Natraj, N. A., Saini, J., & Goswami, S. (2025). Generative AI in digital marketing using behavioral reasoning theory. International Journal of Information Management Data Insights, 5(1), 100317.
14.
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5–20.
15.
Kitchenham, B., Budgen, D., Brereton, P., Turner, M., Charters, S., & Linkman, S. (2007). Software engineering research methods. IET Software, 1(5), 161–171.
16.
Knayer, T., & Kryvinska, N. (2023). Smart meter adoption in Germany. Frontiers in Energy Research, 11, 1223608.
17.
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783.
18.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.
19.
Oliveira, T., & Martins, M. F. (2011). IT adoption models at firm level. The Electronic Journal Information Systems Evaluation, 14(1), 110–121.
20.
Ozay, D., Jahanbakht, M., Shoomal, A., & Wang, S. (2024). Artificial intelligence (AI)-based customer relationship management (CRM): A comprehensive bibliometric and systematic literature review with outlook on future research. Enterprise Information Systems, 18(7), 2351869.
21.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n7....
22.
Papadopoulos, T., Baltas, K. N., & Balta, M. E. (2020). Digital technologies in SMEs during COVID-19. International Journal of Information Management, 55, 102192.
23.
Pfeffer, J., & Salancik, G. (2015). External control of organizations: A resource dependence perspective. Routledge.
24.
Pelletier, C., & Cloutier, L. M. (2019). Challenges of digital transformation in SMEs: Exploration of IT-related perceptions in a service ecosystem. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 4967–4976). University of Hawaiʻi at Mānoa.
25.
Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2021). The cultural benefits of artificial intelligence in the enterprise. MIT Sloan Management Review & Boston Consulting Group. https://sloanreview.mit.edu/pr....
26.
Rialti, R., Marzi, G., Ciappei, C., & Busso, D. (2019). Big data and dynamic capabilities: A bibliometric analysis and systematic literature review. Management Decision, 57(8), 2052–2068.
27.
Ritala, P., Aaltonen, P., Ruokonen, M., & Nemeh, A. (2024). Developing industrial AI capabilities: An organisational learning perspective. Technovation, 138, 103120.
30.
Shemi, A. P., & Procter, C. (2018). E-commerce and entrepreneurship in SMEs: Case of myBot. International Journal of Entrepreneurship and Small Business, 34(1), 117–140.
31.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
32.
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of enterprise performance. Strategic Management Journal, 28(13), 1319–1350.
33.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
34.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
35.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge. British Journal of Management, 14(3), 207–222.
37.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of TAM: Four longitudinal field studies. Management Science, 46(2), 186–204.
38.
Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Impact of artificial intelligence on firm performance: Exploring the mediating effect of process-oriented dynamic capabilities. In R. Agrifoglio, R. Lamboglia, D. Mancini, & F. Scornavacca (Eds.), Digital business transformation (pp. 3–18). Springer. https://doi.org/10.1007/978-3-....
39.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
40.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. (2017). Big data analytics and firm performance. Journal of Business Research, 70, 356–365.
41.
Wei, R., & Pardo, C. (2022). Artificial intelligence and SMEs: How can B2B SMEs leverage AI platforms? Industrial Marketing Management, 107, 466–483.
42.
Zahoor, N., Al-Tabbaa, O., & Khan, Z. (2023). Entrepreneurial learning and AI adoption in emerging markets. International Small Business Journal, 41(1), 3–29.
43.
Zirar, A., Ali, I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 02747.
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| ISSN: | 1429-9321 (1997-2019) |
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