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Factors behind consumer choice of online marketplaces

Author

Listed:
  • A.O. Martsenuk

    (AO T-Bank, Moscow, Russia)

  • A.G. Mirzoyan

    (Lomonosov Moscow State University, Moscow, Russia)

  • P.A. Lavrinenko

    (Financial Research Institute of the Ministry of Finance of the Russian Federation, Moscow, Russia; The Institute of Economic Forecasting of the Russian Academy of Sciences, Moscow, Russia)

Abstract

Marketplaces invest substantial resources in attracting users and compete for market share. Intense competition highlights the importance of pinpointing the factors affecting buyer behaviour. The study aims to identify the characteristics of Russian marketplaces that have the most profound effect on consumer choice of an e-commerce platform, using Ozon and Wildberries as examples. The theory of reasoned action constitutes the methodological basis of the study. The research methods include means clustering, the Analytic Hierarchy Process, and factor analysis. The empirical evidence is comprised of the results of two surveys: first, with the help of 230 respondents, a list of relevant marketplace characteristics was compiled that consumers pay attention to when choosing a marketplace; second, data on the preferences and socio-demographic characteristics of 478 respondents were collected. The results showed that the platforms’ key characteristics are assortment, delivery, and prior platform usage experience. On average, Wildberries has a higher platform quality index and is rated higher in terms of Website Quality and Loyalty Program parameters. Consumer preferences are heterogeneous: within the pro-Wildberries clusters, the key factors of choice are delivery, prior experience and website quality, whereas in the Ozon-dominated cluster, it is prior experience and social circle. The research findings can be utilized to devise marketing strategies, strengthen customer loyalty, and attract new customers.

Suggested Citation

  • A.O. Martsenuk & A.G. Mirzoyan & P.A. Lavrinenko, 2025. "Factors behind consumer choice of online marketplaces," Upravlenets, Ural State University of Economics, vol. 16(3), pages 92-108, July.
    • Handle: RePEc:url:upravl:v:16:y:2025:i:3:p:92-108
    DOI: 10.29141/2218-5003-2025-16-3-7
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    References listed on IDEAS

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    1. Amy Wenxuan Ding & Shibo Li & Patrali Chatterjee, 2015. "Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation," Information Systems Research, INFORMS, vol. 26(2), pages 339-359, June.
    2. Pereira, Hélia Gonçalves & Salgueiro, Maria de Fátima & Rita, Paulo, 2016. "Online purchase determinants of loyalty: The mediating effect of satisfaction in tourism," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 279-291.
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    Cited by:

    1. E.R. Sharko & M.N. Sarukhanian, 2025. "Adapting the business model and marketing strategy of local cosmetic brands in the Russian market," Upravlenets, Ural State University of Economics, vol. 16(6), pages 34-50, January.

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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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