Aug 2026· Management· pp. 822-843· 0 citations· 44 references
TL;DR
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 study aims to support small and medium enterprises (SMEs) in making informed e-commerce adoption decisions by identifying key business constructs and developing a machine learning–based decision support tool.
A framework grounded in the technology–organization–environment (TOE) framework and perceived strategic value (PSV) principle is proposed. An extended multi-objective micro genetic algorithm (MmGA) is employed to identify influential business constructs, including perceived benefits, perceived obstacles, competitive pressure, government support, operational support, firm size and business models. Classification models are trained using survey data collected from manufacturing SMEs.
The proposed MmGA-based model provides e-commerce adoption predictions with useful insights into technological readiness, organizational capability and managerial intent affecting adoption decisions.
The study relies on self-reported survey data, which may introduce recall and social desirability bias and limits generalizability across regions and time. In addition, the scope focuses on e-commerce pre-adoption stages. Future work can extend the proposed multi-objective model to post-adoption factors such as scalability and customization.
The developed decision support tool assists SME managers and policymakers in evaluating adoption readiness and prioritizing strategic factors before entering e-commerce activities.
While the TOE and PSV frameworks are well-established individually, the primary novelty of this study lies in their integration into a multi-objective machine learning paradigm. Unlike traditional regression models that analyze constructs in isolation, our approach treats e-commerce adoption as a multi-objective optimization problem (MOP). This 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-makers (PSV). The extended MmGA model serves as a useful prediction tool with a parsimonious and actionable framework for SME managers in decision support of e-commerce adoption.
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
Small and medium-sized enterprises (SMEs) lag in adopting artificial intelligence (AI) for marketing management, a gap reflected in the scarce literature on AI adoption within marketing contexts. To examine the impacts of AI implementation on firm performance, this study identifies the key drivers of AI adoption and usage in marketing activities. Using a quantitative approach, we collected data from 216 SMEs and tested a structural equation model (SEM) via bootstrapping to evaluate twelve hypotheses linking AI-enabled marketing activities, performance expectancy, co-worker support, management encouragement, and user attitude to AI-driven business performance. Results support six hypotheses. Notably, senior management encouragement is critical—both directly and indirectly—for AI adoption in marketing. Furthermore, integrating AI into marketing activities directly enhances business performance, while co-worker support positively influences performance expectations and actual usage. Conversely, user attitude showed no significant effect in this context. We conclude that initiatives promoting AI adoption must clearly communicate its concrete benefits. Moreover, future research should explore 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
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