Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach
Cryptocurrency is receiving widespread acceptance in the international market. Unfortunately, little attention is focused on the full identification of the cryptocurrency adoption factors, especially when it comes to emerging nations like Saudi Arabia. The current investigation is aimed at investiga...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | Human Behavior and Emerging Technologies |
| Online Access: | http://dx.doi.org/10.1155/2023/9116006 |
| _version_ | 1797687034303741952 |
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| author | Ali S. A. Alomari Nasuha L. Abdullah |
| author_facet | Ali S. A. Alomari Nasuha L. Abdullah |
| author_sort | Ali S. A. Alomari |
| collection | DOAJ |
| description | Cryptocurrency is receiving widespread acceptance in the international market. Unfortunately, little attention is focused on the full identification of the cryptocurrency adoption factors, especially when it comes to emerging nations like Saudi Arabia. The current investigation is aimed at investigating whether the use of dual structural equation modelling and artificial neural network (SEM-ANN) would permit a better comprehension of the determinants of cryptocurrency adoption than the single-step PLS-SEM technique and explore the predictors of cryptocurrency adoption. An extended unified theory of acceptance and use of technology (UTAUT) model was used. A sample of 344 responses from Saudi Arabian students at public universities was used to verify the model. Unlike the majority of existing studies that were based on a single-step PLS-SEM approach, this investigation employed a superior statistical approach, the dual SEM-ANN approach, considered a unique methodological approach that can recognise variables’ connections that are both linear and nonlinear and predict relationships with higher accuracy. Moreover, the dual SEM-ANN analysis revealed security as the most important factor influencing whether users would accept cryptocurrency, followed by effort expectancy and awareness. The application of the dual SEM-ANN technique and the extension of the UTAUT model with security and awareness constructs have enhanced the existing technology adoption literature. Additionally, methodological, theoretical, and practical contributions were offered by this study. |
| first_indexed | 2024-03-12T01:13:27Z |
| format | Article |
| id | doaj.art-27a69ede47aa423aa3f678ebe168f7b8 |
| institution | Directory Open Access Journal |
| issn | 2578-1863 |
| language | English |
| last_indexed | 2024-03-12T01:13:27Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Human Behavior and Emerging Technologies |
| spelling | doaj.art-27a69ede47aa423aa3f678ebe168f7b82023-09-14T00:00:02ZengHindawi-WileyHuman Behavior and Emerging Technologies2578-18632023-01-01202310.1155/2023/9116006Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network ApproachAli S. A. Alomari0Nasuha L. Abdullah1School of Computer SciencesSchool of Computer SciencesCryptocurrency is receiving widespread acceptance in the international market. Unfortunately, little attention is focused on the full identification of the cryptocurrency adoption factors, especially when it comes to emerging nations like Saudi Arabia. The current investigation is aimed at investigating whether the use of dual structural equation modelling and artificial neural network (SEM-ANN) would permit a better comprehension of the determinants of cryptocurrency adoption than the single-step PLS-SEM technique and explore the predictors of cryptocurrency adoption. An extended unified theory of acceptance and use of technology (UTAUT) model was used. A sample of 344 responses from Saudi Arabian students at public universities was used to verify the model. Unlike the majority of existing studies that were based on a single-step PLS-SEM approach, this investigation employed a superior statistical approach, the dual SEM-ANN approach, considered a unique methodological approach that can recognise variables’ connections that are both linear and nonlinear and predict relationships with higher accuracy. Moreover, the dual SEM-ANN analysis revealed security as the most important factor influencing whether users would accept cryptocurrency, followed by effort expectancy and awareness. The application of the dual SEM-ANN technique and the extension of the UTAUT model with security and awareness constructs have enhanced the existing technology adoption literature. Additionally, methodological, theoretical, and practical contributions were offered by this study.http://dx.doi.org/10.1155/2023/9116006 |
| spellingShingle | Ali S. A. Alomari Nasuha L. Abdullah Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach Human Behavior and Emerging Technologies |
| title | Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach |
| title_full | Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach |
| title_fullStr | Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach |
| title_full_unstemmed | Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach |
| title_short | Cryptocurrency Adoption among Saudi Arabian Public University Students: Dual Structural Equation Modelling and Artificial Neural Network Approach |
| title_sort | cryptocurrency adoption among saudi arabian public university students dual structural equation modelling and artificial neural network approach |
| url | http://dx.doi.org/10.1155/2023/9116006 |
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