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A Systematic Review for Predictive Models of IS Adoption

Author

Listed:
  • Rhouma Naceur

    (Laval University, Canada)

  • Yan Cimon

    (Laval University, Canada)

  • Robert Pellerin

    (École Polytechnique de Montréal, Canada)

Abstract

The implementation of a new information system could be a risky decision for any company. In fact, many implementation decisions fail. Studying the success of IS adoption is necessary to identify the factors that impact success and to prevent risk. Many predictive algorithms and models have been used in order evaluate the IS adoption. This paper surveys the relevant predictive models that have been used in this area in the past 20 years. The authors aim to focus on information system adoption, as well as existing adoption models and theory, to put forth a state of the art survey on the issue to further understand the predictive models behind a successful adoption. Therefore, this paper opted for a systematic review to identify all of the articles that study IS adoption and that are using or suggesting a predictive model.

Suggested Citation

  • Rhouma Naceur & Yan Cimon & Robert Pellerin, 2021. "A Systematic Review for Predictive Models of IS Adoption," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 17(1), pages 1-21, January.
    • Handle: RePEc:igg:jeis00:v:17:y:2021:i:1:p:1-21
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    Cited by:

    1. Yujong Hwang & Hui Lin & Donghee Shin, 2023. "An Empirical Study on the Information Formality Motivation, Social Influence, and Goal Commitment of Knowledge Workers," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 19(1), pages 1-17, January.

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