Social and psychological factors in AI chatbot trust-building

Purpose This study examines the aspects driving the formation of effective brand-human–AI connections. It explores the impact of consumers’ trust in AI-enabled e-commerce chatbots on their buying intention, e-loyalty (EL) and reuse intention (RUI). The study develops a conceptual model, drawing on social support theory, psychological reactance theory and the uncanny valley effect. Design/methodology/approach We collected data from 429 AI-enabled shopping chatbot users in Malaysia, employing a convenience sampling approach within a specific purposive frame, and analysed them using partial least squares structural equation modeling (PLS-SEM) via SmartPLS4. Findings Findings indicate that user autonomy with the chatbot and social role enhances consumers’ trust in chatbots. This trust, in turn, positively impacts customers’ purchase intentions, e-loyalty and chatbot RUI. However, the chatbot’s humanlikeness does not affect consumers’ trust in chatbots. The study also revealed that increasing a chatbot agent’s humanlike qualities increases users’ perception of eeriness, which decreases users’ trust in chatbots. Nevertheless, the chatbot’s humanlike attributes and user eeriness were significantly influenced by users’ familiarity with it. Originality/value This study’s theoretical and practical implications offer valuable insights for enhancing the online shopping experience through anthropomorphised chatbot agents in e-commerce.

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Publication Details

Journal
International Journal of Retail & Distribution Management
Published
2026-06-13
DOI
https://doi.org/10.1108/ijrdm-03-2025-0158
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
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article

Social and psychological factors in AI chatbot trust-building

Imran Hameed, Irfan Talib Hameed, Prema Ponnudurai, Zeeshan A. Bhatti et al.
International Journal of Retail & Distribution Management
AI in Service Interactions
article

Social and psychological factors in AI chatbot trust-building

Imran Hameed, Irfan Talib Hameed, Prema Ponnudurai, Zeeshan A. Bhatti, Midhat Nadeem
article en

Abstract

Purpose This study examines the aspects driving the formation of effective brand-human–AI connections. It explores the impact of consumers’ trust in AI-enabled e-commerce chatbots on their buying intention, e-loyalty (EL) and reuse intention (RUI). The study develops a conceptual model, drawing on social support theory, psychological reactance theory and the uncanny valley effect. Design/methodology/approach We collected data from 429 AI-enabled shopping chatbot users in Malaysia, employing a convenience sampling approach within a specific purposive frame, and analysed them using partial least squares structural equation modeling (PLS-SEM) via SmartPLS4. Findings Findings indicate that user autonomy with the chatbot and social role enhances consumers’ trust in chatbots. This trust, in turn, positively impacts customers’ purchase intentions, e-loyalty and chatbot RUI. However, the chatbot’s humanlikeness does not affect consumers’ trust in chatbots. The study also revealed that increasing a chatbot agent’s humanlike qualities increases users’ perception of eeriness, which decreases users’ trust in chatbots. Nevertheless, the chatbot’s humanlike attributes and user eeriness were significantly influenced by users’ familiarity with it. Originality/value This study’s theoretical and practical implications offer valuable insights for enhancing the online shopping experience through anthropomorphised chatbot agents in e-commerce.

International Journal of Retail & Distribution Management
Taylor's University (MY), Institute of Business Management (PK), Institute of Business, Psychology and Management (RU), Sohar University (OM), University of Portsmouth (GB)
Decent work and economic growth
Openalex Percentile: Top 3%
AI in Service Interactions
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Social and psychological factors in AI chatbot trust-building — Imran Hameed, Irfan Talib Hameed, et al. · International Journal of Retail & Distribution Management (2026) | TGRS Research Map | TGRS