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Correlation model in the adoption of E-payment services: A machine learning approach

The purpose of this study was to understand the highly correlated factors that influence user intention to adopt e-payment into their daily lives. The research framework incorporates the 4 main constructs of the UTAUT model and additional determinants of the extended UTAUT model to model the relatio...

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Main Author: Tan, Xi En
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.utar.edu.my/4670/1/fyp_CS_2022_TXE.pdf
http://eprints.utar.edu.my/4670/
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spelling my-utar-eprints.46702022-10-17T14:01:10Z Correlation model in the adoption of E-payment services: A machine learning approach Tan, Xi En Q Science (General) T Technology (General) The purpose of this study was to understand the highly correlated factors that influence user intention to adopt e-payment into their daily lives. The research framework incorporates the 4 main constructs of the UTAUT model and additional determinants of the extended UTAUT model to model the relationship. Previous research has shown that the UTAUT model is able to model the relationship of factors that influence user intention to adopt e-payment. To analyse results, a questionnaire was developed based on the UTAUT model, and was distributed among university graduates, researchers, and students. In total, 286 samples were recorded and analysed. Previously, identifying highly correlated user behaviours was done through a lot of statistical research and hypothesis testing. The main goal of the project is to automate this process, by using machine learning to identify the important features. To extract highly correlated features, several pairwise correlation methods were used, such as Pearson’s Correlation, and Spearman’s Correlation. Then, by using Correlation Based Feature selection algorithm, we select the best subset of features out of the highly correlated features to do predictive modelling. This is a novel method, as we do not need to rely on statistical analysis, rather we can automate the process of identifying important features using machine learning models. The end goal of the project is to develop a model that identifies the important features that affect user intention to adopt e-payment. 2022-04-22 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4670/1/fyp_CS_2022_TXE.pdf Tan, Xi En (2022) Correlation model in the adoption of E-payment services: A machine learning approach. Final Year Project, UTAR. http://eprints.utar.edu.my/4670/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Tan, Xi En
Correlation model in the adoption of E-payment services: A machine learning approach
description The purpose of this study was to understand the highly correlated factors that influence user intention to adopt e-payment into their daily lives. The research framework incorporates the 4 main constructs of the UTAUT model and additional determinants of the extended UTAUT model to model the relationship. Previous research has shown that the UTAUT model is able to model the relationship of factors that influence user intention to adopt e-payment. To analyse results, a questionnaire was developed based on the UTAUT model, and was distributed among university graduates, researchers, and students. In total, 286 samples were recorded and analysed. Previously, identifying highly correlated user behaviours was done through a lot of statistical research and hypothesis testing. The main goal of the project is to automate this process, by using machine learning to identify the important features. To extract highly correlated features, several pairwise correlation methods were used, such as Pearson’s Correlation, and Spearman’s Correlation. Then, by using Correlation Based Feature selection algorithm, we select the best subset of features out of the highly correlated features to do predictive modelling. This is a novel method, as we do not need to rely on statistical analysis, rather we can automate the process of identifying important features using machine learning models. The end goal of the project is to develop a model that identifies the important features that affect user intention to adopt e-payment.
format Final Year Project / Dissertation / Thesis
author Tan, Xi En
author_facet Tan, Xi En
author_sort Tan, Xi En
title Correlation model in the adoption of E-payment services: A machine learning approach
title_short Correlation model in the adoption of E-payment services: A machine learning approach
title_full Correlation model in the adoption of E-payment services: A machine learning approach
title_fullStr Correlation model in the adoption of E-payment services: A machine learning approach
title_full_unstemmed Correlation model in the adoption of E-payment services: A machine learning approach
title_sort correlation model in the adoption of e-payment services: a machine learning approach
publishDate 2022
url http://eprints.utar.edu.my/4670/1/fyp_CS_2022_TXE.pdf
http://eprints.utar.edu.my/4670/
_version_ 1748184794431225856
score 13.211869

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