Literature DB >> 18675583

Using a modified technology acceptance model in hospitals.

Vassilios P Aggelidis1, Prodromos D Chatzoglou.   

Abstract

PURPOSE: The use of information technology in the health care sector and especially in hospitals offers great potential for improving the quality of services provided and the efficiency and effectiveness of the personnel, but also for reducing the organizational expenses. However, the main question that arises according to the literature is whether hospital personnel are willing to use state of the art information technology while performing their tasks. This study attempts to address this issue by developing and testing a modified technology acceptance model taking into consideration other relevant models found in the literature.
METHOD: The original TAM has been extended to include some exogenous variables in order to examine HIS acceptance by Greek hospital personnel. Correlation, explanatory and confirmation factor analysis was performed to test the reliability and validity of the measurement model. The structural equation modeling technique has also been used to evaluate the causal model.
RESULTS: The results indicate that perceived usefulness, ease of use, social influence, attitude, facilitating conditions and self-efficacy significantly affect hospital personnel behavioral intention. Training has a strong indirect impact on behavioral intention through the mediators of facilitating condition and ease of use. Furthermore, the existence of significant positive effects between self-efficacy and social influence, perceived usefulness and anxiety, and facilitating conditions and social influence is also supported.
CONCLUSIONS: The proposed model can explain 87% of the variance of behavioral intention indicating that the core constructs of the technology acceptance models have a strong and statistically significant influence on hospital personnel usage intention.

Entities:  

Mesh:

Year:  2008        PMID: 18675583     DOI: 10.1016/j.ijmedinf.2008.06.006

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  57 in total

1.  Adding intelligence to mobile asset management in hospitals: the true value of RFID.

Authors:  Linda Castro; Elisabeth Lefebvre; Louis A Lefebvre
Journal:  J Med Syst       Date:  2013-08-13       Impact factor: 4.460

2.  Understanding the mediating effects of relationship quality on technology acceptance: an empirical study of e-appointment system.

Authors:  Shih-Chih Chen; Shih-Chi Liu; Shing-Han Li; David C Yen
Journal:  J Med Syst       Date:  2013-10-19       Impact factor: 4.460

3.  Understanding healthcare providers' perceptions of a personal assistant robot.

Authors:  Tracy L Mitzner; Lorenza Tiberio; Charles C Kemp; Wendy A Rogers
Journal:  Gerontechnology       Date:  2018-03

4.  Determinants of RFID adoption in Malaysia's healthcare industry: occupational level as a moderator.

Authors:  Suhaiza Zailani; Mohammad Iranmanesh; Davoud Nikbin; Jameson Khoo Cheong Beng
Journal:  J Med Syst       Date:  2014-12-11       Impact factor: 4.460

5.  A Systematic Review of the Technology Acceptance Model in Health Informatics.

Authors:  Bahlol Rahimi; Hamed Nadri; Hadi Lotfnezhad Afshar; Toomas Timpka
Journal:  Appl Clin Inform       Date:  2018-08-15       Impact factor: 2.342

6.  Determinants of telemedicine acceptance in selected public hospitals in Malaysia: clinical perspective.

Authors:  Suhaiza Zailani; Mina Sayyah Gilani; Davoud Nikbin; Mohammad Iranmanesh
Journal:  J Med Syst       Date:  2014-07-20       Impact factor: 4.460

7.  Factors Affecting Acceptance of Hospital Information Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments.

Authors:  Hamed Nadri; Bahlol Rahimi; Hadi Lotfnezhad Afshar; Mahnaz Samadbeik; Ali Garavand
Journal:  Appl Clin Inform       Date:  2018-04-04       Impact factor: 2.342

Review 8.  The technology acceptance model: its past and its future in health care.

Authors:  Richard J Holden; Ben-Tzion Karsh
Journal:  J Biomed Inform       Date:  2009-07-15       Impact factor: 6.317

9.  The Determinants of M-Health Adoption in Developing Countries: An Empirical Investigation.

Authors:  Ahmad Alaiad; Mohammad Alsharo; Yazan Alnsour
Journal:  Appl Clin Inform       Date:  2019-10-30       Impact factor: 2.342

10.  Acceptance of technology-enhanced learning for a theoretical radiological science course: a randomized controlled trial.

Authors:  Emeka Nkenke; Elefterios Vairaktaris; Anne Bauersachs; Stephan Eitner; Alexander Budach; Christoph Knipfer; Florian Stelzle
Journal:  BMC Med Educ       Date:  2012-03-30       Impact factor: 2.463

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