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Building user trust in AI chatbots for customer service through human-like cues and perceived reliability
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  • Published: 09 February 2026

Building user trust in AI chatbots for customer service through human-like cues and perceived reliability

  • Sheng Wang1,
  • Noor Fatima2,
  • Muhammad Shahbaz3 &
  • …
  • Muhammad Asif3 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Business and management
  • Information systems and information technology
  • Psychology
  • Science, technology and society

Abstract

This qualitative study explores how human-like cues and system competence shape users’ trust and perceptions of reliability in AI-driven chatbot customer service. Data were collected from 28 participants through semi-structured interviews conducted in Pakistan and China. Using thematic analysis supported by NVivo 15, the study identifies key patterns in the formation of user trust and interaction experiences. Two main themes emerged: human-like interaction and emotional connection, and perceived reliability and system competence. The first highlights conversational naturalness, empathy, personalisation, and social presence as drivers of affective trust. In contrast, the second emphasises accuracy, transparency, responsiveness, and data security as core elements of cognitive trust. Together, these dimensions illustrate how emotional and functional factors jointly influence user confidence and satisfaction with chatbots. Beyond reaffirming established trust constructs, the study offers context-specific qualitative insights that deepen understanding of how users in a developing market interpret and negotiate trust in AI-mediated service interactions.

Data availability

Data will be available on demand. Muhammad Shahbaz can be contacted at shahbaz755@yahoo.com.

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Funding

This work was supported by the following funded projects: the Jiangsu Province Key Project of Higher Education Teaching Reform Research, Research on Talent Cultivation Pathways for Live-Streaming E-commerce under the Background of Industrial Integration (No. 2025JGZZ58); and the Jiangsu Province Educational Science Planning Project, Innovative Research on the Cultivation Model of “New Farmers” in Live-Streaming E-commerce from the Perspective of Vocational Education Reform (No. C/2025/02/67).

Author information

Authors and Affiliations

  1. School of E-commerce, Yangzhou Polytechnic Institute, Yangzhou, 225127, Jiangsu, China

    Sheng Wang

  2. Institute of Business Administration, University of the Punjab, Lahore, Pakistan

    Noor Fatima

  3. UE Business School, University of Education, Lahore, Pakistan

    Muhammad Shahbaz & Muhammad Asif

Authors
  1. Sheng Wang
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  2. Noor Fatima
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  3. Muhammad Shahbaz
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  4. Muhammad Asif
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Contributions

N.F. conceptualization, data curation. M.A. written original draft, software, analysis, methodology. M.S. editing, supervision. S.W. editing, supervision, funding.

Corresponding author

Correspondence to Muhammad Shahbaz.

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Competing interests

The authors declare no competing interests.

Informed consent

All participants provided informed consent before taking part in the survey. They were assured of voluntary participation, confidentiality, and the use of data solely for research purposes.

Ethical considerations

This study has been thoroughly reviewed and approved by the Institutional Review Board (IRB) of the UE Business School, University of Education, Lahore, Pakistan, under approval number UE/DM&AS/UEBS/2025/22.

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Wang, S., Fatima, N., Shahbaz, M. et al. Building user trust in AI chatbots for customer service through human-like cues and perceived reliability. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38179-2

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  • Received: 18 October 2025

  • Accepted: 29 January 2026

  • Published: 09 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38179-2

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Keywords

  • Chatbot
  • Trust
  • Human-like interaction
  • Customer service
  • Thematic analysis
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