Hybrid SEM-ANN model for predicting undergraduates’ e-learning continuance intention based on perceived educational and emotional support
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| Vydáno v: | PLoS One vol. 19, no. 12 (Dec 2024), p. e0308630 |
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Public Library of Science
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MARC
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| 024 | 7 | |a 10.1371/journal.pone.0308630 |2 doi | |
| 035 | |a 3144310256 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Xu, Shanshan | |
| 245 | 1 | |a Hybrid SEM-ANN model for predicting undergraduates’ e-learning continuance intention based on perceived educational and emotional support | |
| 260 | |b Public Library of Science |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university students’ continuance intention to engage in e-learning. Researchers conducted a survey using structured questionnaires among 368 university students from three universities in Jiangxi Province. They measured their self-reported responses on six constructs: perceived educational support, perceived emotional support, perceived usefulness, confirmation, satisfaction, and continuance intention. The relationships between predictors and continuance intention, characterized by non-compensatory and non-linear dynamics, were analyzed using Structural Equation Modeling combined with Artificial Neural Networks. Apart from the direct effects of perceived educational and emotional support on perceived usefulness being non-significant, all other hypotheses were confirmed. Furthermore, according to the normalized importance derived from the multilayer perceptron analysis, satisfaction was identified as the most critical predictor (100%), followed by confirmation (29.9%), perceived usefulness (28.3%), perceived educational support (22.6%), and perceived emotional support (21.6%). These constructs explained 62.1% of the total variance in the students’ continuance intention to engage in e-learning. This study utilized a two-stage analytical approach, enhancing the depth and accuracy of data processing and expanding the methodological scope of research in educational technology. The findings of this study contribute to the United Nations’ Sustainable Development Goal 4, which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030. It provides direction for future research in different environmental and cultural contexts. | |
| 653 | |a Teaching | ||
| 653 | |a Lifelong learning | ||
| 653 | |a College students | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Neural networks | ||
| 653 | |a Technology | ||
| 653 | |a Emotional support | ||
| 653 | |a Educational technology | ||
| 653 | |a University students | ||
| 653 | |a Influence | ||
| 653 | |a Distance learning | ||
| 653 | |a Teachers | ||
| 653 | |a Colleges & universities | ||
| 653 | |a Emotions | ||
| 653 | |a Data processing | ||
| 653 | |a Social support | ||
| 653 | |a Social networks | ||
| 653 | |a Hypotheses | ||
| 653 | |a Online instruction | ||
| 653 | |a Sustainable development | ||
| 653 | |a Technology Acceptance Model | ||
| 653 | |a Self-efficacy | ||
| 653 | |a Learning | ||
| 653 | |a Satisfaction | ||
| 653 | |a Higher education | ||
| 653 | |a Success | ||
| 653 | |a School environment | ||
| 653 | |a Structural equation modeling | ||
| 653 | |a Quality of education | ||
| 653 | |a Feedback | ||
| 653 | |a Students | ||
| 653 | |a Computer assisted instruction--CAI | ||
| 653 | |a Educational research | ||
| 653 | |a Classrooms | ||
| 653 | |a Technology assessment | ||
| 653 | |a Social interaction | ||
| 653 | |a Dynamic structural analysis | ||
| 653 | |a Information systems | ||
| 653 | |a Literature reviews | ||
| 653 | |a Nonlinear dynamics | ||
| 653 | |a Usefulness | ||
| 653 | |a Cultural factors | ||
| 653 | |a Undergraduate students | ||
| 653 | |a Internet | ||
| 653 | |a Cultural differences | ||
| 653 | |a Social | ||
| 700 | 1 | |a Wang, Yangxin | |
| 700 | 1 | |a Luo, Wenbo | |
| 773 | 0 | |t PLoS One |g vol. 19, no. 12 (Dec 2024), p. e0308630 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3144310256/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3144310256/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3144310256/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |