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
AuthorsSohaib, Osama, Hussain, Walayat, Asif, Muhammad, Ahmad, Muhammad and Mazzara, Manuel
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.

KeywordsBitcoin; cryptocurrency; neural network; PLS-SEM; technology readiness
Year2020
JournalIEEE Access
Journal citation8, pp. 13138-13150
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2019.2960083
Scopus EID2-s2.0-85078763033
Open accessPublished as ‘gold’ (paid) open access
Page range13138-13150
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online16 Dec 2019
Publication process dates
Accepted08 Dec 2019
Deposited18 Jul 2023
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