Technology readiness and cryptocurrency adoption: PLS-SEM and Deep Learning Neural Network analysis

Abdullah Alharbi; Osama Sohaib

doi:10.1109/access.2021.3055785

Technology readiness and cryptocurrency adoption: PLS-SEM and Deep Learning Neural Network analysis

Abdullah Alharbi, Osama Sohaib

Research output: Contribution to journal › Article › peer-review

50 Citations (Scopus)

13 Downloads (Pure)

Abstract

Today's world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people's adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.

Original language	English
Pages (from-to)	21388-21394
Number of pages	7
Journal	IEEE Access
Volume	9
DOIs	https://doi.org/10.1109/access.2021.3055785
Publication status	Published - 1 Feb 2021
Externally published	Yes

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Cryptocurrency
PLS
SEM
neural network
technology readiness

Access to Document

10.1109/access.2021.3055785Licence: CC BY

Publisher version (open access)Final published version, 6.28 MBLicence: CC BY

Cite this

@article{b406d1dfdda8404baeeb16e23020843b,

title = "Technology readiness and cryptocurrency adoption: PLS-SEM and Deep Learning Neural Network analysis",

abstract = "Today's world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people's adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.",

keywords = "Cryptocurrency, PLS, SEM, neural network, technology readiness",

author = "Abdullah Alharbi and Osama Sohaib",

note = "Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",

year = "2021",

month = feb,

day = "1",

doi = "10.1109/access.2021.3055785",

language = "English",

volume = "9",

pages = "21388--21394",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

TY - JOUR

T1 - Technology readiness and cryptocurrency adoption

T2 - PLS-SEM and Deep Learning Neural Network analysis

AU - Alharbi, Abdullah

AU - Sohaib, Osama

N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - Today's world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people's adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.

AB - Today's world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people's adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.

KW - Cryptocurrency

KW - PLS

KW - SEM

KW - neural network

KW - technology readiness

UR - http://www.scopus.com/inward/record.url?scp=85100792968&partnerID=8YFLogxK

U2 - 10.1109/access.2021.3055785

DO - 10.1109/access.2021.3055785

M3 - Article

SN - 2169-3536

VL - 9

SP - 21388

EP - 21394

JO - IEEE Access

JF - IEEE Access

ER -

Technology readiness and cryptocurrency adoption: PLS-SEM and Deep Learning Neural Network analysis

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this