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Predicting the Intention to Use Social Media Sites: A Hybrid SEM - Machine Learning Approach

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

Abstract

The study conducted aims to form a conceptual model to calculate the pupils’ acceptance of social media in education and its factors. Although the amount of research done on the acceptance of social media applications has amplified, the factors affecting its acceptance for learning are not recognized. The study is carried out by extending the Technology Acceptance Model (TAM) using perceived playfulness and social influence. Alongside this, the collected data is evaluated through Machine Learning (ML) approaches and the partial least squares-structural equation modeling (PLS-SEM). A total of 369 students enrolled at highly regarded universities in the United Arab Emirates (UAE) filled out questionnaire surveys, then analyzed, and results are stated. This research suggests that students’ intention to adopt social media networks in learning is due to significant factors such as perceived playfulness, social influence, perceived usefulness, and perceived ease of use.

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Author information

Authors and Affiliations

  1. School of Science, Engineering, and Environment, University of Salford, Salford, UK

    Said A. Salloum

  2. Machine Learning and NLP Research Group, Department of Computer Science, University of Sharjah, Sharjah, UAE

    Said A. Salloum

  3. Ministry of Education, Dubai, UAE

    Nafla Mahdi Nasser AlAhbabi

  4. Faculty of Mass Communication - Department of Radio and Television, Yarmouk University, Irbid, Jordan

    Mohammed Habes

  5. Quality and Corporate Development Office, Dubai Health Authority, Dubai, UAE

    Ahmad Aburayya

  6. Information Systems Department, University of Sharjah, Sharjah, UAE

    Iman Akour

Authors
  1. Said A. Salloum
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  2. Nafla Mahdi Nasser AlAhbabi
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  3. Mohammed Habes
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  4. Ahmad Aburayya
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  5. Iman Akour
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Corresponding author

Correspondence to Said A. Salloum .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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Salloum, S.A., AlAhbabi, N.M.N., Habes, M., Aburayya, A., Akour, I. (2021). Predicting the Intention to Use Social Media Sites: A Hybrid SEM - Machine Learning Approach. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_32

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