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How does a service chatbot build parasocial relationships with customers? A perspective from parasocial interaction theory

Ching-Hua Lu et al.

Internet Research2026https://doi.org/10.1108/intr-03-2024-0421article
AJG 3ABDC A
Weight
0.50

Abstract

Purpose Drawing on parasocial interaction theory, this study examines how key attributes of artificial intelligence (AI)-enabled chatbot services, including functionality, trustworthiness, efficiency, human-likeness, responsiveness and reference to the service, affect consumers’ perceived interactivity and foster brand experience, thereby enhancing parasocial relationships between brands and customers. Design/methodology/approach Structural equation modeling was used to analyze the data. A total of 278 valid responses were obtained from an online questionnaire survey. Findings The attributes of AI-enabled chatbot services, namely trustworthiness, efficiency, human-likeness and reference to the service, positively affect their perceived interactivity. Furthermore, brand experience mediates the relationship between perceived interactivity and parasocial relationships. Practical implications AI-enabled chatbots serve as virtual frontline employees capable of simulating human-like interpersonal engagement, thereby enhancing brand experiences. When consumers perceive a high level of interactivity with an AI-enabled chatbot, they develop more favorable impressions of the brand, which in turn strengthens the parasocial bond between the brand and its customers. These findings provide practical guidance for designing AI-enabled chatbot services that foster deeper brand–customer relationships. Originality/value While previous research on AI-enabled chatbots has focused primarily on anthropomorphic features and service quality, limited attention has been paid to how specific service attributes contribute to building parasocial relationships in the context of online retail. This study addresses this gap by demonstrating how chatbot–customer interactions can strengthen brand–customer connections. Our findings provide valuable insight for managers across various industries and offer guidance on the application of AI technologies in digital transformation strategies to enhance customer service.

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https://doi.org/https://doi.org/10.1108/intr-03-2024-0421

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@article{ching-hua2026,
  title        = {{How does a service chatbot build parasocial relationships with customers? A perspective from parasocial interaction theory}},
  author       = {Ching-Hua Lu et al.},
  journal      = {Internet Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/intr-03-2024-0421},
}

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How does a service chatbot build parasocial relationships with customers? A perspective from parasocial interaction theory

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.