Abstract:

Online shopping has achieved significant growth in recent years, also for the reasons caused by the COVID-19 pandemic. According to forecasts, this growth should continue in the coming years. The aim of this paper is to determine the factors influencing certain aspects of consumer behavior when shopping on the Internet. We divided the research into three phases, literature study, construction of research models, and their evaluation. The data for the evaluation of the models came from a questionnaire survey on a sample of 1318 respondents. We used the statistical method of logistic regression to evaluate the models, which we performed by using the open-source software JAMOVI. In our study, we focused on two relatively frequently researched areas of consumer shopping behavior on the Internet, namely the importance of other customers' reviews by purchasing decisions (model 1), the financial/non-financial benefits of online shopping (model 2), and what factors predict them. We identified in the first model these statistically significant predictors D1 - Gender, D5 - University student, F1 - Identification with statement 1, F4 - Purchase on electronic marketplaces, F5 - The most trust-inspiring e-shop function, F6 - Comparison of similar products from different sellers before purchasing and by the second model they were D3 - Type of settlement and F9 - Preferred payment option. According to Nagelkerke R squared explains the first model 0.196 variability and the second model just 0.0341 variability of the dependent variable.