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Examining the Acceptance of WhatsApp Stickers Through Machine Learning Algorithms

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Recent Advances in Intelligent Systems and Smart Applications

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 295))

Abstract

WhatsApp stickers are gaining popularity among university students due to their pervasiveness, specifically in educational WhatsApp groups. However, the acceptance of stickers by university students is still in short supply. Thus, this research aims to empirically examine the determinants affecting the acceptance of WhatsApp stickers through a proposed theoretical model by integrating the technology acceptance model (TAM) with the uses and gratifications theory (U&G). A questionnaire survey was circulated to collect data from 372 university students who have been engaged in a “Group Talk” in WhatsApp. A novel approach was employed to analyze the hypothesized relationships among the constructs in the research model through the use of machine learning algorithms. The results pointed out that IBk and RandomForest classifiers have performed better than the other classifiers in predicting the actual use of stickers with an accuracy of 78.57%. The research findings are believed to provide future directions for stickers developers to better promote stickers in educational activities.

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Acknowledgements

This is an extended version of a conference paper published by the International Conference on Advanced Intelligent Systems and Informatics 2019.

Author information

Authors and Affiliations

  1. Department of English Language, Al Buraimi University College, Al Buraimi, Oman

    Rana A. Al-Maroof

  2. Department of Computer Education and Instructional Technology, Tokat Gaziosmanpasa University, Tokat, Turkey

    Ibrahim Arpaci

  3. Department of Information Technology, Al Buraimi University College, Al Buraimi, Oman

    Mostafa Al-Emran

  4. Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, UAE

    Said A. Salloum

  5. Faculty of Engineering and IT, The British University in Dubai, Dubai, UAE

    Khaled Shaalan

Authors
  1. Rana A. Al-Maroof
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  2. Ibrahim Arpaci
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  3. Mostafa Al-Emran
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  4. Said A. Salloum
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  5. Khaled Shaalan
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Corresponding author

Correspondence to Rana A. Al-Maroof .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Al Buraimi University College, Al Buraimi, Oman

    Mostafa Al-Emran

  2. Faculty of Engineering and IT, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Al-Maroof, R.A., Arpaci, I., Al-Emran, M., Salloum, S.A., Shaalan, K. (2021). Examining the Acceptance of WhatsApp Stickers Through Machine Learning Algorithms. In: Al-Emran, M., Shaalan, K., Hassanien, A. (eds) Recent Advances in Intelligent Systems and Smart Applications. Studies in Systems, Decision and Control, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-47411-9_12

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