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A predictive analysis on student behaviour in UTAR by using e-commerce platform

People frequently use e-commerce platforms as their main daily shopping method during the global Covid-19 pandemic. E-commerce platforms are indispensable application software for university students. There will be a notable increase in their intention to purchase, particularly during promotional pe...

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Bibliographic Details
Main Author: Chin, Wai Teng
Format: Final Year Project / Dissertation / Thesis
Published: 2024
Subjects:
T Technology (General)
TD Environmental technology. Sanitary engineering
Online Access:http://eprints.utar.edu.my/7022/1/fyp_IB_2024_CWT.pdf
http://eprints.utar.edu.my/7022/
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spelling my-utar-eprints.70222025-02-27T07:25:54Z A predictive analysis on student behaviour in UTAR by using e-commerce platform Chin, Wai Teng T Technology (General) TD Environmental technology. Sanitary engineering People frequently use e-commerce platforms as their main daily shopping method during the global Covid-19 pandemic. E-commerce platforms are indispensable application software for university students. There will be a notable increase in their intention to purchase, particularly during promotional periods like Double 11. Decision-makers or the marketing department of a company may recommend or advertise users considering consumer characteristics like gender, age, needs, and product preferences. Their desire to purchase will grow more easily as a result, and the dashboard results can clearly highlight each point that is significant or might affect the sale. Thus, by utilizing suitable predictive models and dashboard tools like Power BI or Jupyter, the underlying factors can be examined. As a result, this study examines the variables affecting UTAR students' online buying behavior. Undergraduate students at UTAR's Kampar Campus are the research's intended respondents. Since all the data for this study is collected at once, it is cross-sectional in nature and uses a quantitative research methodology. The data is analyzed using the Data Science Life Cycle. Predictive models and dashboard technologies such as Power BI and Jupyter were used to investigate the underlying elements driving student behavior. The findings showed that perceived ease, availability, and time-saving benefits have a substantial impact on students’ online buying selections. Furthermore, security issues, product quality worries, and the inability to verify things before purchasing were significant deterrents. Lastly based on the findings, it is advised that e-commerce platforms aimed at university students’ transparency, improve transaction security, and provide flexible return policies to alleviate product-related concerns. Moreover, personalized marketing methods emphasizing convenience and availability benefits may boost student involvement with online buying platforms. 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7022/1/fyp_IB_2024_CWT.pdf Chin, Wai Teng (2024) A predictive analysis on student behaviour in UTAR by using e-commerce platform. Final Year Project, UTAR. http://eprints.utar.edu.my/7022/
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 T Technology (General)
TD Environmental technology. Sanitary engineering
spellingShingle T Technology (General)
TD Environmental technology. Sanitary engineering
Chin, Wai Teng
A predictive analysis on student behaviour in UTAR by using e-commerce platform
description People frequently use e-commerce platforms as their main daily shopping method during the global Covid-19 pandemic. E-commerce platforms are indispensable application software for university students. There will be a notable increase in their intention to purchase, particularly during promotional periods like Double 11. Decision-makers or the marketing department of a company may recommend or advertise users considering consumer characteristics like gender, age, needs, and product preferences. Their desire to purchase will grow more easily as a result, and the dashboard results can clearly highlight each point that is significant or might affect the sale. Thus, by utilizing suitable predictive models and dashboard tools like Power BI or Jupyter, the underlying factors can be examined. As a result, this study examines the variables affecting UTAR students' online buying behavior. Undergraduate students at UTAR's Kampar Campus are the research's intended respondents. Since all the data for this study is collected at once, it is cross-sectional in nature and uses a quantitative research methodology. The data is analyzed using the Data Science Life Cycle. Predictive models and dashboard technologies such as Power BI and Jupyter were used to investigate the underlying elements driving student behavior. The findings showed that perceived ease, availability, and time-saving benefits have a substantial impact on students’ online buying selections. Furthermore, security issues, product quality worries, and the inability to verify things before purchasing were significant deterrents. Lastly based on the findings, it is advised that e-commerce platforms aimed at university students’ transparency, improve transaction security, and provide flexible return policies to alleviate product-related concerns. Moreover, personalized marketing methods emphasizing convenience and availability benefits may boost student involvement with online buying platforms.
format Final Year Project / Dissertation / Thesis
author Chin, Wai Teng
author_facet Chin, Wai Teng
author_sort Chin, Wai Teng
title A predictive analysis on student behaviour in UTAR by using e-commerce platform
title_short A predictive analysis on student behaviour in UTAR by using e-commerce platform
title_full A predictive analysis on student behaviour in UTAR by using e-commerce platform
title_fullStr A predictive analysis on student behaviour in UTAR by using e-commerce platform
title_full_unstemmed A predictive analysis on student behaviour in UTAR by using e-commerce platform
title_sort predictive analysis on student behaviour in utar by using e-commerce platform
publishDate 2024
url http://eprints.utar.edu.my/7022/1/fyp_IB_2024_CWT.pdf
http://eprints.utar.edu.my/7022/
_version_ 1825817830117867520
score 13.244413

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