A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption
The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable m...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8933370/ |
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| author | Osama Sohaib Walayat Hussain Muhammad Asif Muhammad Ahmad Manuel Mazzara |
| author_facet | Osama Sohaib Walayat Hussain Muhammad Asif Muhammad Ahmad Manuel Mazzara |
| author_sort | Osama Sohaib |
| collection | DOAJ |
| description | The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) – and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor’s importance-performance. |
| format | Article |
| id | doaj-art-96011ea3e3df4125a74c4b8f4372e25e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-96011ea3e3df4125a74c4b8f4372e25e2025-01-30T00:00:55ZengIEEEIEEE Access2169-35362020-01-018131381315010.1109/ACCESS.2019.29600838933370A PLS-SEM Neural Network Approach for Understanding Cryptocurrency AdoptionOsama Sohaib0https://orcid.org/0000-0001-9287-5995Walayat Hussain1https://orcid.org/0000-0003-0610-4006Muhammad Asif2https://orcid.org/0000-0003-1839-2527Muhammad Ahmad3https://orcid.org/0000-0002-3320-2261Manuel Mazzara4https://orcid.org/0000-0002-3860-4948School of Information, Systems, and Modelling, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, AustraliaSchool of Information, Systems, and Modelling, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, AustraliaDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Engineering, Khwaja Freed University of Engineering and Information Technology, Punjab, PakistanInstitute of Software Development and Engineering, Innopolis University, Innopolis, RussiaThe majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) – and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor’s importance-performance.https://ieeexplore.ieee.org/document/8933370/Bitcoincryptocurrencyneural networkPLS-SEMtechnology readiness |
| spellingShingle | Osama Sohaib Walayat Hussain Muhammad Asif Muhammad Ahmad Manuel Mazzara A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption IEEE Access Bitcoin cryptocurrency neural network PLS-SEM technology readiness |
| title | A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption |
| title_full | A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption |
| title_fullStr | A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption |
| title_full_unstemmed | A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption |
| title_short | A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption |
| title_sort | pls sem neural network approach for understanding cryptocurrency adoption |
| topic | Bitcoin cryptocurrency neural network PLS-SEM technology readiness |
| url | https://ieeexplore.ieee.org/document/8933370/ |
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