Service Value in the Age of AI: Reconceptualisation, Conceptual Extension, and Explanatory Integration of the Service Marketing Mix in Higher Education

Artificial intelligence (AI) is transforming higher education service provision, educational engagement, and the processes through which student and institutional value is realised. However, conventional Service Marketing Mix (SMM) frameworks primarily address institutional offerings and service delivery, providing limited integrated explanation of how AI-mediated educational services relate to learning activities, student development, and institutional outcomes.

This conceptual study reconceptualises and extends the traditional seven-P Service Marketing Mix through a literature-informed synthesis and theory-building approach. Integrating service marketing, service-dominant logic, affordance actualisation, learning theories, and organisational development perspectives, the study proposes a sixteen-P Service Marketing Mix framework organised into four analytical layers:

1. Perceived Provisions: Seven reconceptualised service-marketing dimensions representing institutional offerings, perceived value, accessibility, positioning, human oversight, operational integrity, and observable AI-enabled service features.

2. Progression: Three dimensions addressing purposeful educational engagement, learning-directed practice, and academic task performance.

3. Personal Progress: Three dimensions representing independently acquired proficiency, psychological well-being, and student satisfaction.

4. Productive Prosperity: Three dimensions addressing sustained institutional relationships, progressive educational quality, and mission-related educational and societal impact.

The framework distinguishes institutional service provision from educational participation, AI-assisted academic performance from independently demonstrated proficiency, and positive student experiences from institutional improvement and broader societal outcomes. Six theoretical propositions specify potential relationships among the four layers, including direct, indirect, and conditional pathways of educational value realisation.

The study contributes to service-marketing theory by retaining the managerial foundations of the traditional seven Ps while extending the framework's explanatory scope to educational activities, student-level consequences, and institutional outcomes. It provides a conceptual foundation for evaluating AI-mediated higher education services, supporting responsible institutional decision-making, and guiding future empirical research.

Research type: Conceptual research article; theoretical framework development.

Research status: Preprint. The proposed constructs and theoretical relationships have not yet undergone empirical validation or formal peer review.

Keywords: Service Marketing Mix; Artificial Intelligence; Higher Education; Educational Value Realisation; Conceptual Framework; Student Development; Institutional Outcomes.