The findings show that SMEs have yet leveraged very limited capabilities of AI for business operations, and challenges such as limited infrastructure, skill gaps, and governance concerns persist.
Abstract
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.
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.
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.
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.
These findings highlight the necessity of customized implementation strategies, focused capacity-building initiatives, encouraging legislative frameworks, and multi-stakeholder cooperation to promote inclusive, responsible AI integration in SMEs.
E. S. Hamid, Bhenu Artha· Archives of Business Researc...· 1 citation
This approach treats e-commerce adoption as a multi-objective optimization problem (MOP) that allows for the simultaneous optimization of predictive performance and model parsimony, aiming to undertake the non-linear trade-offs between objective organizational readiness (TOE) and subjective strategic intent of decision...
Seng-Chee Lim, Mohammed Falah Mohammed, Choo Jun Tan et al.· International Journal of Int...· 0 citations
This study investigates the adoption of BI practices in small and medium-sized enterprises (SMEs) to examine the extent of BI integration and its impact on decision-making, competitiveness, and the critical success factors influencing effective BI implementation.
Mirano Jansen, K. Ohei, Sam Lubbe· Veredas do Direito· 0 citations
It is demonstrated that AI adoption outcomes are contingent upon complementary organizational capabilities, knowledge management infrastructure, human capital quality, and institutional context rather than technology deployment alone.
Ridha Rayan Furqan, W. Adawiyah, Ali Şahin et al.· The International Conference...· 0 citations
It is concluded that initiatives promoting AI adoption must clearly communicate its concrete benefits and how managers can effectively foster adoption while addressing employee insecurities regarding AI technology.
Y. Cancino-Gómez, Lugo Manuel Barbosa-Guerrero, Jairo Jamith Palacios-Rozo· Revista Venezolana de Gerenc...· 0 citations
It is asserted that policymakers and SME managers need to prioritise training, infrastructure and digital readiness to ease the path for AI adoption and the literature affirms that AI adoption increases the marketing performance of SMEs.
Salma El-Gohary, M. B. Ben Mimoun, Hatem El-Gohary· Journal of Cultural Analysis...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.