A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

Sohaib, O; Hussain, W; Asif, M; Ahmad, M; Mazzara, M

A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

Sohaib, O

Hussain, W

Asif, M

Ahmad, M

Mazzara, M

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Publication Type:: Journal Article
Citation:: IEEE Access, 2020, 8 pp. 13138 - 13150
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Sohaib, O https://orcid.org/0000-0001-9287-5995	en_US
dc.contributor.author	Hussain, W https://orcid.org/0000-0003-0610-4006	en_US
dc.contributor.author	Asif, M https://orcid.org/0000-0003-1839-2527	en_US
dc.contributor.author	Ahmad, M https://orcid.org/0000-0002-3320-2261	en_US
dc.contributor.author	Mazzara, M https://orcid.org/0000-0002-3860-4948	en_US
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	IEEE Access, 2020, 8 pp. 13138 - 13150	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138005
dc.description.abstract	© 2013 IEEE. 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.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2960083	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2013 IEEE. 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.

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http://hdl.handle.net/10453/138005