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Testing students’ e-learning via Facebook through Bayesian structural equation modeling

Author

Listed:
  • Hashem Salarzadeh Jenatabadi
  • Sedigheh Moghavvemi
  • Che Wan Jasimah Bt Wan Mohamed Radzi
  • Parastoo Babashamsi
  • Mohammad Arashi

Abstract

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Suggested Citation

  • Hashem Salarzadeh Jenatabadi & Sedigheh Moghavvemi & Che Wan Jasimah Bt Wan Mohamed Radzi & Parastoo Babashamsi & Mohammad Arashi, 2017. "Testing students’ e-learning via Facebook through Bayesian structural equation modeling," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0182311
    DOI: 10.1371/journal.pone.0182311
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    References listed on IDEAS

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    1. Mahalingam Vasantha & Malaisamy Muniyandi & Chinnaiyan Ponnuraja & Ramalingam Srinivasan & Perumal Venkatesan, 2021. "Bayesian structural equation modeling for post treatment health related quality of life among tuberculosis patients," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-10, May.
    2. Che Wan Jasimah Wan Mohamed Radzi & Hashem Salarzadeh Jenatabadi & Ayed R. A. Alanzi & Mohd Istajib Mokhtar & Mohd Zufri Mamat & Nor Aishah Abdullah, 2019. "Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation," IJERPH, MDPI, vol. 16(3), pages 1-17, February.
    3. Hashem Salarzadeh Jenatabadi & Che Wan Jasimah Bt Wan Mohamed Radzi & Nadia Samsudin, 2020. "Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach," IJERPH, MDPI, vol. 17(14), pages 1-24, July.

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