The adoption of mobile healthcare by hospital's professionals: An integrative perspective

By Ing-Long Wu, Jhao-Yin Li, Chu-Ying Fu. Decision Support Systems, 2011

409 citations recorded by the source corpus.

Subfields: Information Systems and Management, Organizational Behavior and Human Resource Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e4681ae2b567e48b520077514f8838042b785fb2

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In Information Systems and Management

In Organizational Behavior and Human Resource Management

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Citations in the corpus

Comparison of the middle-aged and older users' adoption of mobile health services in China (2014, methodology): “The theory of planned behavior (TPB) and its extensions are widely used in adoption research [20,27,28].”
Using the Technology Acceptance Model in Understanding Academics' Behavioural Intention to Use Learning Management Systems (2014, methodology): “Questionnaire To ensure content validity, the questionnaire used in this study was adapted from the original measurement scales used in TAM[13] and from other literatures[31, 55, 59, 62] with some modifications and the necessary wording changes and validation to fit the context of LMS usage.”
Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India (2020, positive): “…are several studies where it is seen that PEU has significant links with the intention of the users to use the new technology, for example, in the use of new technology (Wu, Li, & Fu, 2011), it is noted that PEU has a positive relation with the ultimate use of the new technology (Kim & Chiu, 2019).”
What catalyses mobile apps usage intention: an empirical analysis (2015, methodology): “In accordance to Wu et al. (2011), PLS-SEM places minimal demands on the size of the sample as well as the distributions of the residuals.”
“Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms” (2023, background): “, 2014), virtual learning (Raaij & Schepers, 2008), and healthcare technologies (Fan et al., 2020; Wu et al., 2011).”
Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective (2020, neutral): “Adoption of e-health monitoring using smart wearable healthcare devices has been one of the recent contributions to extant research literature [64]; [86]; [123]; [132].”
Determinants of end-user acceptance of biometrics: Integrating the "Big 3" of technology acceptance with privacy context (2013, background): “TAM proposes perceived usefulness (PU) [4, 12, 95] and perceived ease of use (PEOU) determine intention to accept a technology [21, 47].”
The privacy-personalization paradox in mHealth services acceptance of different age groups (2016, background): “By integrating TAM and TPB and examining both their technological and organizational aspects, Wu et al. (2011) studied how healthcare professionals adopt mobile healthcare services.”
Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS) (2018, methodology): “Another example was provided by Wu et al. (2011) where TAM and TPB were integrated to explore hospital’s professionals’ adoption towards mobile health services.”