A PLS-SEM neural network approach for understanding cryptocurrency adoption
Journal article
Sohaib, Osama, Hussain, Walayat, Asif, Muhammad, Ahmad, Muhammad and Mazzara, Manuel. (2020). A PLS-SEM neural network approach for understanding cryptocurrency adoption. IEEE Access. 8, pp. 13138-13150. https://doi.org/10.1109/ACCESS.2019.2960083
Authors | Sohaib, Osama, Hussain, Walayat, Asif, Muhammad, Ahmad, Muhammad and Mazzara, Manuel |
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Abstract | 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. |
Keywords | Bitcoin; cryptocurrency; neural network; PLS-SEM; technology readiness |
Year | 2020 |
Journal | IEEE Access |
Journal citation | 8, pp. 13138-13150 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2019.2960083 |
Scopus EID | 2-s2.0-85078763033 |
Open access | Published as ‘gold’ (paid) open access |
Page range | 13138-13150 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 16 Dec 2019 |
Publication process dates | |
Accepted | 08 Dec 2019 |
Deposited | 18 Jul 2023 |
https://acuresearchbank.acu.edu.au/item/8z526/a-pls-sem-neural-network-approach-for-understanding-cryptocurrency-adoption
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Publisher's version
OA_Sohaib_2020_A_PLS_SEM_neural_network_approach.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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