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An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

A lot of attention has been given to institutional repositories from scholars in various disciplines and from all over the world as they are considered as a novel and substitute technology for scholarly communication. The purposed study aimed to examine the factors that have an influence on the adoption and intention of the researchers to use institutional repositories. The adoption intention of researchers was assessed using the following factors: attitude, effort expectancy, performance expectancy, social influence, internet self-efficacy and resistance to change. Data for this analysis was obtained from 177 Malaysian researchers and the research model put forward was tested using the multi-analytical approach. The variables that significantly affected institutional repositories adoption was initially determined using structural equation modeling (SEM). The neural network model (NN) was then used to put the comparative impact of significant predictors identified from SEM in order. It was found that the strongest predictors of the intentional to employ institutional repositories were internet self-efficacy and social influence. The findings of this research play an important part in influencing the decision-making of executives by determining and ranking factors through which they are able to identify the way they can promote the use of institutional repositories in their university. In addition, the research outcomes also provide information regarding the most important factors that are vital for formulating an appropriate strategic model to improve adoption of institutional repositories.

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Authors and Affiliations

  1. Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Seri Kembangan, Malaysia

    Shahla Asadi, Rusli Abdullah & Yusmadi Yah Jusoh

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  1. Shahla Asadi
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  2. Rusli Abdullah
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  3. Yusmadi Yah Jusoh
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Correspondence to Shahla Asadi .

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Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Asadi, S., Abdullah, R., Jusoh, Y.Y. (2020). An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_38

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