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

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  • 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)

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.

Suggested Citation

  • Kin Lok Keung & Carman Lee & K.K.H. Ng & Sing Sum Leung & K.L. Choy, 2018. "An Empirical Study on Patients' Acceptance and Resistance Towards Electronic Health Record Sharing System: A Case Study of Hong Kong," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 9(2), pages 1-27, April.
    • Handle: RePEc:igg:jkss00:v:9:y:2018:i:2:p:1-27
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    Cited by:

    1. Piyanuch Arunrukthavon & Dittapong Songsaeng & Chadaporn Keatmanee & Songphon Klabwong & Mongkol Ekpanyapong & Matthew N. Dailey, 2022. "Diagnostic Performance of Artificial Intelligence for Interpreting Thyroid Cancer in Ultrasound images," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 13(1), pages 1-13, January.
    2. Houji Zhong & Yuanyuan Wang & Wuyi Yue, 2022. "An E-Commerce Product Recommendation Method Based on Visual Search and Customer Satisfaction," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 13(1), pages 1-14, January.
    3. Leung, Polly P.L. & Wu, C.H. & Kwong, C.K. & Ip, W.H. & Ching, W.K., 2021. "Digitalisation for optimising nursing staff demand modelling and scheduling in nursing homes," Technological Forecasting and Social Change, Elsevier, vol. 164(C).

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