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<p>Humanizing chatbots through social cues is a common strategy to increase user acceptance. However, whether and in which circumstances this strategy is generally effective is still unclear. This meta-analysis thus examines the effect of text-based chatbots’ social cues on users’ social responses and the influence of potential moderators. It includes experimental studies that manipulate human-likeness using social cues and examine their effects on user responses, including attitude, perception, affect, rapport, trust, and behavior. A systematic search for published and unpublished research resulted in a final sample of 800 effect sizes from 199 datasets reported in 142 papers (<i>N</i> = 41,642). Meta-analytic random-effects models computed overall and for each outcome category yielded a small effect of human-likeness on social responses (<i>g</i> = 0.36, 95% CI [0.27, 0.44]). The results further suggested that human-like chatbot characteristics improve user responses to varying degrees and under different boundary conditions. The findings can guide practitioners in designing effective and ethically justifiable chatbots.</p>

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The effects of human-like social cues on social responses towards text-based conversational agents—a meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Stefanie Helene Klein

摘要

Humanizing chatbots through social cues is a common strategy to increase user acceptance. However, whether and in which circumstances this strategy is generally effective is still unclear. This meta-analysis thus examines the effect of text-based chatbots’ social cues on users’ social responses and the influence of potential moderators. It includes experimental studies that manipulate human-likeness using social cues and examine their effects on user responses, including attitude, perception, affect, rapport, trust, and behavior. A systematic search for published and unpublished research resulted in a final sample of 800 effect sizes from 199 datasets reported in 142 papers (N = 41,642). Meta-analytic random-effects models computed overall and for each outcome category yielded a small effect of human-likeness on social responses (g = 0.36, 95% CI [0.27, 0.44]). The results further suggested that human-like chatbot characteristics improve user responses to varying degrees and under different boundary conditions. The findings can guide practitioners in designing effective and ethically justifiable chatbots.