Enterprise AI Analysis
Evaluating Egyptian Citizens' Perception Toward Voice Assistant Technology for Public Service Delivery
Authors: Yasser Halim, Hazem Halim, Karim Salem, Israa Lewaaelhamd
Journal: Future Business Journal, (2025) 11:242
Published: October 04, 2025
This research assesses the inclination of Egyptian citizens toward embracing Voice Assistant Technology (VAT) to deliver public services based on the functioning of perceived usefulness, ease of use, trust, and perceived risk. This also examines the possibility of using machine learning (ML) models to forecast adoption behavior.
Executive Impact & Key Findings
Our analysis highlights critical success factors and predictive insights for AI adoption in public administration.
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Top ML Prediction Accuracy (SGD)
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Citizens' Acceptance Reliability (Cronbach's)
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Usefulness Impact on VAT Acceptance
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Perceived Risk Impact on VAT Acceptance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding Perceived Usefulness
Perceived Usefulness (PU) refers to the degree to which users believe technology enhances performance. The empirical data consistently supports a positive influence of usefulness on technology adoption. For Egyptian public services, this means VAT must clearly demonstrate how it improves efficiency, saves time, and provides better access to information.
Simplicity in Adoption: Perceived Ease of Use
Perceived Ease of Use (PEU) refers to the perception of requiring little effort to use a system. Prior research universally supports its positive impact on adoption. For VAT in Egypt, this implies designing interfaces that are intuitive, require minimal training, and are accessible across different literacy and age groups to ensure broad acceptance.
Building Trust: A Crucial Factor for VAT Adoption
Trustworthiness encompasses competence (system effectiveness), honesty (open communication), and benevolence (real concern for users). This is particularly critical in high-uncertainty avoidance cultures like Egypt, where people are often cautious of new technology. Establishing trust is a pre-adoption necessity, requiring transparency and reliable performance.
Mitigating Perceived Risk in Public Service AI
Perceived Risk reflects anxieties over privacy, potential fraud, and dependability. The presence of cybercrime exacerbates distrust, making risk reduction essential for VAT adoption. Addressing concerns about data leakage, service malfunction, and financial insecurity is vital to encourage citizens to embrace AI-driven public services.
Forecasting Adoption with Machine Learning
Machine Learning (ML) models, specifically Stochastic Gradient Descent (SGD) and Ridge Regression, were utilized to predict citizens' perception toward VAT adoption with high accuracy (71.9% and 70.9% respectively). These computational methods complement traditional behavioral theories by providing robust predictive power for forecasting technology acceptance trends.
Enterprise Process Flow: Conceptual Model of VAT Acceptance
Perceived Usefulness
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Perceived Ease of Use
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Perceived Trustworthiness
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Perceived Risk
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Citizens' Acceptance of VAT
| Models | MSE | RMSE | MAE | Accuracy level |
|---|---|---|---|---|
| Ridge regression | 0.358125 | 0.598435 | 0.42134 | 70.94% |
| Gradient boosting | 0.400894 | 0.633162 | 0.455648 | 67.46% |
| Random forest | 0.444423 | 0.666651 | 0.456421 | 63.93% |
| SGD | 0.345969 | 0.588191 | 0.418641 | 71.92% |
| Support vector machine | 0.388439 | 0.623249 | 0.43123 | 68.48% |
| Decision tree | 0.624383 | 0.790179 | 0.523802 | 49.33% |
| k-nearest neighbors | 0.399485 | 0.632048 | 0.482591 | 67.58% |
| XGBoost | 0.424169 | 0.651282 | 0.455674 | 65.58% |
| CatBoost | 0.428009 | 0.654224 | 0.45291 | 65.26% |
| LightGBM | 0.421754 | 0.649426 | 0.459824 | 65.77% |
Key Conclusions for Enterprise Strategy
The findings demonstrate that perceived usefulness, ease of use, and trust significantly encourage VAT acceptance, while perceived risk acts as a barrier. The ML results corroborate these findings and highlight the promise of predictive analytics in forecasting adoption trends, a rarely applied innovation in public administration scholarship. This study uniquely links classic theoretical models with contemporary data-driven practices.
Case Study: PWC Report - Challenges in VAT Adoption
Reports from PWC indicate that Egyptian citizens' limited knowledge of the full breadth of VAT capabilities, their hesitation due to probable system complexity and its repercussions, as well as a general lack of trust, are primary reasons why VAT introduction activities beyond basic functionalities are rarely used. This underscores the need for comprehensive citizen education and transparent trust-building initiatives.
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AI Implementation Roadmap for Public Services
A phased approach to integrate Voice Assistant Technology, focusing on citizen trust and seamless adoption.
Phase 01: Pilot & Trust Building
Implement VAT in a limited scope, focusing on high-impact, low-risk services. Prioritize transparent communication about data security and system reliability to build initial citizen trust. Gather feedback and address concerns proactively.
Phase 02: Incremental Rollout & Education
Gradually expand VAT services, ensuring parallel traditional channels remain available. Launch public awareness campaigns and digital literacy programs. Engage early adopters (youth) to champion the technology.
Phase 03: Feature Enhancement & Personalization
Based on user data, enhance VAT functionality, accommodate diverse user preferences (e.g., accents, languages), and introduce personalized service options. Continuously monitor performance and iterate based on ML-driven insights.
Phase 04: Full Integration & Policy Alignment
Integrate VAT across all eligible public services, leveraging predictive analytics for resource allocation and service optimization. Develop robust policies to ensure data protection, ethical AI use, and long-term sustainability.
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