Measuring customer experience in the age of artificial intelligence

Ghesh, Nada Mohammad

Thesis

Measuring customer experience in the age of artificial intelligence

Download PDF

Creator

Ghesh, Nada Mohammad

Rights statement

Strathclyde Thesis Copyright

Awarding institution

University of Strathclyde

Date of award

2026

Thesis identifier

T17640

Person Identifier (Local)

202170428

Qualification Level

Doctoral (Postgraduate)

Qualification Name

Doctor of Philosophy (PhD)

Department, School or Faculty

Department of Marketing

Abstract

The emergence of artificial intelligence-enabled technologies (AI-ETs) has revolutionized customer experiences (CX), where AI-ETs play increasingly prominent roles, customers interact more actively, new touchpoints are introduced and existing ones and reconfigured. Despite acknowledged importance on both academic and practical levels, the impact of artificial intelligence (AI) on CX is underexplored, reflecting a novel and developing phenomena. Previous literature typically views AI as contextual, fails to acknowledge its transformative potential, and frequently adopts a conceptual focus or addresses individual touchpoints rather than AI’s holistic impact. To address this gap, this thesis aims to explore customer experiences enabled by AI and measure their impact on associated behavioural outcomes through two research objectives. The first objective is to understand and map the research landscape on the role of AI in shaping customer experiences. The second objective is to develop a scale for measuring the AICX. This thesis begins with a systematic literature review (SLR) to explore AI-enabled customer experiences (AICX) and identify knowledge gaps. This informed a sequential exploratory mixed methods study conducted in two phases. Phase one involved qualitative conceptualisation of AICX and item generation, drawing on literature and netnography, followed by expert review. Phase two focused on quantitative validation of the scale, conducted through a pilot and three surveys with customers who had prior interactions with AI-ETs. This thesis makes three key contributions to CX theory and the broader services marketing literature. First, it introduces the AICX as a unified and novel construct, conceptualised with a framework of diverse AI-ETs. Second, it presents the AI-ET Cube, offering a systematic approach to categorising and analysing AI-ETs based on their roles within the customer journey, shifting the focus from technological characteristics to their functional and experiential impact. Finally, it develops a measurement scale—a robust tool to measure, manage, and adapt AICX. These contributions address key theoretical and empirical gaps by providing a structured lens to study AICX dynamics and enabling businesses to assess and optimise its implementation.

Advisor / supervisor