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

TL;DR

This study develops an AI-enhanced TOE framework to improve industrial and environmental performance in fragile economies like Yemen and Saudi Arabia, highlighting the role of AI in accelerating sustainability and operational efficiency.

Contribution

It introduces an integrated AI-TOE framework tailored for resource-constrained environments, validated with field data from SMEs in Yemen and Saudi Arabia.

Findings

AI significantly improves environmental and manufacturing performance.

Infrastructure maturity influences AI's impact on organizations.

AI integration accelerates operational efficiency and sustainability.

Abstract

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.