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Investigating the Predictors of Telemedicine Service Usage Intention in China During the COVID-19 Pandemic: An Extended Technology Acceptance Perspective.

Wanqi GongJiawei Liu
Published in: Telemedicine journal and e-health : the official journal of the American Telemedicine Association (2023)
Introduction: In China, digital health and telemedicine services grew particularly rapidly during the COVID-19 pandemic. The goal of this study was to examine the effects of technology acceptance model (TAM) predictors, previous social media health service exposure, and telemedicine experience on individual telemedicine service usage intention within the extended theoretical framework of TAM and TAM2. Methods: The study adopted a cross-sectional survey to collect data ( N  = 1,088) through a Chinese online panel provider (wenjuan.com). Structural equation modeling was performed to examine relationships between the variables in the proposed model. Results: Our results indicated that technology anxiety (TA) was negatively related to perceived ease of use (PEOU) and usage intention. PEOU mediated the relationship between TA and usage intention. Social media health information consumption was positively associated with perceived usefulness (PU). Previous telemedicine satisfaction was positively related to PEOU and PU, but the direct relationship between satisfaction with telemedicine and usage intention was not found to be significant. Besides, PEOU and PU mediated the relationship between previous telemedicine satisfaction and usage intention. Conclusions: Findings of the study not only contribute to literature pertaining to telemedicine promotion by identifying important mediation relationships but also help identify potential users and provide a convenient internet-based promotion channel since they reveal that social media health information consumption is positively related to PU of telemedicine services.
Keyphrases
  • social media
  • health information
  • healthcare
  • mental health
  • social support
  • depressive symptoms
  • risk assessment
  • climate change
  • artificial intelligence
  • human health