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Conversational agents powered by artificial intelligence are transforming customer support. Many include artificial empathy (phrasing that validates user emotions) under the assumption that it improves satisfaction and organizational reputation, yet its effect during service failures remains unclear. This study compared an empathic and a neutral conversational agent in a standardized support scenario. In total, 84 participants completed a pre-test, 41 interacted with the agents, and 9 completed a post-test. Emotional state was measured with the Positive and Negative Affect Schedule (PANAS), perceptions with a Likert-scale battery, and AI literacy with the Meta AI Literacy Scale (MAILS). All 41 chatlogs were analyzed for behavioral markers of frustration, escalation, and resolution. The empathic agent resolved more cases (62%) and offered more compensations, but participants rated the neutral agent higher in satisfaction and organizational reputation. Higher AI literacy correlated with lower frustration, while general AI use showed no effect. Despite the small post-test sample, findings reveal a paradox: empathy improved procedural outcomes but not perceived credibility. These results suggest that in chat-based support, users may value efficiency and authenticity over empathic wording. The study provides exploratory evidence that artificial empathy, while operationally useful, can challenge perceived professionalism, emphasizing the need for further research across diverse contexts and populations.

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Artificial Empathy in Conversational Agents: Examining User Perceptions, Emotional Responses, and Organizational Reputation in Customer Support Scenarios

  • 会议论文

  • 发表日期：

    2026年

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  • Wendy Alejandra Molinares-Miranda,
  • Miguel Angel Ortiz-Esparza,
  • Hector Cardona-Reyes

摘要

Conversational agents powered by artificial intelligence are transforming customer support. Many include artificial empathy (phrasing that validates user emotions) under the assumption that it improves satisfaction and organizational reputation, yet its effect during service failures remains unclear. This study compared an empathic and a neutral conversational agent in a standardized support scenario. In total, 84 participants completed a pre-test, 41 interacted with the agents, and 9 completed a post-test. Emotional state was measured with the Positive and Negative Affect Schedule (PANAS), perceptions with a Likert-scale battery, and AI literacy with the Meta AI Literacy Scale (MAILS). All 41 chatlogs were analyzed for behavioral markers of frustration, escalation, and resolution. The empathic agent resolved more cases (62%) and offered more compensations, but participants rated the neutral agent higher in satisfaction and organizational reputation. Higher AI literacy correlated with lower frustration, while general AI use showed no effect. Despite the small post-test sample, findings reveal a paradox: empathy improved procedural outcomes but not perceived credibility. These results suggest that in chat-based support, users may value efficiency and authenticity over empathic wording. The study provides exploratory evidence that artificial empathy, while operationally useful, can challenge perceived professionalism, emphasizing the need for further research across diverse contexts and populations.