A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

Sohaib, Osama; Hussain, Walayat; Asif, Muhammad; Ahmad, Muhammad; Mazzara, Manuel

doi:10.1109/access.2019.2960083

Cited by 117 publications

(98 citation statements)

References 63 publications

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“…Contrary to previous empirical studies which had employed shallow ANN approach along with structural equational modelling [ 29 – 31 ], the current study has adopted the deep leaning dual-stage SEM-ANN approach to confirm the legitimacy of hypothesised relations in the research model [ 33 ]. This methodology consists of two phases.…”

Section: Methodsmentioning

confidence: 99%

“…Fundamentally, the input layer contains several neurons (independent variables) which obtain raw data and passes it to the hidden layers in the shape of synaptic weights. The output of each hidden layer is reliant on the choice of activation function, among which the most frequently used is the sigmoidal function [ 28 , 29 ]. Furthermore, multi-layer neural network models are observed as very complicated and very powerful, with the capability to resolve intricacies in higher-order models.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, studies applying SEM & ANN approaches with single hidden layers are not uncommon in numerous technology adoption studies, e.g. adoption of hybrid vehicles [ 28 ], ERP acceptance [ 29 ], wearable heath care technology acceptance [ 26 ], cryptocurrency adoption [ 29 ], m-health apps adoption [ 30 ], predicting m-government security [ 31 ]. In fact, the application of SEM & ANN in the domain of technology adoption is considered very useful [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in an emerging country like Malaysia, responding to the call for further empirical researches and improving the existing technology adoption literature exclusively on already empirically developed cryptocurrency theoretical models, i.e. based on shallow ANN approach [ 29 ]; this study fulfils the research gap and attains both objectives of the study (a- to examine whether application of deep learning based dual stage PLS-SEM & ANN analysis enables better in-depth research results as compared to PLS-SEM approach; b- to excavate the factors which can predict behavioural intention to adopt cryptocurrency). However, this study does not try to compare the results of deep neural networks results with shallow ANN approach.…”

Section: Introductionmentioning

confidence: 99%

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The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

et al. 2021

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In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

et al. 2021

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“…The application of machine learning algorithm has gained popularity in different fields of computing [43][44][45][46][47][48]. The range of scholars motivated to investigate prognosis and diagnosis using machine learning methods is progressively increasing.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Classification Algorithms on Birth Dataset

et al. 2020

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Generating intuitions from data using data mining and machine learning algorithms to predict outcomes is useful area of computing. The application area of data mining techniques and machine learning is wide ranging including industries, healthcare, organizations, academics etc. A continuous improvement is witnessed due to an ongoing research, as seen particularly in healthcare. Several researchers have applied machine learning to develop decision support systems, perform analysis of dominant clinical factors, extraction of useful information from hideous patterns in historical data, making predictions and disease classification. Successful researches created opportunities for physicians to take appropriate decision at right time. In current study, we intend to utilize the learning capability of machine learning methods towards the classification of birth data using bagging and boosting classification algorithms. It is obvious that differences in living styles, medical assistances, religious implications and the region you live in collectively affect the residents of that society. This motive has encouraged the researchers to conduct studies at regional levels to comprehensively explore the associated medical factors that contribute towards complications among women during pregnancy. The current study is a comprehensive comparison of bagging and boosting classification algorithms performed on birth data collected from the government hospitals of city Muzaffarabad, Kashmir. The experimental tasks are carried out using caret package in R which is considered an inclusive framework for building machine learning models. Accuracy based results with different evaluation measures are presented. Bagging functions including Adabag and BagFda performed marginally better in terms of accuracy, precision and recall. Improvements are observed in comparison to previous study performed on same dataset.

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Fostering trust and overcoming psychological resistance towards cryptocurrencies and cryptoassets

Babu,

Bason,

Porreca

et al. 2023

Psychology and Marketing

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This research investigates the extent to which sponsorships can be utilised to foster trust and reduce barriers to adopting new technologies. Using Crypto.com's sponsorship of the 2022 FIFA World Cup as the context, this mixed‐methods study utilises innovation resistance theory (IRT) and trust transfer theory (TTT) to investigate the extent to which such a sponsorship can increase trust and reduce barriers in innovative technologies such as cryptoassets, while also filling a research gap concerning consumer resistance to innovations in digital financial products and services. The findings of study 1, using a survey (n = 1081), and study 2 using interviews (n = 24) reveal that a positive image of sponsorship significantly influences favourability and interest, and trust of the product of the sponsor which subsequently reduces psychological barriers to adoption. Integrating the theoretical viewpoints of IRT and TTT, this study enhances our conceptual understanding regarding the psychological dimension of sponsorship and the extent to which a sponsorship generates interest, giving assurance and trust in the sponsor's product, and removing uncertainty; thus, reducing barriers to adoption.

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A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

Cited by 117 publications

References 63 publications

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

Performance Analysis of Classification Algorithms on Birth Dataset

Fostering trust and overcoming psychological resistance towards cryptocurrencies and cryptoassets

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