A Conceptual Partial Mediation Framework of AI-CRM Capabilities, Customer Engagement, and Customer Satisfaction in the Ministry of Interior, UAE

Abstract

Artificial intelligence has become a major driver of transformation in Customer Relationship Management (CRM) by enabling organizations to predict customer needs, automate service interactions, personalize communication, analyse customer feedback, and improve decision-making. In public-sector institutions, AI-enabled CRM is increasingly important because citizens, residents, businesses, visitors, and other service users expect government services to be efficient, responsive, secure, transparent, and customer-centred. This paper develops a conceptual partial mediation framework of AI-CRM capabilities, customer engagement, and customer satisfaction in the Ministry of Interior, UAE. The framework identifies five AI-CRM capabilities: predictive analytics, churn prediction and retention tools, chatbots and virtual assistants, personalization, and sentiment analysis. Customer satisfaction is positioned as the dependent variable, while customer engagement is introduced as the mediating variable. The framework is described as a partial mediation model because AI-CRM capabilities are expected to influence customer satisfaction directly and indirectly through customer engagement. The paper contributes to AI-CRM literature by contextualizing AI-enabled customer relationship management within the UAE public-sector environment and by explaining how customer engagement strengthens the relationship between AI-CRM capabilities and customer satisfaction. It also provides a conceptual basis for future empirical testing and practical guidance for improving AI-enabled public service delivery in the Ministry of Interior, UAE.