Understanding Artificial Intelligence Addiction in Gen Z: The Role of Overreliance, Emotional Attachment, Perceived Usefulness and Trust

Abstract

Concerns about excessive usage behaviours, or compulsive behaviour patterns, associated with increased use of Artificial Intelligence (AI) technologies in everyday life are growing. Using Behavioural Addiction Theory, Automation Bias Theory, and Social Response (Media Equation) theory as a basis and then critiquing the underlying assumptions of Technology Acceptance Theory, this study investigates the relationship between reliance (Overreliance), the Perceived Usefulness of an AI system, Emotional Attachment to an AI system and Trust in an AI system to create an "addictive" user experience. A quantitatively based, cross-sectional methodology was utilised in conducting this research, where primary data were collected from 242 actively engaging with AI users were obtained using a structured questionnaire, which is measured using a 5-point Likert scale. Data were analysed using SPSS version 20 and were studied by descriptive statistics, correlation analysis, multiple regression analysis and ANOVA. Results indicate that Overreliance, Emotional Attachment, and Trust are all statistically significant and positively predict AI addiction. However, perceived usefulness does not have a statistically significant direct effect on AI addiction. Overall, the regression model account for approximately 40% of the variance in AI addiction, thereby providing evidence of its strong explanatory power. Statistical analyses of demographics indicate no statistically significant difference across age. However, educational qualification did show statistically selective differences between levels of AI addiction experienced. This research contributes to the theoretical understanding of AI addiction, demonstrating that addiction to AI technologies is largely due to psychological and relational mechanisms, rather than solely being driven by functional utility. The findings emphasize the importance of responsible AI design, user awareness and targeted interventions to mitigate addictive AI usage behavior.