User Adoption Intentions Toward Medical Escort Platforms: An Integrated Technology Adoption Framework Based on Task–Technology Fit and the Extended Technology Acceptance Model

Amid the rapid evolution of digital health services, medical escort platforms that integrate healthcare delivery and digital technology are increasingly vital for enhancing the user experience and improving healthcare accessibility.To investigate the adoption mechanisms of such platforms, this study integrates Task-Technology Fit (TTF) theory and the Extended Technology Acceptance Model (ETAM) to develop a multi-path model of behavioral intention.Empirical validation was conducted using 522 valid samples collected in China.The results showed that Task-Technology Fit has a significant direct effect on usage intention and exerts notable indirect effects via cognitive variables, including perceived usefulness, ease of use, professionalism, and security.Perceived usefulness, as a core construct of ETAM, is a central mediating node across multiple paths, demonstrating chained and straightforward mediation mechanisms.Additionally, perceived professionalism and perceived security, introduced as contextspecific extensions, significantly enhance users' perceived value and adoption intention, as influenced by service reliability and technological safety.These findings extended the theoretical boundaries and contextual applicability of Technology Acceptance Models within multitasking, privacy-intensive healthcare environments.This study revealed how task-technology fit influences user decision-making through multiple cognitive pathways.It deepens the explanatory scope of technology-cognitionbehavior models.It also offers systematic insights for platform optimization, service design, regulatory governance, and inclusive digital healthcare.