Factors influencing the online shopping behavior of students in UTAR, Kampar Campus

• Main Authors: Chai, Kar Yen, Lee, Yong Wei, Peh, Jing Ting, Yap, Hew Yeng
• Format: Final Year Project / Dissertation / Thesis
• Published: 2018
• Subjects: HM Sociology
• Online Access: http://eprints.utar.edu.my/3357/1/fyp_PR_CKY_2018.pdf; http://eprints.utar.edu.my/3357/

Recently, National Consumer Complaints Centre (NCCC) found out that there are more and more online shopping users become the victims of unscrupulous online merchants in Malaysia. There were 7,692 complaints and amounting to a loss of RM4 million which were reported at NCCC’s Annual Consumer Complaints Report 2015. However, online shopping is still an uptrend in Malaysia. According to Malaysia e-commerce insight, there were 15 million of Malaysian are actively on using online shopping and Generations Y are the most active online users. Therefore, this study examines the factors influencing the online shopping behaviour of students in Universiti Tunku Abdul Rahman (UTAR), Kampar campus. The targeted respondents of this research are foundation students and undergraduates in UTAR, Kampar. A conceptual framework is proposed by applying the constructs of Theory of Planned Behaviour (TPB) including attitudes, subjective norms, perceived behavioural control, intention and behaviour and two additional factors which are perceived risks and perceived benefits adopted from the past empirical studies. Purposive sampling which is categorized as one of the nonprobability sampling has been selected as the sampling method for this study. The research method of this study is quantitative research and this study is a cross-sectional study as the data is obtained all at once. A pilot test will be carried out before the distribution of actual questionnaires to the respondents to ensure the reliability of the variables and improve the quality of the questionnaires. Total of 390 questionnaires will be distributed to the respondents. Descriptive analysis, Pearson Correlation Coefficient (PCC) and Multiple Linear Regression (MLR) are used to analyse the data collected.