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

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  • Stefanie Helene Klein

    (Leibniz-Institut für Wissensmedien)

Abstract

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 A systematic search for published and unpublished research resulted in a final sample of 800 effect sizes from 199 datasets reported in 142 papers (N = 41,642).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.

Suggested Citation

  • Stefanie Helene Klein, 2025. "The effects of human-like social cues on social responses towards text-based conversational agents—a meta-analysis," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05618-w
    DOI: 10.1057/s41599-025-05618-w
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