Decision trees do not lie: Curiosities in preferences of Croatian online consumers

Ana Marija Filipas (), Nenad Vretenar () and Ivan Prudky ()
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Ana Marija Filipas: University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia
Nenad Vretenar: University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia
Ivan Prudky: University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia

Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, 2023, vol. 41, issue 1, 157-181

Abstract: Understanding consumers’ preferences has always been important for economic theory and for business practitioners in operations management, supply chain management, marketing, etc. While preferences are often considered stable in simplified theoretical modelling, this is not the case in real-world decision-making. Therefore, it is crucial to understand consumers’ preferences when a market disruption occurs. This research aims to recognise consumers’ preferences with respect to online shopping after the COVID-19 outbreak hit markets. To this purpose, we conducted an empirical study among Croatian consumers with prior experience in online shopping using an online questionnaire. The questionnaire was completed by 350 respondents who met the criteria. We selected decision-tree models using the J48 algorithm to determine the influences of the found shopping factors and demographic characteristics on a consumer’s preference indicator. The main components of our indicators that influence consumer behaviour are the stimulators and destimulators of online shopping and the importance of social incidence. Our results show significant differences between men and women, with men tending to use fewer variables to make decisions. In addition, the analysis revealed that four product groups and a range of shopping mode-specific influencing factors are required to evaluate consumers’ purchase points when constructing the consumers’ preference indicator.

Keywords: decision-making; consumers’ preferences; data mining; decision trees; shopping behaviour indicators (search for similar items in EconPapers)
JEL-codes: C44 D12 (search for similar items in EconPapers)
Date: 2023
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