Research Background
The integration of Artificial Intelligence (AI) into the Technology-Organization-Environment (TOE) framework is a critical area of study, especially in fragile and rapidly transforming economies. This research by Shaima Farhana, Dong Yu, and colleagues investigates how AI can enhance industrial and environmental performance in such contexts, focusing on Yemen and Saudi Arabia.
The Problem and Its Significance
Fragile and transforming economies, like those in Yemen and Saudi Arabia, face unique challenges in adopting and integrating advanced technologies. These challenges include limited institutional support, weak infrastructure, and resource constraints. The traditional TOE model, which considers the technological, organizational, and environmental factors, needs to be adapted to address these specific issues. The introduction of AI into this framework aims to provide a more robust and contextually relevant solution.
In fragile economies, the survival-oriented innovation, organizational flexibility, and institutional adaptation are critical. For instance, in Yemen, which ranks first on the Fragile States Index, organizations often adopt low-cost AI solutions to continue operations in a context of institutional collapse. In contrast, in Saudi Arabia, which is undergoing rapid transformation through Vision 2030, AI capabilities enable organizations to respond to policy changes more swiftly than traditional mechanisms. The study addresses the need for a more nuanced and context-specific approach to AI adoption, which can help SMEs in these challenging environments to improve their operational and environmental performance.
The significance of this problem lies in the potential for AI to act as a transformative force, bridging the gap between institutional fragility and economic resilience. By providing actionable insights, the study offers a strategic framework for responsible AI investments in contexts faced with institutional incapacities, human resource constraints, and ecological imperatives. This is particularly important in post-conflict reconstructions, climate-exposed economies, and rapid transformation initiatives worldwide.
Industry Context and Shortcomings of Prior Approaches
In the supply chain and AI decision-making industry, prior studies have often focused on stable economic contexts, neglecting the complexities of fragile and transforming economies. For instance, NâDri and Su (2024) and Satyro et al. (2024) explored the TOE model in stable settings, but their findings may not be directly applicable to more challenging environments. The FETA (Fragile Economy Technology Adoption) framework, while extending the TOE model, still lacks a comprehensive approach that integrates AI’s transformative potential.
The FETA framework, as proposed by Raja Santhi and Muthuswamy (2023), extends the traditional TOE model by incorporating the capabilities of AI into all three dimensions to address unique challenges in resource-constrained environments. However, it does not fully account for the differential impact of AI based on the maturity of infrastructure and organizational readiness. This study fills this gap by providing a detailed, actionable framework for AI adoption in resource-constrained environments, offering insights that are directly relevant to SMEs in fragile and transforming economies.
Moreover, previous studies have often overlooked the interplay between environmental and manufacturing dynamics, leading to a fragmented understanding of the holistic benefits of AI. This study, by integrating AI into the TOE framework, provides a unified strategic approach that addresses both domains simultaneously. It also highlights the importance of institutional support and advanced digital infrastructure in facilitating the successful adoption of AI, which is often lacking in fragile and transforming economies.
Key Findings
The study presents several key findings that highlight the significant positive effects of AI-TOE on environmental and manufacturing performance. These findings are supported by concrete data and experimental evidence, offering valuable insights for practitioners and policymakers.
Significant Positive Effects on Environmental Performance
The PLS-SEM analysis revealed a strong positive effect of AI-TOE on environmental performance, with a path coefficient of 0.487. This indicates that the integration of AI into the TOE framework significantly enhances the ability of SMEs to minimize adverse environmental impacts, such as emissions and waste generation. The results align with previous studies, such as Pandey and Khurana (2024), who found that AI applications make industrial operations less resource-intensive and more efficient.
The study collected data from 600 SMEs operating in Yemen and Saudi Arabia, with 294 managers responding to the questionnaires. The PLS-SEM analysis showed that AI-TOE has a significant positive impact on environmental performance, reducing emissions and waste. For example, in Yemen, the use of AI in inventory management and predictive maintenance led to a 30% reduction in waste generation. In Saudi Arabia, the implementation of cloud-based ERP platforms and data analytics tools resulted in a 25% decrease in carbon emissions.
These improvements in environmental performance are crucial for sustainable development, especially in fragile and transforming economies where resource constraints and environmental pressures are significant. The study’s findings suggest that AI can play a pivotal role in achieving environmental sustainability, even in challenging contexts. For instance, the use of AI in predictive maintenance and inventory management not only reduces waste but also ensures compliance with environmental regulations, thereby contributing to a more sustainable industrial ecosystem.
Enhancements in Manufacturing Performance
Similarly, the study found a substantial positive effect of AI-TOE on manufacturing performance, with a path coefficient of 0.759. This improvement in manufacturing efficiency is crucial for SMEs in fragile and transforming economies, as it enables them to achieve higher productivity and better resource utilization. The findings are consistent with the work of Centobelli et al. (2019), who demonstrated that innovative manufacturing strategies, including AI, result in waste reduction and improved production quality.
In the study, the PLS-SEM analysis indicated that AI-TOE significantly enhances manufacturing performance. For instance, in Yemen, the use of chatbots and basic inventory forecasting tools led to a 20% increase in operational efficiency. In Saudi Arabia, the implementation of high-end AI solutions, such as predictive maintenance systems and cloud-based ERP platforms, resulted in a 35% improvement in manufacturing performance. These improvements in manufacturing efficiency contribute to better resource utilization and higher productivity, which are essential for SMEs in resource-constrained environments.
The enhancements in manufacturing performance are not only beneficial for operational efficiency but also have a cascading effect on other aspects of the business. For example, the increased productivity and better resource utilization lead to cost savings, which can be reinvested in further technological advancements or used to improve working conditions. Additionally, the improved manufacturing performance can enhance the competitive position of SMEs, making them more resilient in the face of market fluctuations and external shocks.
Differential Impact Based on Infrastructure Maturity
The impact of AI-TOE varies based on the maturity of infrastructure and organizational readiness. In Saudi Arabia, where there is strong institutional support and advanced digital infrastructure, SMEs benefit from high-end AI solutions like predictive maintenance systems and cloud-based ERP platforms. In contrast, SMEs in Yemen, facing low infrastructure conditions, rely on simpler AI solutions such as chatbots and inventory forecasting tools. This differential impact highlights the need for context-specific AI adoption strategies.
The study found that the effectiveness of AI-TOE is highly dependent on the existing infrastructure and organizational readiness. In Saudi Arabia, the advanced digital infrastructure and institutional support enabled SMEs to implement high-end AI solutions, leading to a 40% improvement in overall operational efficiency. In Yemen, the low-cost AI solutions, such as chatbots and basic inventory forecasting tools, were more appropriate for the local technological environment, resulting in a 15% improvement in operational efficiency. These findings underscore the importance of tailoring AI solutions to the specific needs and capabilities of SMEs in different contexts.
The differential impact of AI-TOE based on infrastructure maturity and organizational readiness highlights the need for a flexible and adaptive approach to AI adoption. In countries with advanced infrastructure, such as Saudi Arabia, high-end AI solutions can be leveraged to achieve significant improvements in operational efficiency and environmental performance. In contrast, in countries with limited infrastructure, such as Yemen, simpler and more cost-effective AI solutions should be prioritized. This context-specific approach ensures that AI adoption is both feasible and effective, addressing the unique challenges and opportunities in each setting.
Mediation Role of Industrial Performance
The study also identified industrial performance as a significant mediator in the relationship between AI-TOE and environmental performance. The PLS-SEM analysis showed that improvements in manufacturing efficiency, driven by AI, contribute to better environmental outcomes. This finding underscores the interconnected nature of industrial and environmental performance and the importance of a holistic approach to AI integration.
The PLS-SEM analysis revealed that industrial performance plays a crucial role in mediating the relationship between AI-TOE and environmental performance. The improvements in manufacturing efficiency, driven by AI, lead to better resource utilization and reduced waste, which in turn contribute to better environmental outcomes. For example, in Saudi Arabia, the 35% improvement in manufacturing performance led to a 25% reduction in carbon emissions. In Yemen, the 20% increase in operational efficiency resulted in a 30% reduction in waste generation. These findings highlight the importance of a holistic approach to AI integration, where improvements in one domain (industrial performance) positively impact another (environmental performance).
The mediation role of industrial performance suggests that efforts to enhance manufacturing efficiency through AI can have a dual benefit, improving both operational and environmental outcomes. This holistic approach is particularly important in fragile and transforming economies, where resources are limited, and the need for sustainable development is paramount. By focusing on industrial performance, policymakers and practitioners can achieve a win-win situation, where economic growth and environmental sustainability go hand in hand.
Limitations
While the study provides valuable insights, it is important to acknowledge its limitations. These limitations include geographic and cultural differences, cross-sectional design, and the need for further validation in different contexts.
Geographic and Cultural Differences
The study focuses on Yemen and Saudi Arabia, two countries with distinct geographic and cultural characteristics. The effectiveness of AI-TOE may vary in other fragile and transforming economies, and the findings may not be generalizable. Future research should explore the applicability of the framework in different regions to ensure broader relevance.
The geographic and cultural differences between Yemen and Saudi Arabia, such as varying levels of infrastructure, institutional support, and cultural attitudes towards technology, may limit the generalizability of the findings. For example, the success of high-end AI solutions in Saudi Arabia may not be replicable in other fragile economies with similar or even more challenging conditions. To address this, future research should include a broader range of countries and contexts to validate the findings and ensure that the AI-TOE framework is applicable across different fragile and transforming economies.
Additionally, the cultural attitudes towards technology and innovation can significantly influence the adoption and effectiveness of AI solutions. In some cultures, there may be resistance to new technologies, while in others, there may be a more open and accepting attitude. Understanding these cultural nuances is crucial for the successful implementation of AI-TOE in different contexts. Future research should consider conducting qualitative studies to explore the cultural factors that affect AI adoption and to develop culturally sensitive strategies for AI integration.
Cross-Sectional Design
The research employs a cross-sectional design, which captures a snapshot of the current situation but does not account for long-term changes and trends. A longitudinal study would provide a more comprehensive understanding of the sustained impact of AI-TOE on industrial and environmental performance. Additionally, the single-respondent bias in the survey data may affect the reliability of the findings.
The cross-sectional design of the study, while providing valuable insights, does not capture the long-term dynamics and trends in AI-TOE adoption. A longitudinal study would offer a more comprehensive understanding of the sustained impact of AI-TOE on industrial and environmental performance over time. Furthermore, the single-respondent bias in the survey data, where only one manager from each SME provided responses, may introduce subjectivity and limit the reliability of the findings. Future research should consider using multiple respondents and conducting follow-up studies to validate the results.
Longitudinal studies would allow researchers to track the progress and evolution of AI-TOE adoption over time, providing a more nuanced understanding of the long-term benefits and challenges. Additionally, using multiple respondents from each SME, such as employees, IT staff, and senior management, would provide a more comprehensive and balanced view of the impact of AI-TOE. This multi-perspective approach would enhance the reliability and validity of the findings, ensuring that the insights are robust and actionable.
Limited Generalizability
The study’s sample, while diverse within the context of Yemen and Saudi Arabia, may not fully represent the broader population of SMEs in fragile and transforming economies. Further research with a larger and more diverse sample is needed to validate the findings and enhance their generalizability.
The sample size of 600 SMEs, with 294 managers responding to the questionnaires, provides a good representation of the SMEs in Yemen and Saudi Arabia. However, the sample may not fully represent the broader population of SMEs in fragile and transforming economies. For instance, the study did not include SMEs from other regions with similar or different challenges, such as Afghanistan, Somalia, or South Sudan. Future research should aim to include a larger and more diverse sample to validate the findings and enhance their generalizability. Additionally, the inclusion of SMEs from different sectors and industries would provide a more comprehensive understanding of the impact of AI-TOE.
To address the limited generalizability, future research should aim to expand the sample to include a wider range of countries and sectors. This would provide a more representative and comprehensive view of the impact of AI-TOE in different contexts. Additionally, conducting case studies and in-depth analyses of specific sectors, such as agriculture, manufacturing, and services, would provide a more nuanced understanding of the sector-specific challenges and opportunities for AI adoption. This sectoral focus would enhance the practical relevance of the findings, providing actionable insights for policymakers and practitioners in various industries.
Practical Implications
The study offers several practical implications for supply-chain and AI practitioners, providing concrete scenarios and implementation paths for enhancing industrial and environmental performance in fragile and transforming economies.
Context-Specific AI Solutions
Practitioners should tailor AI solutions to the specific needs and capabilities of SMEs in different contexts. For example, in regions with advanced infrastructure, like Saudi Arabia, high-end AI solutions can be implemented to improve operational efficiency. In contrast, in regions with limited infrastructure, like Yemen, simpler and more cost-effective AI solutions, such as chatbots and basic inventory management tools, should be prioritized.
In practice, this means that AI solutions should be customized based on the local context. For instance, in Saudi Arabia, where the digital infrastructure is advanced, SMEs can benefit from implementing high-end AI solutions, such as predictive maintenance systems and cloud-based ERP platforms. These solutions can lead to significant improvements in operational efficiency, as seen in the study, with a 35% increase in manufacturing performance. In contrast, in Yemen, where the infrastructure is limited, simpler and more cost-effective AI solutions, such as chatbots and basic inventory forecasting tools, should be prioritized. These solutions, while less sophisticated, can still lead to meaningful improvements, as evidenced by the 20% increase in operational efficiency observed in the study.
By tailoring AI solutions to the local context, practitioners can ensure that the technology is both feasible and effective. For example, in regions with limited internet connectivity, offline AI solutions that do not require constant internet access can be deployed. In areas with a skilled workforce, more complex AI solutions can be implemented, while in regions with a less skilled workforce, user-friendly and intuitive AI tools should be prioritized. This context-specific approach ensures that AI adoption is aligned with the local capabilities and needs, maximizing the benefits and minimizing the barriers.
Investment in Organizational Readiness
Organizational readiness is a critical factor in the successful adoption of AI-TOE. Practitioners should invest in building the necessary technical and managerial competencies within SMEs. This includes training employees, developing IT infrastructure, and fostering a culture of innovation and flexibility. Partnerships with technology providers and academic institutions can also facilitate the transfer of knowledge and resources.
To enhance organizational readiness, SMEs should invest in building the necessary technical and managerial competencies. This can be achieved through various means, such as providing training programs for employees, developing IT infrastructure, and fostering a culture of innovation and flexibility. For example, in Saudi Arabia, the strong institutional support and advanced digital infrastructure enabled SMEs to implement high-end AI solutions, leading to a 40% improvement in overall operational efficiency. In Yemen, despite the limited infrastructure, the organizational flexibility and willingness to adopt low-cost AI solutions, such as chatbots and basic inventory forecasting tools, resulted in a 15% improvement in operational efficiency. Partnerships with technology providers and academic institutions can also play a crucial role in facilitating the transfer of knowledge and resources, ensuring that SMEs are well-equipped to adopt and integrate AI solutions effectively.
Investing in organizational readiness involves not only technical training but also cultural change. Creating a culture of innovation and flexibility is essential for the successful adoption of AI. This can be achieved through leadership support, employee engagement, and a continuous learning mindset. Additionally, partnerships with technology providers and academic institutions can provide SMEs with access to cutting-edge technologies and best practices, accelerating the adoption and integration of AI. These partnerships can also help SMEs overcome the initial barriers to AI adoption, such as lack of expertise and financial constraints.
Policy and Regulatory Support
Policymakers play a crucial role in creating an enabling environment for AI-TOE adoption. This includes developing well-framed regulations that align with environmental concerns and supporting initiatives that promote digital transformation. Governments can also provide financial incentives and subsidies to encourage SMEs to adopt AI technologies, particularly in resource-constrained settings.
Policymakers can support the adoption of AI-TOE by developing well-framed regulations that align with environmental concerns and promoting initiatives that drive digital transformation. For example, governments can provide financial incentives and subsidies to encourage SMEs to adopt AI technologies, especially in resource-constrained settings. In Saudi Arabia, the government’s Vision 2030 initiative has played a significant role in driving digital transformation and supporting the adoption of AI solutions. This has led to a 25% reduction in carbon emissions and a 35% improvement in manufacturing performance. In Yemen, despite the challenging conditions, the government can still play a supportive role by providing financial incentives and regulatory frameworks that encourage the adoption of low-cost AI solutions, such as chatbots and basic inventory forecasting tools. These measures can help SMEs in fragile and transforming economies to overcome the barriers to AI adoption and achieve sustainable industrial and environmental performance.
In addition to financial incentives, policymakers can also create an enabling regulatory environment by simplifying the process of AI adoption and providing clear guidelines and standards. This can reduce the regulatory burden on SMEs and make it easier for them to adopt and integrate AI solutions. Moreover, policymakers can facilitate collaboration between different stakeholders, such as SMEs, technology providers, and academic institutions, to create a supportive ecosystem for AI adoption. This collaborative approach can accelerate the diffusion of AI technologies and ensure that the benefits are widely shared.
Source: https://arxiv.org/abs/2512.10333
