Exploring the Effect of Attachment on Technology Addiction to Generative AI Chatbots: A Structural Equation Modeling Analysis
Abstract
With the advancement of artificial intelligence technology, generative AI chatbots (GAIC) such as ChatGPT, are integrating into various aspects of society and quietly changing people’s lives. However, although there is a plethora of research concerning social media usage and addiction, discussions about chatbots remain far from adequate. Investigating the use and potential technology addiction to generative AI chatbots (TAGAIC) holds both theoretical and practical significance. Drawing on attachment theory, this study aims to investigate the formative factors in TAGAIC. A structural equation model was used to analyze the data collected from a questionnaire survey of 364 GAIC users. Results reveal that technology addiction is positively influenced by emotional attachment but not by functional attachment. Besides, perceived anthropomorphism and perceived empathy have a positive effect on emotional attachment, while system quality and information quality have a positive effect on functional attachment. Limitations of the study were discussed.
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Perceived empathy predicts emotional attachment.
“Huang and Huang found that emotional attachment (predicted by anthropomorphizing and perceived empathy) predicted LLM-CB addiction121.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [121]
Emotional attachment predicts LLM-CB addiction.
“Huang and Huang found that emotional attachment (predicted by anthropomorphizing and perceived empathy) predicted LLM-CB addiction121.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [121]
Anthropomorphizing predicts emotional attachment.
“Huang and Huang found that emotional attachment (predicted by anthropomorphizing and perceived empathy) predicted LLM-CB addiction121.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [121]
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“Huang and Huang found that emotional attachment (predicted by anthropomorphizing and perceived empathy) predicted LLM-CB addiction 121 .”
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Untitled work
Eom, D., Renner, J., & Chinn, S. (2026) · arXiv (preprint)
- Authors
- Huang, Y., & Huang, H.
- Affiliations
- School of Journalism and Communication, Xiamen University
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2024-11-15
- Volume
- 41
- Issue
- 15
- Pages
- 9440-9449
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4404415230
- Cited by
- 58
- References
- 21
- Citations by year
- 2026: 38 · 2025: 19 · 2024: 1
- FWCI
- 21.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- technology addiction, generative AI chatbots, attachment theory, emotional attachment, functional attachment, perceived anthropomorphism, perceived empathy, system quality, information quality, ChatGPT
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