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Examining Readiness of E-Learning Implementation using Information System Readiness Impact Model

Authors: Esa Firmansyah; Dody Herdiana; Dwi Yuniarto;

Examining Readiness of E-Learning Implementation using Information System Readiness Impact Model

Abstract

This study aims to test the readiness model and the impact of integrated information systems by assessing the impact of using e-learning during the COVID-19 pandemic. Based on data from e-learning users collected through the survey obtained, structural equation models and path analysis were used to test the research model. The sample consisted of e-learning users from rural campuses. In this case, the students of STMIK Sumedang and UIN Bandung. The research sample was obtained by distributing questionnaires through the WA Group and email. As many as 169 respondents filled out the questionnaire and stated that they were ready to use e-learning. The results showed that optimism and innovation had a positive effect on system use. The quality of the system and the quality of information are the main factors driving the use of the system. Also, this study tries to provide a literature review of the latest research published in the field of information systems, especially in rural areas.

Subjects by Vocabulary

Microsoft Academic Graph classification: Knowledge management Coronavirus disease 2019 (COVID-19) business.industry media_common.quotation_subject Information quality Structural equation modeling Data modeling Optimism Information system Rural area Path analysis (statistics) business Psychology media_common

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Related to Research communities
COVID-19
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