Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The increasing adoption of healthcare devices necessitates a deeper understanding of the factors that influence user acceptance in this rapidly evolving area. Therefore, this study examined the factors influencing the technology acceptance of healthcare devices, focusing on radar sensors and wearable devices. A total of 1158 valid responses were used to test hypotheses, mediation, and moderation effects using SmartPLS 4.0. The results highlighted the significant role of performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived risk in shaping user attitudes and trust, which in turn influence behavioral intention. The findings suggested that attitudes fully mediate the effects of performance expectancy and effort expectancy on behavioral intention, while social influence, facilitating conditions, and perceived risk exhibit partial mediation. Moderation analysis revealed significant effects of generation on the relationship between attitude, trust, and behavioral intention. Additionally, device type moderated the effect of trust on behavioral intention, showing a different influence between radar sensors and wearable devices. These findings provide theoretical contributions by extending the unified theory of acceptance and use of technology (UTAUT) model and offering practical implications for manufacturers and policymakers to tailor strategies that foster positive attitudes, enhance trust, and address generational and device-specific differences in healthcare technology adoption.
Download full-text PDF |
Source |
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http://dx.doi.org/10.3390/s24247921 | DOI Listing |
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