THE TECHNOLOGY ACCEPTANCE MODEL: ITS PAST AND ITS FUTURE IN HEALTH CARE

By R. Holden, Ben-Tzion Karsh. Journal of Biomedical Informatics, 2009

2,538 citations recorded by the source corpus.

Increasing interest in end users’ reactions to health information technology (IT) has elevated the importance of theories that predict and explain health IT acceptance and use. This paper reviews the application of one such theory, the Technology Acceptance Model (TAM), to health care. We reviewed 16 data sets analyzed in over 20 studies of clinicians using health IT for patient care. Studies differed greatly in samples and settings, health ITs studied, research models, relationships tested, and construct operationalization. Certain TAM relationships were consistently found to be significant, whereas others were inconsistent. Several key relationships were infrequently assessed. Findings show that TAM predicts a substantial portion of the use or acceptance of health IT, but that the theory may benefit from several additions and modifications. Aside from improved study quality, standardization, and theoretically motivated additions to the model, an important future direction for TAM is to adapt the model specifically to the health care context, using beliefs elicitation methods.

Subfields: General Health Professions, Information Systems and Management, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/ab3b68b70039cab9ca723afbdafa48109616bae3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Health Professions

1. S. Reddy
2. Calvin K. L. Or
3. Roger L. Brown
4. P. Brennan
5. Dolores J. Severtson
6. Laura J. Burke
7. Ben-Tzion Karsh

In Information Systems and Management

1. S. Reddy
2. Calvin K. L. Or
3. Ben-Tzion Karsh

In Organizational Behavior and Human Resource Management

1. S. Reddy
2. Calvin K. L. Or
3. Ben-Tzion Karsh
4. Michael A. Talias

Citations in the corpus

• Technology acceptance among pre-service teachers: Does gender matter? (2015, neutral): “…contexts, such as digital library system (Park, Roman, Lee, & Chung, 2009), business management (Hernández, Jiménez, & Martín, 2008 ), health care (Holden & Karsh, 2010), e-shopping (Ha & Stoel, 2009), internet usage (Kim, Park & Lee, 2007), social networks (Hossain & Silva, 2009), in-service…”
• Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT (2015, neutral): “Often these links have proved to be significant when measured among the various types of health professionals affected when an IT is adopted [15].”
• Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, methodology): “In addition to these factors, the model also suggests that external factors like social influence and facilitating conditions can influence the acceptance and use of a new technology [57, 59].”
• Factors affecting home care patients' acceptance of a web-based interactive self-management technology (2011, methodology): “which has been used in over a dozen studies modeling healthcare providers' acceptance of health information technology,”
• Digital Transformation in Healthcare: Technology Acceptance and Its Applications (2023, background): “However, the reason for their research was the study of Holden and Karsh (2010) [306].”