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Towards a Model of Customer Satisfaction in the Digital Era: A Systematic Literature Review of the Impact of Artificial Intelligence on Customer Satisfaction

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Dissertation(793.73 KB)

Date

2024

Authors

Ha Ngoc, Khuong

Supervisor

Xu, Yingzi
De Villiers, Rouxelle

Item type

Thesis

Degree name

Master of Business

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Publisher

Auckland University of Technology

Abstract

My research explores the effect of artificial intelligence (AI) capabilities on customer satisfaction. A systematic literature review methodology was conducted to achieve this research's objective by analysing 70 carefully selected journal articles in the marketing domain. By synthesising the findings from relevant, qualified peer-reviewed journal articles, the study synthesises a comprehensive understanding of AI's impact on service interactions and customer experience. My data analysis reveals five themes associated with AI and customer satisfaction: AI system quality, AI anthropomorphism, AI communication quality, AI competency, and customer trust. Following the themes of AI and customer satisfaction identified from the data analysis, I propose a conceptual framework integrating AI and customer satisfaction themes with AI business value (automation and augmentation) and AI customer experiences (data capture, classification, delegation, and social experience). The framework provides a foundation for understanding the relationship between customer satisfaction factors and the interplay of current AI capability. My research makes a notable theoretical contribution by addressing the need for a holistic view of AI and customer satisfaction, establishing clear definitions of AI functions, and integrating insights from diverse fields. Practical implications include providing managers with a multi-dimensional understanding of AI-driven customer satisfaction and a roadmap for aligning AI initiatives with customer experience priorities. Future research directions involve empirically validating the proposed framework, exploring human-AI collaboration effects, and refining the AI-customer-experience (AI-CX) model proposed herein. The study's limitations include potential omissions of relevant research and the need for further validation of the AI-CX components.

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http://hdl.handle.net/10292/18190

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Masters Dissertations