Predicting the intention to use social media sites : a hybrid SEM - machine learning approach

Salloum, SA ORCID: https://orcid.org/0000-0002-6073-3981, AlAhbabi, NMN ORCID: https://orcid.org/0000-0002-1704-1920, Habes, M ORCID: https://orcid.org/0000-0003-3790-7303, Aburayya, A ORCID: https://orcid.org/0000-0002-1428-0547 and Akour, I ORCID: https://orcid.org/0000-0002-6914-2213 2021, Predicting the intention to use social media sites : a hybrid SEM - machine learning approach , in: International Conference on Advanced Machine Learning Technologies and Applications, 20th-22nd March 2021, Cairo, Egypt.

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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.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Advanced Machine Learning Technologies and Applications : proceedings of AMLTA 2021
Publisher: Springer
Series Name: Advances in Intelligent Systems and Computing
ISBN: 9783030697167 (print); 9783030697174 (ebook)
ISSN: 2194-5357
Related URLs:
Depositing User: USIR Admin
Date Deposited: 22 Jun 2021 13:08
Last Modified: 22 Jun 2021 13:08
URI: http://usir.salford.ac.uk/id/eprint/61008

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