Literature DB >> 17644029

An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry.

Feng-Cheng Tung1, Su-Chao Chang, Chi-Min Chou.   

Abstract

PURPOSE: Ever since National Health Insurance was introduced in 1995, the number of insurants increased to over 96% from 50 to 60%, with a continuous satisfaction rating of about 70%. However, the premium accounted for 5.77% of GDP in 2001 and the Bureau of National Health Insurance had pressing financial difficulties, so it reformed its expenditure systems, such as fee for service, capitation, case payment and the global budget system in order to control the rising medical costs. Since the change in health insurance policy, most hospitals attempted to reduce their operating expenses and improve efficiency. Introducing the electronic logistics information system is one way of reducing the cost of the department of central warehouse and the nursing stations. Hence, the study proposes a technology acceptance research model and examines how nurses' acceptance of the e-logistics information system has been affected in the medical industry.
METHODS: This research combines innovation diffusion theory, technology acceptance model and added two research parameters, trust and perceived financial cost to propose a new hybrid technology acceptance model. Taking Taiwan's medical industry as an experimental example, this paper studies nurses' acceptance of the electronic logistics information system. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model.
RESULTS: The results of the survey strongly support the new hybrid technology acceptance model in predicting nurses' intention to use the electronic logistics information system.
CONCLUSION: The study shows that 'compatibility', 'perceived usefulness', 'perceived ease of use', and 'trust' all have great positive influence on 'behavioral intention to use'. On the other hand 'perceived financial cost' has great negative influence on behavioral intention to use.

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Year:  2007        PMID: 17644029     DOI: 10.1016/j.ijmedinf.2007.06.006

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


  41 in total

1.  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

2.  Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health: A Cross-sectional Survey.

Authors:  K Gartrell; A M Trinkoff; C L Storr; M L Wilson; A P Gurses
Journal:  Appl Clin Inform       Date:  2015-04-08       Impact factor: 2.342

3.  Prioritizing barriers to successful implementation of hospital information systems.

Authors:  Leila Ahmadian; Reza Khajouei; Simin Salehi Nejad; Maryam Ebrahimzadeh; Somayeh Ezhari Nikkar
Journal:  J Med Syst       Date:  2014-11-04       Impact factor: 4.460

4.  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

5.  Assessing the relationship between technology readiness and continuance intention in an E-appointment system: relationship quality as a mediator.

Authors:  Shih-Chih Chen; Din Jong; Min-Tsai Lai
Journal:  J Med Syst       Date:  2014-07-10       Impact factor: 4.460

6.  Examining construct and predictive validity of the Health-IT Usability Evaluation Scale: confirmatory factor analysis and structural equation modeling results.

Authors:  Po-Yin Yen; Karen H Sousa; Suzanne Bakken
Journal:  J Am Med Inform Assoc       Date:  2014-02-24       Impact factor: 4.497

7.  Applying Electronic Medical Records in Health Care: Physicians' Perspective.

Authors:  Mohammadhiwa Abdekhoda; Maryam Ahmadi; Afsaneh Dehnad; Alireza Noruzi; Mahmodreza Gohari
Journal:  Appl Clin Inform       Date:  2016-05-11       Impact factor: 2.342

8.  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

9.  The Design and Validation of a Child Developmental e-Screening System.

Authors:  Hsin-Yi Kathy Cheng; Hsien-Tsung Chang; Po-Hsin Huang; Yan-Ying Ju; Li-Ying Chen; Kevin C Tseng
Journal:  J Med Syst       Date:  2017-03-10       Impact factor: 4.460

Review 10.  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

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