AI-Enhanced TOE Framework for Sustainable Industrial Performance in Fragile and Transforming Economies: Evidence from Yemen and Saudi Arabia

Using an integrated framework rooted in the TOE model enhanced with AI, this study looks at ways to improve industrial performance and environmental sustainability in fragile and rapidly transforming contexts such as those found in Yemen and Saudi Arabia. Data for the research are field-based and were obtained from a total of 600 SMEs operating in both countries. Based on the questionnaires’ responses by 294 managers, results from the partial least squares structural equation modeling (PLS-SEM) have indicated significant positive effects of AI-TOE on environmental performance (beta = 0.487) and manufacturing performance (beta = 0.759). Results indicate that AI acts as a transformative force, though its impact differs based on the maturity of infrastructure and organizational readiness. The Saudi SMEs gain from their institutional support and advanced technologies, while those in Yemen are dependent on the low-cost adoption of AI and organizational flexibility to accept structural challenges. PLS-SEM analysis of the study showed that integrating AI into the TOE dimensions accelerates operational efficiency in order to support environmental performance. Industrial performance was found to be a very important mediator in this relationship. This study responds to the call for digital transformation literature by providing an actionable framework of AI adoption in resource-constrained environments. These findings offer insights that might guide policymakers and organizations toward more resilient and sustainable operational strategies. These findings provide valuable guidance for engineering managers within the context of negotiating digital transformation and sustainability trade-offs in fragile and resource-constrained contexts.

💡 Research Summary

This study investigates the impact of Artificial Intelligence (AI) on the industrial and environmental performance of Small and Medium-sized Enterprises (SMEs) operating in fragile and rapidly transforming economic contexts. Using an integrated theoretical framework that enhances the classic Technology-Organization-Environment (TOE) model with AI capabilities—termed the AI-enhanced TOE framework—the research provides a comparative analysis of SMEs in Yemen (representing a fragile state) and Saudi Arabia (representing a fast-transforming economy).

The research methodology involved a survey of 600 SMEs across both countries, with valid responses collected from 294 managers. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The key empirical findings reveal that the AI-enhanced TOE framework has a significant positive effect on both environmental performance (β = 0.487) and manufacturing performance (β = 0.759). Furthermore, manufacturing performance was identified as a crucial mediator in the relationship between AI-TOE and environmental performance, suggesting that operational improvements driven by AI subsequently enable better environmental outcomes.

A central contribution of the paper is the demonstration of context-dependent AI adoption pathways. In Saudi Arabia, characterized by advanced digital infrastructure and strong institutional support (e.g., Vision 2030), SMEs successfully leverage sophisticated AI solutions like predictive maintenance and cloud-based analytics to enhance efficiency. Conversely, in Yemen, which faces institutional collapse and infrastructural deficits, SMEs rely on low-cost, simple AI tools (e.g., chatbots, inventory forecasting) and organizational flexibility to pursue “survival-oriented innovation.” This highlights that the transformative power of AI is not uniform but is shaped by the maturity of local infrastructure and organizational readiness.

Theoretically, the study extends the traditional TOE model by introducing the “Fragile Economy Technology Adoption (FETA)” framework, which incorporates dimensions like survival-oriented innovation and institutional adaptation. It also proposes the concept of “institutional leapfrogging,” where AI enables organizations in transforming economies to adapt to policy changes faster than traditional institutional mechanisms. The study bridges a gap in the digital transformation literature, which has predominantly focused on stable economies, by offering an actionable framework for AI adoption in resource-constrained and institutionally challenging environments. The findings provide valuable guidance for policymakers and engineering managers seeking to navigate the trade-offs between digital transformation and sustainability in fragile contexts, promoting strategies for resilient and sustainable industrial operations.