The role of demographic characteristics and shopping habits in online shopping behavior
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DOIhttp://dx.doi.org/10.21511/im.21(1).2025.14
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Article InfoVolume 21 2025, Issue #1, pp. 170-181
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Creative Commons Attribution 4.0 International License
E-commerce is a prospering industry, its global market size has doubled in recent years and, in addition to volume growth of more than 100%, its market share is constantly increasing. There has been a significant change in the information search phase of purchase decisions recently, which appears in a differentiated manner in generations. Social network sites have become the primary sources for the members of the young generation (Generation Y and Z) these days. The paper explores the habits of Hungarian online consumers and the factors influencing them. A quantitative data collection tool was selected as a method of the research, including an online questionnaire survey with 720 respondents. Based on the results, it can be concluded that most respondents have integrated monthly online shopping in their lives, and they browse offers online on a weekly basis. With respect to product categories, clothing products and electronic items were among the most frequently purchased, in terms of delivery, most people prefer delivery by courier service, while regarding payment methods, credit card payment is preferred. After conducting Principal Component Analysis, the research identified six factors that influence online purchase, including convenience, risk, time efficiency, social media, logistics, and availability. The hypothesis tests revealed significant differences along all variables related to demographics and shopping habits.
Acknowledgment
Supported by the University of Debrecen Program for Scientific Publication.
- Keywords
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JEL Classification (Paper profile tab)M31, L14
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References60
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Tables4
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Figures2
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- Figure 1. Model of factors influencing online shopping
- Figure 2. Product type ranking in the sample
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- Table 1. Demographic characteristics of the sample
- Table 2. Principal Component Analysis of the short version of the consumer online shopping behavior scale created by Rao et al. (2018)
- Table 3. Hypothesis testing along demographic variables
- Table 4. A hypothesis test along shopping habits variables
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