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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Sheng Wang,
  • Noor Fatima,
  • Muhammad Shahbaz,
  • Muhammad Asif

摘要

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.