Service Value in the Age of AI: Reconceptualisation, Conceptual Extension, and Explanatory Integration of the Service Marketing Mix in Higher Education
Year: 2026
Type: preprint
Abstract: Artificial intelligence (AI) is changing how higher education institutions provide services, how students engage in educational activities, and how educational value is realised. However, conventional Service Marketing Mix (SMM) frameworks primarily explain institutional offerings and service delivery, providing a limited integrated explanation of how AI-mediated services relate to student development and institutional outcomes. This study addresses this conceptual and explanatory gap by reconceptualising and extending the SMM for AI-mediated higher education. Using a literature-informed conceptual synthesis and theory-building approach, the study integrates service marketing, service-dominant logic, affordance actualisation, learning theories, and organisational development perspectives. It proposes a sixteen-P SMM framework organised into four analytical layers: Perceived Provisions, Progression, Personal Progress, and Productive Prosperity. Seven SMM dimensions reconceptualise established service-marketing functions, while nine additional dimensions extend the framework to educational activities, student-level consequences, and institutional outcomes. Six theoretical propositions specify potential relationships among the four layers, including direct, indirect, and conditional pathways of educational value realisation. A central insight is that AI-enabled service availability does not necessarily produce meaningful engagement, AI-assisted academic performance does not establish independently acquired proficiency, and positive student experiences do not automatically translate into institutional improvement or societal impact. The framework therefore distinguishes service provision, educational activity, individual development, and institutional achievement without treating them as interchangeable outcomes or assuming a predetermined causal sequence. The framework also provides a basis for evaluating how institutional service strategies may be adjusted to support both quantitative performance and qualitative educational development. The proposed constructs and relationships remain subject to empirical validation, competing-model evaluation, and contextual refinement. The study argues that reconceptualising and extending the SMM is necessary because AI changes not only how educational services are delivered but also how educational activities are performed, how learning outcomes are demonstrated, and how institutional value is assessed. A framework limited to institutional inputs and service delivery risks overlooking the difference between technological efficiency and genuine educational development. By connecting service provision with educational processes and independently assessable consequences, the proposed framework offers a more comprehensive basis for responsible service-marketing decisions, educational quality improvement, and institutional purpose fulfilment in AI-mediated higher education.
Author: Prashant Mahajan
Language: English
Subfield: Marketing
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7225124072