Factors Affecting Customer Involvement in the Use of Artificial Intelligence-Enabled Customer Relationship Management (AI-CRM) Systems

Yap, Xin Wei (2026) Factors Affecting Customer Involvement in the Use of Artificial Intelligence-Enabled Customer Relationship Management (AI-CRM) Systems. Masters thesis, Tunku Abdul Rahman University of Management and Technology.

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Abstract

The increasing adoption of Artificial Intelligence-enabled Customer Relationship Management (AI-CRM) systems has improved customer engagement and service personalisation. However, excessive system features, continuous communication, and high information demands may create multidimensional digital overload that negatively affects customer involvement. This study examines the effects of system feature overload, communication overload, and cognitive overload on customer involvement in AI-CRM systems, with cognitive burnout acting as a mediating variable. The study is grounded in Cognitive Load Theory (CLT) and the Stimuli-Organism-Response (S-O-R) framework. A quantitative cross-sectional design was adopted, and data were collected through an online survey among Malaysian users with prior AI-CRM experience. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that system feature overload and communication overload significantly increased cognitive overload and cognitive burnout. Furthermore, cognitive overload and cognitive burnout demonstrated significant relationships with customer involvement in AI-CRM systems. The mediation analyses also confirmed the mediating roles of cognitive overload and cognitive burnout in the relationships between overload-related factors and customer involvement. This study contributes to the AI-CRM literature by providing a comprehensive understanding of how multidimensional digital overload influences customer involvement through cognitive mechanisms. The findings also offer practical insights for organisations to develop more user-centric and cognitively sustainable AI-CRM systems.