Testing students' e-learning via Facebook through Bayesian structural equation modeling.
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/m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182311&type=printable |
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| _version_ | 1850076655662399488 |
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| author | Hashem Salarzadeh Jenatabadi Sedigheh Moghavvemi Che Wan Jasimah Bt Wan Mohamed Radzi Parastoo Babashamsi Mohammad Arashi |
| author_facet | Hashem Salarzadeh Jenatabadi Sedigheh Moghavvemi Che Wan Jasimah Bt Wan Mohamed Radzi Parastoo Babashamsi Mohammad Arashi |
| author_sort | Hashem Salarzadeh Jenatabadi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-67c2da8746b24cd68390268dccab5a4a |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-67c2da8746b24cd68390268dccab5a4a2025-08-20T02:45:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018231110.1371/journal.pone.0182311Testing students' e-learning via Facebook through Bayesian structural equation modeling.Hashem Salarzadeh JenatabadiSedigheh MoghavvemiChe Wan Jasimah Bt Wan Mohamed RadziParastoo BabashamsiMohammad ArashiLearning 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182311&type=printable |
| spellingShingle | Hashem Salarzadeh Jenatabadi Sedigheh Moghavvemi Che Wan Jasimah Bt Wan Mohamed Radzi Parastoo Babashamsi Mohammad Arashi Testing students' e-learning via Facebook through Bayesian structural equation modeling. PLoS ONE |
| title | Testing students' e-learning via Facebook through Bayesian structural equation modeling. |
| title_full | Testing students' e-learning via Facebook through Bayesian structural equation modeling. |
| title_fullStr | Testing students' e-learning via Facebook through Bayesian structural equation modeling. |
| title_full_unstemmed | Testing students' e-learning via Facebook through Bayesian structural equation modeling. |
| title_short | Testing students' e-learning via Facebook through Bayesian structural equation modeling. |
| title_sort | testing students e learning via facebook through bayesian structural equation modeling |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182311&type=printable |
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