An extension of the Expectation Confirmation Model (ECM) to study continuance behavior in using e-Health services

  • Received January 28, 2020;
    Accepted March 26, 2020;
    Published April 10, 2020
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  • Article Info
    Volume 16 2020, Issue #2, pp. 15-28
  • Cited by
    16 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

Given the negative utilitarianism and difficulty in maintaining long-term loyalty, hospitals resort to a variety of images that define and redefine their relationship strategies in order to stay patient-centric. As in any other sector, in healthcare, patients play an important role in service design and delivery. The basic services of medical appointment scheduling, online payment and health information search are recognized as one of the most important elements that increase patient footfall, service planning, patient satisfaction and their continued usage, in particular in developing economies such as India. This study seeks to understanding the basic e-Health services continuance usage intention among patients by integrating the Expectation Confirmation Model (ECM) and the Technology Acceptance Model (TAM) and extending them by including certain external variables. With a well-structured questionnaire, a survey of 453 respondents – out-patients and care-givers, who should have used e-Health services at least once, in particular, visited multispecialty hospitals, revealed that along with the ECM and TAM constructs such as satisfaction, confirmation, perceived ease-of-use, and perceived usefulness, the external variables such as trust, social influence, perceived service quality, and perceived privacy and security had a significant influence (p < 0.05) on e-Health services continuance usage. The main findings of the study contribute to developing and empirically testing a model that explains the basic process of motivating the e-Health service users for continuance usage intention.

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    • Figure 1. Research framework of the study
    • Table 1. Demographic profile of respondents
    • Table 2. Correlation among study variables
    • Table 3. Confirmatory factor analysis results
    • Table 4. Discriminant analysis
    • Table 5. Structural path coefficients
    • Data curation
      K Anil Kumar
    • Investigation
      K Anil Kumar
    • Software
      K Anil Kumar
    • Validation
      K Anil Kumar
    • Writing – original draft
      K Anil Kumar
    • Conceptualization
      Subhashree Natarajan
    • Formal Analysis
      Subhashree Natarajan
    • Methodology
      Subhashree Natarajan
    • Project administration
      Subhashree Natarajan
    • Supervision
      Subhashree Natarajan
    • Writing – review & editing
      Subhashree Natarajan