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Facebook/Meta usage in higher education: A deep learning‐based dual‐stage SEM‐ANN analysis

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The paper’s main aim is to investigate and predict major factors in students’ behav- ioral intentions toward academic use of Facebook/Meta as a virtual classroom, tak- ing into account its adoption level, purpose, and education usage. In contrast to ear- lier social network research, this one utilized a novel technique that comprised a two-phase analysis and an upcoming the Artificial Neural Network (ANN) analy- sis approach known as deep learning was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). This study has con- firmed that perceived task-technology fit is the most affirmative and meaningful effect on Facebook/Meta usage in higher education. Moreover, facilitating condi- tions, collaboration, subjective norms, and perceived ease of use has strong influ- ence on Facebook usage in higher education. The study’s findings can be utilized to improve the usage of social media tools for teaching and learning, such as Facebook/ Meta. There is a discussion of both theoretical and practical implications.
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Vol.:(0123456789)
https://doi.org/10.1007/s10639-022-11012-9
1 3
Facebook/Meta usage inhigher education: Adeep
learning‑based dual‑stage SEM‑ANN analysis
YakupAkgül1 · AliOsmanUymaz2
Received: 19 November 2021 / Accepted: 18 March 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2022
Abstract
The paper’s main aim is to investigate and predict major factors in students’ behav-
ioral intentions toward academic use of Facebook/Meta as a virtual classroom, tak-
ing into account its adoption level, purpose, and education usage.In contrast to ear-
lier social network research, this one utilized a novel technique that comprised a
two-phase analysis and an upcoming the Artificial Neural Network (ANN) analy-
sis approach known as deep learning was engaged to sort out relatively significant
predictors acquired from Structural Equation Modeling (SEM). This study has con-
firmed that perceived task-technology fit is the most affirmative and meaningful
effect on Facebook/Meta usage in higher education. Moreover, facilitating condi-
tions, collaboration, subjective norms, and perceived ease of use has strong influ-
ence on Facebook usage in higher education. The study’s findings can be utilized to
improve the usage of social media tools for teaching and learning, such as Facebook/
Meta. There is a discussion of both theoretical and practical implications.
Keywords Facebook/Meta· Social media· Social networking sites· Structural
equation modeling· Artificial Neural network· Deep Learning· Higher education·
Online learning Turkey
* Yakup Akgül
yakup.akgul@alanya.edu.tr
Ali Osman Uymaz
ali.uymaz@alanya.edu.tr
1 Department ofBusiness, Faculty ofEconomics, Faculty ofEconomics, Administrative
andSocial Sciences, Alanya Alaaddin Keykubat University, Alanya,Antalya07425, Kestel,
Turkey
2 Department ofHuman Resources Management, Faculty ofEconomics, Administrative
andSocial Sciences, Alanya Alaaddin Keykubat University, Alanya,Antalya07425, Kestel,
Turkey
Education and Information Technologies (2022) 27:9821–9855
/ Published online: 5 April 2022
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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