Generative Artificial Intelligence Adoption in Emerging Economies: A Technology-Organization-Environment Framework Analysis of Large Language Model Integration in Small and Medium Enterprises

Abstract

The proliferation of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has precipitated substantial transformations in organizational practices across developed economies. However, a conspicuous lacuna persists in scholarly understanding concerning the adoption dynamics within emerging market contexts where resource constraints and infrastructural limitations present distinctive challenges. This study investigates the determinants and outcomes of GenAI adoption among Small and Medium Enterprises (SMEs) across BRICS+ nations, employing the Technology-Organization-Environment (TOE) framework as theoretical foundation. Through a mixed-methods sequential explanatory design, we collected quantitative data from 487 manufacturing and service sector SMEs during March-August 2025 and supplemented findings with 32 semi-structured interviews with senior managers. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis reveals that technological readiness (β = 0.412, p < .001), leadership support (β = 0.378, p < .001) and competitive pressure (β = 0.289, p < .01) significantly influence GenAI adoption. Furthermore, findings indicate that GenAI adoption mediates the relationship between organizational factors and firm performance, with AI-driven marketing strategies moderating this relationship. The study contributes to digital transformation literature by extending TOE framework applicability to GenAI contexts in resource-constrained settings and offers practical implications for policymakers seeking to foster inclusive AI ecosystems.

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