📈 economics

How Artificial Intelligence (AI) Affects Sustainable Economic Growth in the Jordanian Industrial Sector

This study empirically demonstrates that Artificial Intelligence adoption positively drives sustainable economic growth in Jordan's industrial sector, with organizational learning serving as a significant partial mediator in this relationship.

Original authors: Ahmad Eyadat, Anas Kanaan, Tariq Talafha, Odai Mansour, Saed Mustafa, Amro Alamaren, Oualid Ali

Published 2026-07-08

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Ahmad Eyadat, Anas Kanaan, Tariq Talafha, Odai Mansour, Saed Mustafa, Amro Alamaren, Oualid Ali

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: AI as a Super-Engine

Imagine the industrial sector in Jordan (factories, manufacturing plants, etc.) as a fleet of trucks trying to deliver goods. The researchers wanted to know: If we put a high-tech, self-driving engine (Artificial Intelligence) into these trucks, will they drive faster, use less fuel, and last longer (Sustainable Economic Growth)?

The answer from the study is a resounding yes, but with a very important catch. It's not just about installing the engine; it's about having a driver who knows how to use it.

The Cast of Characters

To understand the study, think of three main characters:

Artificial Intelligence (AI): The High-Tech Engine. It's a powerful tool that can predict traffic, optimize routes, and fix problems before they happen. It generates a massive amount of data and insights.
Organizational Learning: The Driver's Training School. This is the company's ability to learn, share knowledge, and adapt. It's the team sitting in the cab, reading the engine's data, and figuring out how to drive better together.
Sustainable Economic Growth: The Destination. This isn't just about making a quick profit today; it's about the trucking company staying in business for decades, growing steadily, and being efficient without burning out.

What the Researchers Did

The team surveyed 120 managers from industrial companies in Jordan. These were experienced leaders (many with over 10 years of experience and advanced degrees) who knew their businesses inside and out. They asked them questions about:

How much they use AI.
How well their company learns and shares knowledge.
How much their company has grown and become more efficient.

They used a statistical method (like a sophisticated calculator) to see how these three things connect.

The Three Main Findings (The "What Happened")

1. AI Makes You a Better Learner (H1)

The Finding: When companies start using AI, they learn faster.
The Analogy: Think of AI as a super-tutor. Before, a manager might have to guess why a machine broke down. Now, the AI tells them exactly why, shows them the pattern, and suggests a fix. This forces the whole team to learn new things and share that knowledge. The study found a strong link: More AI = Better Learning.

2. Learning Leads to Long-Term Success (H2)

The Finding: Companies that are good at learning grow better and last longer.
The Analogy: A driver who keeps taking driving school and learning new routes will always be more efficient than one who just drives the same way every day. When a company learns, it becomes more innovative and efficient, which leads to Sustainable Economic Growth.

3. AI Directly Helps Growth (H3)

The Finding: Even without looking at learning, AI directly helps companies grow.
The Analogy: Just putting the Super-Engine in the truck makes it faster and more fuel-efficient immediately. AI automates boring tasks and cuts waste, which directly boosts the bottom line.

The Secret Ingredient: The "Bridge" (Mediation)

This is the most important part of the paper. The researchers found that Organizational Learning acts as a bridge or a magnifying glass.

The Analogy: Imagine you have a powerful Flashlight (AI).
- If you just shine the flashlight on a wall, it helps a little (Direct Effect).
- But, if you have a Team of Explorers (Organizational Learning) who know how to read the light, map the terrain, and share the map with everyone, the flashlight becomes incredibly powerful.
The Result: The study found that AI helps growth in two ways:
1. Directly: It makes things faster.
2. Indirectly (The Bridge): It gives companies new information, which they learn from, which then makes them even smarter and more successful.

The study calls this "Partial Mediation." This means AI is great on its own, but it becomes even more powerful when the company knows how to learn from it.

Why This Matters for Jordan

The paper highlights that while many studies look at rich countries with perfect technology, this study looked at Jordan, an emerging economy.

The Lesson: For Jordanian factories to succeed in the modern world, they can't just buy the "Super-Engine" (AI) and hope for the best. They must also build the "Training School" (Organizational Learning).
The Advice: Managers need to invest in training their people to understand the data AI gives them. Policymakers should support programs that teach digital skills and knowledge sharing.

What the Study Didn't Say (Limitations)

The authors were honest about what they couldn't prove:

Snapshot in Time: They took a "photo" of the companies at one moment. They didn't watch them over 5 or 10 years to see if the growth lasted forever.
Just Jordan: The results are specific to Jordan's industrial sector. Other countries or different types of businesses (like hospitals or schools) might have different results.
Self-Reporting: The data came from managers answering surveys. It's possible they were a bit too optimistic about their own success.

Summary in One Sentence

Artificial Intelligence is a powerful tool that helps Jordanian industries grow, but it works best when companies use that technology to teach their teams how to learn, adapt, and innovate together.

Technical Summary: Artificial Intelligence and Sustainable Economic Growth in the Jordanian Industrial Sector

Problem Statement
While Artificial Intelligence (AI) is widely recognized as a transformative force for the Fourth Industrial Revolution, there is a significant empirical gap regarding its impact on sustainable economic growth within emerging economies. Existing literature predominantly focuses on developed nations with mature technological infrastructures, leaving a void in understanding how AI functions in contexts characterized by varying institutional environments and technological readiness. Furthermore, prior research has largely examined the direct link between AI adoption and organizational performance, often neglecting the specific organizational mechanisms—such as knowledge conversion processes—that mediate the translation of AI investments into sustainable economic outcomes. This study addresses these gaps by investigating the Jordanian industrial sector, aiming to determine how AI adoption influences sustainable economic growth and to identify the mediating role of organizational learning in this relationship.

Methodology
The study employs a quantitative, cross-sectional research design grounded in a deductive strategy. The theoretical framework integrates three distinct theories:

Resource-Based View (RBV): Posits AI as a strategic, valuable, rare, and non-substitutable resource.
Dynamic Capabilities Theory (DC): Suggests organizations must reconfigure resources to adapt to changing environments.
Endogenous Growth Theory (EGT): Links long-term economic growth to technological innovation and knowledge generation.

Data was collected via structured questionnaires from 120 managers within industrial organizations in Jordan. The sample was selected using purposive sampling to ensure respondents possessed relevant experience with AI and organizational strategy. The survey utilized a 5-point Likert scale to measure three latent constructs: Artificial Intelligence Adoption (Independent Variable), Organizational Learning (Mediating Variable), and Sustainable Economic Growth (Dependent Variable).

Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS version 4. This method was chosen for its suitability with smaller sample sizes and complex predictive models involving mediation. The analysis proceeded in two phases:

Measurement Model Assessment: Evaluated indicator reliability (factor loadings > 0.7), internal consistency (Cronbach's Alpha and Composite Reliability > 0.7), convergent validity (AVE > 0.5), and discriminant validity (HTMT < 0.85).
Structural Model Assessment: Examined path coefficients, $R^2$ (coefficient of determination), $Q^2$ (predictive relevance), and bootstrapped confidence intervals (5,000 resamples) to test hypotheses and mediation effects.

Key Results
The empirical analysis yielded the following statistically significant findings:

H1 Supported: AI adoption has a strong positive influence on organizational learning ( $\beta = 0.612, p < 0.001$ ). AI adoption explains 37.5% of the variance in organizational learning.
H2 Supported: Organizational learning positively influences sustainable economic growth ( $\beta = 0.487, p < 0.001$ ).
H3 Supported: AI adoption has a direct positive impact on sustainable economic growth ( $\beta = 0.321, p < 0.001$ ).
H4 Supported: Organizational learning acts as a partial mediator in the relationship between AI adoption and sustainable economic growth. The indirect effect was significant ( $\beta = 0.298, p < 0.001$ ).

The model demonstrated strong explanatory power, with AI adoption and organizational learning collectively accounting for 58.1% of the variance in sustainable economic growth ( $R^2 = 0.581$ ). Predictive relevance ( $Q^2$ ) was confirmed for both endogenous constructs.

Key Contributions
The paper makes three primary contributions to the existing literature:

Contextual Expansion: It provides empirical evidence from an emerging market (Jordan), extending the geographic scope of AI research beyond developed economies and addressing the lack of studies in developing contexts with distinct institutional environments.
Mechanism Identification: It identifies and validates organizational learning as a critical mediating mechanism. The study demonstrates that AI does not merely generate value through automation; rather, its economic impact is significantly amplified when organizations possess the capability to acquire, distribute, interpret, and apply the knowledge generated by AI.
Theoretical Integration: It successfully integrates RBV, Dynamic Capabilities, and Endogenous Growth Theory to explain the pathway from technological adoption to sustainable economic growth, reinforcing the view that strategic resources (AI) require complementary capabilities (learning) to yield superior performance.

Significance and Claims
The authors claim that the study offers concrete insights for managers and policymakers in emerging economies regarding digital transformation. The findings suggest that for industrial organizations to fully reap the productivity and innovation benefits of AI, they must embed these technologies within robust organizational learning processes. The study posits that sustainable economic growth is not solely a result of technology investment but is driven by the interaction between technology and the organization's ability to convert data into actionable knowledge.

Practically, the paper argues that managers must cultivate a culture of knowledge sharing and continuous learning to maximize AI investments. For policymakers, the study supports the development of national strategies that simultaneously promote digital infrastructure and workforce upskilling in organizational learning and digital literacy. The research concludes that while AI is a powerful driver of growth, its effectiveness is contingent upon the organization's learning capabilities, making the development of these capabilities a strategic imperative for sustainable industrial development in Jordan and similar emerging economies.

Limitations
The authors acknowledge several limitations: the cross-sectional nature of the data prevents definitive causal inferences; the focus is limited to the industrial sector in Jordan, limiting generalizability to other sectors or countries; the reliance on self-reported survey data introduces potential common method bias; and the study examines only one mediator (organizational learning), excluding other potential variables like innovation capability or absorptive capacity.