Exploring the factors that influence physician technostress from using mobile electronic medical records

doi:10.1080/17538157.2017.1364250

CNU IR > College of Smart Living > Dept. of Information Management > Periodical Articles > Item 310902800/32618

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/32618

Title:	Exploring the factors that influence physician technostress from using mobile electronic medical records
Authors:	Chung-Feng Liu(劉忠峰) Cheng, Tain-Junn Chen, Chin-Tung
Contributors:	Chia Nan Univ Pharm & Sci, Dept Informat Management Chi Mei Med Ctr, Dept Neurol Chi Mei Med Ctr, Dept Occupat Med Chia Nan Univ Pharm & Sci, Dept Hosp & Hlth Care Adm Natl Yunlin Univ Sci & Technol, Grad Sch Technol & Vocat Educ
Keywords:	Mobile electronic medical records physicians technology dependency technostress
Date:	2019-01
Issue Date:	2020-07-29 13:52:20 (UTC+8)
Publisher:	TAYLOR & FRANCIS INC
Abstract:	This paper proposes an integrated model for investigating how physicians' perceived individual and technology characteristics affect their technological stress (technostress) that is derived from using mobile electronic medical records (MEMRs). Individual characteristics comprise constructs of mobile self-efficacy and technology dependence, whereas perceived technology characteristics comprise constructs of perceived usefulness, complexity, and reliability. We employed the survey method to collect 158 valid questionnaires from physicians working at three branch hospitals to determine perceptions regarding MEMRs, yielding a response rate of 33.62%. Partial least squares, a structural equation modeling technique, was used for model examination and hypothesis testing. The results show that physicians have a low perception of MEMR dependence and technostress. Furthermore, physicians' perceived MEMR technology dependency, mobile self-efficacy, and complexity were proven to significantly affect physician technostress when using MEMRs, whereas perceived usefulness and reliability were not. The explanatory power of the research model reached 67.8%. The results of this study provide valuable insights and significant knowledge for technostress in health care, particularly from academic and practical perspectives.
Relation:	Informatics For Health & Social Care, v.44, n.1, pp.92-104
Appears in Collections:	[Dept. of Information Management] Periodical Articles

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