Striving to achieve sustainable development goals and taking care of the environment into the policies of car manufacturers forced the search for alternative sources of vehicle propulsion. One way to implement a sustainable policy is to use electric motors in cars. The observable development of the electric car market provides consumers with a wide spectrum of choices for a specific model that would meet their expectations. Currently, there are 53 different electric car models on the primary market in Poland. The aim of the article was to present the performed market segmentation, focused on identifying the similarities in the characteristics of electric car models on the Polish market and proposing their groupings. Based on the classification by the hierarchical cluster analysis algorithm (Ward’s method, squared Euclidean distance), the market division into 2, 3, and 4 groups was proposed. The Polish EV market segmentation took place not only in terms of the size and class of the car but primarily in terms of performance and overall quality of the vehicle. The performed classification did not change when the price was additionally included as a variable. It was also proposed to divide the market into 4 segments named: Premium, City, Small, and Sport. The segmentation carried out in this way helps to better understand the structure of the electric car market.
The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big data analyzes in scientific literature sources. This article adopts two research methods. The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. This method has been conducted in four steps: search, selection, analysis, and synthesis. The second research method is the bibliographic verification of the obtained results. The verification consisted of querying the Scopus database with previously identified key phrases and then performing trend analysis on the revealed Scopus results. The main contributions of this study are: (1) to organize knowledge on the role of advanced big data analytics (BDA), mainly big data mining in understanding customer behavior; (2) to indicate the importance of the temporal dimension of customer behavior; and (3) to identify an interesting research gap: mining of temporal big data for a complete picture of customers.
The growing popularity of electric scooters has resulted in the dynamic development of this market on a global scale. Each potential customer has different preferences and therefore should be able to choose a scooter that meets their expectations. The study used a dataset comprising 42 scooters available on Polish market with their specifications. The aim of the study was to present the structure of the electric scooter market in Poland and carry out a market segmentation. On the basis of an arbitrary decision, under the terms of the quotients of the coefficients in successive stages of combining into clusters, two and four classes of scooters were distinguished. The comparison of clusters with the adopted price ranges proved that, with the increase in the performance of the electric scooter, the price rises. Such a combination can help customers choose the cheapest scooter from a given market segment, according to their budget constraints and personal preferences.
The coronavirus pandemic affected all areas of social life and changed the conditions in which most industries operate. The current forms of profitable business activities were suspended in many sectors of the market, which forced entrepreneurs to adapt to the new market conditions. During the Covid-19 pandemic, particular attention should be paid to the activities of enterprises in two so far closely related areas: corporate social responsibility (CSR) and sustainable development (SD). Enterprises typically pursued sustainable development goals (SDGs) and supported them as part of their CSR. The paper is exploratory in nature and it aims to determine the degree of CSR commitment and the implementation of the SDGs during the first year of the Covid-19 pandemic. The results show that the sudden outbreak of the pandemic and the equally dynamic response of governments left some enterprises in uncertainty as going concerns. However, financially sound companies have become committed to helping the population groups most affected by the Covid-19 pandemic. Moreover, the pandemic situation has significantly distanced companies from achieving the intended long-term Global Goals, and the spread of the Covid-19 disease has a significant (mostly negative) impact on the sustainable development of the world. Furthermore, it is impossible to determine the long-term impact of a pandemic on CSR activities and on the implementation of the SDGs.
Technological development has resulted in the digitization of many areas of life. The modern society is particularly defined as the “information society”, because of the fact that most of their activities take place on the Internet and thus the society is information-dependent. The introduced governmental sanitary restrictions (also in terms of the functioning of the economy) to limit the spread of the coronavirus caused that life has shifted to a greater extent to the Internet sphere. The aim of the study is to determine the potential impact of a pandemic on the virtualization of buying behavior. The con- ducted research is exploratory in nature and constitutes a valuable starting point for po- tential further directions of analyzes. The results of this study showed that excavating the topic of the impact of the pandemic on buying behavior in the area of consumption vir- tualization is justified, because in all the analyzed product categories there was a statisti- cally significant change in the form of purchases. Keywords: virtualization, buying behavior, customer behavior, pandemic, COVID-19. JEL Classification: D10, D12, E21
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