The Development of The E-Commerce Market in Poland in the Conditions of Intensification of Migration Processes

Abstract

The impact of the war in Ukraine and migration has affected the e-commerce markets of the recipient countries, presenting both opportunities, in the form of an increased consumer base, and challenges, such as the lack of a clear development vision. This paper aims to investigate the influence of migration processes on the development of e-commerce in Poland and examine the feasibility of using forecasting methods by e-commerce companies under these conditions for future activity planning. To fulfill the research objective, the following tasks were addressed: investigating the current state of e-commerce development influenced by migration processes; exploring modern migration processes and their impact on global economies; assessing the impact of migration from Ukraine on the Polish market; and analyzing a Polish online store to develop a model for forecasting data and planning activities under the influence of migration processes. To achieve this goal, three models were constructed: a multiple regression model to assess the level of migration processes' influence on e-commerce; a neural network to forecast sales for a Polish e-commerce store; and cluster analysis to identify clusters of goods most affected by migration processes. The study analyzed the nuances of modern migration processes and assessed the reverse effect of migration as a driver of e-commerce development. Migration stimulates e-commerce by altering consumer behavior and logistics routes, increasing exports and imports, and fostering the spread of digital entrepreneurship. Using data from a Polish online store, the study modeled the impact of market changes on the company's operations and identified the most significant factors. Thus, the analysis explored the impact of migration on e-business in Poland through constructed models. Regression analysis revealed that migration processes have contributed to the development of the Polish online store's sales, thanks to the increase in migrant consumers and rising price levels. A neural network was developed with machine learning, incorporating macroeconomic and demographic factors into its forecasting typology. Cluster analysis was employed to examine the online store's assortment, identifying clusters by sales volume and migrants' influence. The analysis determined that, following the onset of the migration movement, categories experiencing a surge in demand from refugees, such as baby food products, appliances, telephones, furniture, and communication devices, saw the most significant growth.

Keywords

• e-commerce;migration;forecasting;modeling;machine learning;big data;Data Science;regression analysis;clustering;neural network;