Intention of Continuing to use the Hospital Information System: Integrating the elaboration-likelihood, social influence and cognitive learning

  • Farzandipour M
  • Mohamadian H
  • Sohrabi N
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Abstract

INTRODUCTION Anticipating effective factors in information system acceptance by using persuasive messages, is one of the main issues less focused on so far. This is one of the first attempts at using the elaboration-likelihood model combined with the perception of emotional, cognitive, self-efficacy, informational and normative influence constructs, in order to investigate the determinants of intention to continue use of the hospital information system in Iran. METHODS The present study is a cross-sectional survey conducted in 2014. 600 nursing staff were chosen from clinical sectors of public hospitals using purposive sampling. The questionnaire survey was in two parts: Part one was comprised of demographic data, and part two included 52 questions pertaining to the constructs of the model in the study. To analyze the data, structural equation model using LISREL 8.5 software was applied. RESULT The findings suggest that self-efficacy (t= 6.01, β= 0.21), affective response (t= 5.84, β= 0.23), and cognitive response (t= 4.97, β= 0.21) explained 64% of the variance for the intention of continuing to use the hospital information system. Furthermore, the final model was able to explain 0.46 for self-efficacy, 0.44 for normative social influence, 0.52 for affective response, 0.55 for informational social influence, and 0.53 for cognitive response. CONCLUSION Designing the necessary mechanisms and effective use of appropriate strategies to improve emotional and cognitive understanding and self-efficacy of the nursing staff is required, in order to increase the intention of continued use of the hospital information system in Iran.

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Farzandipour, M., Mohamadian, H., & Sohrabi, N. (2016). Intention of Continuing to use the Hospital Information System: Integrating the elaboration-likelihood, social influence and cognitive learning. Electronic Physician, 8(12), 3385–3394. https://doi.org/10.19082/3385

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