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Is more human-like better? The impact of virtual streamer language naturalness on user engagement

Author

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  • Zhang, Ruoxi
  • Fang, Jiaming
  • Han, Lintong
  • Liao, Miyan

Abstract

The proliferation of livestreaming eCommerce platforms has witnessed a surge in the utilization of virtual streamers. Language naturalness of streamer refers to how “human-like” the streamer sounds in both their speech (voice naturalness) and scripted language (text naturalness), serving as a crucial informational cue that can swiftly capture user attention. However, empirical investigation into the significance of language naturalness exhibited by virtual streamers within the context of livestreaming events remains largely unexplored. To address this critical yet under-researched issue, this study employs a comprehensive approach, integrating deep learning networks, large language models, fundamental frequency extraction, and econometric analysis on a dataset comprising 394 livestreaming events from a large livestreaming platform. Building on the information foraging theory, this study examines the impact of both text and voice naturalness on user engagement. The findings reveal that text naturalness exerts a negative influence on user engagement, whereas voice naturalness yields a positive impact. Moreover, anthropomorphic appearance (i.e., appearance resembling human-like features) mitigates the negative effect of text naturalness and amplifies the positive effect of voice naturalness on user engagement. Additionally, the impact of language naturalness on user engagement exhibits heterogeneity.

Suggested Citation

  • Zhang, Ruoxi & Fang, Jiaming & Han, Lintong & Liao, Miyan, 2026. "Is more human-like better? The impact of virtual streamer language naturalness on user engagement," Technological Forecasting and Social Change, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:tefoso:v:222:y:2026:i:c:s0040162525004238
    DOI: 10.1016/j.techfore.2025.124392
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