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Service robots in healthcare: Toward a healthcare service robot acceptance model (sRAM)

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  • Lim, Weng Marc
  • Jasim, K. Mohamed
  • Malathi, A.

Abstract

Our research examines the perceptions and intentions surrounding the use of healthcare service robots. Guided by service robot acceptance model (sRAM) and stimulus-organism-response (S-O-R) model, we explore how perceptions of functional, social, emotional, and robotic features of service robots shape their trust and use in healthcare. Our research incorporated data from 398 responses collected via an online questionnaire, which was analyzed using partial least squares structural equation modeling (PLS-SEM) through the SmartPLS software, revealing that functional (ease of use), emotional (anxiety and enjoyment), and social (social interactivity and presence) features significantly influence healthcare service robots trust and use. Contrarily, usefulness—a functional feature—had no significant role in shaping healthcare service robots trust and use. Nevertheless, trust mediated perceptions relating to anxiety, ease of use, enjoyment, social interactivity, and social presence with healthcare service robots use. Interestingly, anthropomorphism—a robotic feature—had no moderating effect while subjective norms—a non-robotic feature—only moderated the impact of social interactivity on healthcare service robots use. Conclusively, our research organizes sRAM antecedents into clear, discrete categories (functional, emotional, and social) and delivers a comprehensive, structured acceptance model. This new and novel model supports systematic theory development and comparability in healthcare service robot research while also offering critical implications for enhancing the integration and utilization of service robots within healthcare.

Suggested Citation

  • Lim, Weng Marc & Jasim, K. Mohamed & Malathi, A., 2025. "Service robots in healthcare: Toward a healthcare service robot acceptance model (sRAM)," Technology in Society, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:teinso:v:82:y:2025:i:c:s0160791x25001228
    DOI: 10.1016/j.techsoc.2025.102932
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