Purpose:The objective of this study involves the determination of data-driven solutions needed to increase the usability of e-commerce systems and its profitability. Design/Methodology/Approach: In the research implementation process, logic generalization and induction to identify and analyze the most beneficial data science tools in e-commerce. deign of the study is to generalize existing approaches of data science usage in e-commerce, to develop practical recommendations to ensure the competitive advantages of e-commerce market participants and to estimate the cost of technical tools needed to launch the data science project in e-commerce. Findings: The results clearly demonstrate that in 2020 businesses that have e-commerce system were financially successful and in next 3 years online sales will increase rapidly. The simple analytics will not cover the demand of online business and it is needed to implement advanced data-driven decisions now. Practical Implications: The present research provides generalized knowledge on how to launch a data science project in e-commerce and how to choose the best programming and visualization app to ensure the profitability of a project. The scientific paper gives an instruction on the marketing contribution analysis, which is the tool of key importance for online marketplaces. Originality/Value: The main research value drawn from the study is to launch the datadriven models in e-commerce company it is needed to observe the real business need and available data, find the best programming and visualization tools. It was defined that the most beneficial data science solutions are demand forecasting, estimation of the marketing contribution, customers clustering, recommendation system and customers' attitude analysis. The main business need for each e-commerce company is to estimate the contribution of all marketing channels and advertisement formats separately. This issue may be easily handled with a regression modelling, which helps to understand a set of factors influencing sales.
What are the economy sectors will help countries overcome the crisis caused by the COVID-19 pandemic? How countries should rezone their investment strategies to bolster recovery in main economy sectors? Using the Cobb-Douglas model, the importance of agriculture, energy, education, and ICT industries for GDP growth was proven. It was confirmed that agriculture and industry will be key sectors in the post-crisis period for Ukraine, Poland and Austria. During the time of economic uncertainty growth, ICT and e-commerce sectors are principal tools that will sustain the population’s well-being.
The COVID-19 pandemic dramatically and irreversibly transformed global e-commerce trends. The process of internationalization has been slowed down considerably, the level of globalization impact has decreased, that, consequently, has led to a strong lag in the development of some countries. The aim of the study is to determine the nature of e-commerce socio and economic effects in Ukraine, Poland, and Austria. For analysis, a modern mathematical apparatus was used – the Cobb-Douglas model and the Markov chain methodology. The study shows a significant positive impact of e-commerce on employment and GDP in the three countries. Thus, with an increase in investment levels in e-commerce and ICT by 1%, employment in Ukraine will increase by 0.02%, in Poland – by 0.14%, and in Austria – by 0.17%. Similarly, Ukraine’s GDP may rise by 0.07%, Poland’s – by 0.2% and Austria’s – by 0.07%. Therefore, a stable flow of investment in e-commerce will provide countries with a faster way out of the crisis, create more jobs and opportunities for business development.
The ingrained tendency to implement information and communication technologies (ICT) in EU enterprises over the last decade has caused dramatic changes in the labor market. Since the demand for ICT personnel is growing, there is still a need to create a comprehensive strategy to effectively manage ICT specialists when restructuring enterprises. The aim of the research is to identify transferring processes between low‑ and high‑skilled ICT personnel and predict the employment rate in the ICT field until 2025. A Markov chain was used as the method of analysis. Using statistical data about the employment rate of ICT personnel by education attainment level, we have built a Markov chain model that describes the processes of ICT personnel with different levels of education. Data from 2005 to 2019 was used to build forecasting because of the absence of the latest information. We demonstrate that with the help of digitalization, the employment rate of ICT staff in 2025 will increase by 64% compared to 2018. The research verifies that ICT personnel will be in great demand until 2023 and, importantly, low‑ and middle‑skilled personnel will be in demand, as well as high‑skilled personnel. The employment rate in the ICT field will be at its highest level in 2022 as the favorable economic conditions for ICT adoption will help it. The growing demand for low‑ and medium‑skilled ICT staff are met both by staff relocation processes and by the increasing digitalization of business units and public sector institutions. The added value of the analysis is the prediction that the largest growth in ICT personnel employment will occur by 2023, but employment growth will slow down after that. The main obstacle to employment growth through digitalization is the global economic crisis because of different reasons.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.