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.
Cotton (Gossypium hirsutum L.) is a major fibre crop underpinning the global textile industry; however, its productivity is increasingly threatened by climatic variability and the resurgence of sap-sucking insect pests. The interaction between genotype and environment (G × E) further complicates the identification of stable and high-yielding genotypes, particularly under rainfed conditions. The present study evaluated 7 cotton genotypes across 9 environments (three locations over 3 consecutive years: 2021–24) in Odisha, India, to assess G × E interaction for 14 quantitative traits related to yield and sucking pest resistance using advanced statistical approaches. Combined analysis of variance revealedhighly significant (p < 0.01) effects of genotypes, environments and G × E interaction for all traits studied. The interaction component was particularly significant for key traits, including seed cotton yield (SCY), lint yield (LY) and populations of major sucking pests, necessitating a comprehensive stability analysis. High broad-sense heritability coupled with moderate to high genetic advance for yield and pest resistance traits indicated the predominance of additive gene action. Stability analyses using the Eberhart and Russell model, additive main effects and multiplicative interaction (AMMI) and genotype plus genotype by environment (GGE) biplot consistently identified genotype BS 3-17 as superior, exhibiting the highest mean SCY (1978 kg ha-1) and LY (675 kg ha-1), along with the lowest mean populations of aphids (APH) (5.28 per 3 leaves) and jassids (JAS) (1.82 per 3 leaves) across environments. The multi-trait stability index (MTSI) further ranked BS 3-17 as the most desirable genotype (MTSI score = 5.51), indicating its closest proximity to the ideotype. Agronomic validation trials demonstrated that high-density planting (90 × 30 cm) combined with 125 % of the recommended dose of fertilisers significantly enhanced the yield potential of BS 3-17, achieving up to 3114 kg ha-1. These findings establish BS 3-17 as a climate-resilient, high-yielding genotype suitable for commercial cultivation and a promising donor parent for breeding programs targeting yield stability and sucking pest resistance.
D. Subhashree, S. N. Bhabani, R. Jyoti et al.· Plant Science Today· 0 citations