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Exploring AI Adoption and SME Performance in Resource-Constrained Environments: A TOE–RBV Perspective with Mediation and Moderation Effects

Faizan ul Haq et al.

Journal of Digital Economy2025https://doi.org/10.1016/j.jdec.2025.07.002article
ABDC B
Weight
0.53

Abstract

This study examines the adoption of Artificial Intelligence (AI) and its impact on the performance of Small and Medium Enterprises (SMEs) in resource-constrained environments, with a focus on Pakistan’s manufacturing sector. Grounded in the Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV), the study investigates how technological readiness, leadership vision, and competitive pressure influence AI adoption, and how AI adoption mediates their effects on firm performance. While SMEs in developed economies benefit from advanced infrastructure and institutional support, firms in emerging markets face significant challenges such as skill shortages, financial constraints, and infrastructural gaps. Data were collected from 349 managerial-level respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multiple Regression Analysis (MRA). The results show that AI adoption significantly mediates the relationship between key organizational and environmental factors and SME performance. Additionally, AI-driven marketing strategies moderate the link between AI adoption and firm performance, enhancing the benefits of digital transformation. The study contributes to the growing literature on digital innovation in emerging economies and offers practical implications for SME leaders and policymakers aiming to foster performance through strategic AI integration.

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https://doi.org/https://doi.org/10.1016/j.jdec.2025.07.002

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@article{faizan2025,
  title        = {{Exploring AI Adoption and SME Performance in Resource-Constrained Environments: A TOE–RBV Perspective with Mediation and Moderation Effects}},
  author       = {Faizan ul Haq et al.},
  journal      = {Journal of Digital Economy},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jdec.2025.07.002},
}

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Exploring AI Adoption and SME Performance in Resource-Constrained Environments: A TOE–RBV Perspective with Mediation and Moderation Effects

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Evidence weight

0.53

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.70 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.