An Empirical Study on Patients' Acceptance and Resistance Towards Electronic Health Record Sharing System: A Case Study of Hong Kong

Kin Lok Keung, Carman Lee, K.K.H. Ng, Sing Sum Leung and K.L. Choy
Additional contact information
Kin Lok Keung: The Hong Kong Polytechnic University, Hung Hom, Hong Kong
Carman Lee: The Hong Kong Polytechnic University, Hung Hom, Hong Kong
K.K.H. Ng: The Hong Kong Polytechnic University, Hung Hom, Hong Kong
Sing Sum Leung: The Hong Kong Polytechnic University, Hung Hom, Hong Kong
K.L. Choy: The Hong Kong Polytechnic University, Hung Hom, Hong Kong

International Journal of Knowledge and Systems Science (IJKSS), 2018, vol. 9, issue 2, 1-27

Abstract: This article aims at identifying significant factors influencing behavioural intention and resistance of patients toward electronic health record sharing systems by using PLS-SEM. A questionnaire was selected as the major data collection method and 243 responses were collected. Thus, this paper reviewed different theoretical models to illustrate the factors which influence the behavioural intention of patients towards the usage of the system and to identify the most important factors for acceptance and resistance of patients' respectively. The responses were then divided into two groups, specialist patients and normal patients, which had the common factors including performance expectancy and effort expectancy. For specialist patients, transition costs were identified as the only factor significantly affecting resistance to use. For normal patients, sunk costs and regret avoidance were found to be positively correlated with resistance to using of normal patients.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 018/IJKSS.2018040101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jkss00:v:9:y:2018:i:2:p:1-27

Access Statistics for this article

International Journal of Knowledge and Systems Science (IJKSS) is currently edited by Van Nam Huynh

More articles in International Journal of Knowledge and Systems Science (IJKSS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().