Enterprise AI Analysis
Al-integrated procurement and inventory systems in Jordanian customs points and employee performance: evidence from e-commerce supply chains
This research explores the impact of AI-based technology on e-commerce supply chains in a sample of customs officers at prime border and port points of entry in the Hashemite Kingdom of Jordan. It explores the effect of three major digital capabilities on the competitiveness of firms: logistics and delivery, IT and information, and customer experience and demand management.
The study commences with employee performance and job satisfaction and further explores the effect of AI-based procurement and inventory systems. A total of 338 customs officials responded to a structured survey questionnaire, and the data were analyzed using Structural Equation Modeling and Confirmatory Factor Analysis.
Our findings indicate that all three digital devices result in positive effects on employee performance, and digital procurement and inventory systems reinforced with AI technologies boost task autonomy, clarity and sharing decision process. Second, employee performance plays a mediate role in the relationship between e-commerce supply chain technologies and job satisfaction, which means that better e-commerce supply chain technologies will result in increasing the performance of employees.
The results suggest a human centric digital system for the logistics of public sector. It's this form of training combined with infrastructure to allow employee feedback and company-wide deployment of inclusive AI that will provide the most value. These opinions offer policy guidance on the modernization of customs clearance in digitalized-developing societies. These opinions are emphasizing for practical orientation of the customs reform in the fast digitalizing developing countries societies.
Quantifiable Impact: Key Metrics
Explore the direct, measurable improvements our AI solutions deliver.
0.54
R2 for employee job satisfaction
0.412
LF → EP Path Coefficient
0.365
IT → EP Path Coefficient
0.301
CEMD → EP Path Coefficient
0.633
EP → JSAE Path Coefficient
Deep Analysis & Enterprise Applications
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3.75
Logistics and Fulfilment Mean Score
3.63
Information and Technology Mean Score
3.47
AI-integrated Procurement and Inventory Mean Score
Enterprise Process Flow
Logistics and Fulfilment
→
Information and Technology
→
Customer Experience and Managing Demand
→
Employee Performance
→
Job Satisfaction Among Employees
| Feature | Traditional ECSC | AI-Integrated ECSC |
|---|---|---|
| Operational Efficiency |
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| Decision Making |
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| Employee Impact |
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| Organizational Agility |
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Jordan Customs Modernization: A Path to Human-Centric AI Integration
The study's findings reveal that while ECSC initiatives positively impact employee performance, the integration of AI-integrated procurement and inventory systems (AIIPIS) significantly moderates these effects. This highlights the importance of human-centered AI design, where technology augments rather than replaces human capabilities.
- Key Learning: AI-enabled systems boost task autonomy and clarity for customs officers.
- Key Learning: Employee performance mediates the relationship between ECSC technologies and job satisfaction.
- Key Learning: Training and infrastructure for employee feedback are crucial for successful AI deployment.
- Key Learning: Customs reforms should prioritize an inclusive AI strategy that considers human behavior and organizational readiness.
Advanced ROI Calculator
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Estimated Annual Savings
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AI Implementation Roadmap
Our phased approach ensures seamless integration and rapid value realization.
Phase 1: Assessment & Strategy Alignment
Conduct a thorough analysis of current procurement and inventory processes, identify key challenges, and align AI integration strategy with organizational goals. This includes stakeholder workshops and technology readiness assessments.
Phase 2: Pilot Implementation & User Training
Deploy AIIPIS in a controlled pilot environment. Provide targeted training for customs officers, focusing on hands-on system use, data interpretation, and collaborative decision-making with AI tools. Gather initial user feedback for refinement.
Phase 3: Scaled Deployment & Continuous Optimization
Roll out AIIPIS across relevant departments. Establish feedback loops, monitor performance metrics (e.g., task accuracy, response times), and continuously optimize AI models and workflows based on operational data and employee input.
Phase 4: Performance Review & Skill Development
Conduct post-implementation review to measure ROI and impact on employee performance and job satisfaction. Invest in ongoing digital literacy programs and advanced AI skills development for staff to ensure sustained competitive advantage.
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