Analysis of consumer choice features in the digital environment

Authors

  • Krystsina I. Korbut Leverex International, 6 Aginskaga Street, Minsk 220076, Belarus
  • Olga I. Lavrova Belarusian State University of Informatics and Radioelectronics, 6 P. Browki Street, Minsk 220013, Belarus

Keywords:

consumer behaviour, logistic regression, premium purchase, factor analysis, cluster analysis, big data, digital marketing

Abstract

The article studies the behaviour of consumers of an online store selling durable goods in order to stimulate sales of premium products. The key objective is to build a predictive model that allows for a highly accurate assessment of the likelihood of making the next premium purchase based on big data analytics. The study uses multivariate data analysis methods, in particular, factor analysis (to identify latent patterns of product perception) and cluster analysis (to group users based on information-behavioural strategies). It is established that information involvement (the depth of interaction with reviews and filtering) greatly increases the likelihood of making a premium purchase. The model results make it possible to implement personalised content display, segment the audience for email and push notifications, and take into account regional characteristics and macroeconomic conditions that influence consumer preferences. The hypothesis about the significance of the influence of cognitive factors in making consumer choices in the digital environment is confirmed.

Author Biographies

  • Krystsina I. Korbut, Leverex International, 6 Aginskaga Street, Minsk 220076, Belarus

    analytical engineer

  • Olga I. Lavrova, Belarusian State University of Informatics and Radioelectronics, 6 P. Browki Street, Minsk 220013, Belarus

    PhD (economics), docent; dean of the faculty of engineering and economics

References

  1. Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica. 1979;47(2):263–292. DOI: 10.2307/1914185.
  2. Thaler RH, Sunstein CR. Nudge. Improving decisions about health, wealth, and happiness. New Haven: Yale University Press; 2008. 293 p.
  3. Фролова ДА, Гулецкая ЕА. Использование больших данных для повышения лояльности к бренду. В: Богуш ВА, редактор. Big data и анализ высокого уровня. Сборник научных статей X Международной научно-практической конференции; 13 марта 2024 г.; Минск, Беларусь. Часть 1. Минск: Белорусский государственный университет информатики и радиоэлектроники; 2024. с. 181–186. EDN: JZMYHY.
  4. Boyer KK, Hult GTM. Customer behavioral intentions for online purchases: an examination of fulfillment method and customer experience level. Journal of Operations Management. 2006;24(2):124–147. DOI: 10.1016/j.jom.2005.04.002.
  5. Sundararaj V, Rejeesh MR. A detailed behavioral analysis on consumer and customer changing behavior with respect to social networking sites. Journal of Retailing and Consumer Services. 2021;58:102190. DOI: 10.1016/j.jretconser.2020.102190.
  6. Kwak J, Zhang Yu, Yu J. Legitimacy building and e-commerce platform development in China: the experience of Alibaba. Technological Forecasting and Social Change. 2019;139:115–124. DOI: 10.1016/j.techfore.2018.06.038.
  7. Koehn D, Lessmann S, Schaal M. Predicting online shopping behaviour from clickstream data using deep learning. Expert Systems with Applications. 2020;150:113342. DOI: 10.1016/j.eswa.2020.113342.
  8. Fu H, Manogaran G, Wu K, Cao M, Jiang S, Yang A. Intelligent decision-making of online shopping behavior based on Internet of things. International Journal of Information Management. 2020;50:515–525. DOI: 10.1016/j.ijinfomgt.2019.03.010.
  9. Pappas IO, Kourouthanassis PE, Giannakos MN, Chrissikopoulos V. Explaining online shopping behavior with fsQCA: the role of cognitive and affective perceptions. Journal of Business Research. 2016;69(2):794–803. DOI: 10.1016/j.jbusres.2015.07.010.
  10. Bucko Jo, Kakalejčík L, Ferencová M. Online shopping: factors that affect consumer purchasing behaviour. Cogent Business & Management. 2018;5(1):1535751. DOI: 10.1080/23311975.2018.1535751.
  11. Nozaki Yu, Watanabe F, Satoh T. Analysis of item selection behavior in online shopping. In: Indrawan-Santiago M, Pardede E, Salvadori IL, Steinbauer M, Khalil I, Anderst-Kotsis G, editors. iiWAS2018. Proceedings of the 20th International conference on information integration and web-based applications & services; 2018 November 19–21; Yogyakarta, Indonesia. New York: Association for Computing Machinery; 2018. p. 41–45.
  12. Nguyen PH, Turkay C, Andrienko G, Andrienko N, Thonnard O, Zouaoui J. Understanding user behaviour through action sequences: from the usual to the unusual. IEEE Transactions on Visualization and Computer Graphics. 2018;25(9):2838–2852. DOI: 10.1109/tvcg.2018.2859969.
  13. Svatosova V. The importance of online shopping behavior in the strategic management of e-commerce competitiveness. Journal of Competitiveness. 2020;12(4):143–160. DOI: 10.7441/joc.2020.04.09.

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Published

2025-12-31

Issue

Section

C. Mathematical and Quantitative Methods

How to Cite

[1]
Korbut, K.I. and Lavrova, O.I. 2025. Analysis of consumer choice features in the digital environment. Journal of the Belarusian State University. Economics. 2 (Dec. 2025), 4–14.