As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.

The aim of this research study was to establish a framework for the relationships between the sharing economy and the Sustainable Development Goals (hereinafter, SDGs) set by the United Nations. There are 17 SDGs with 169 targets, which, in the scientific literature, are classified into sustainability dimensions: economic, social, and environmental. Thus, the objective of the current research was to perform an economic assessment of the sharing economy in the context of SDGs by analyzing European Union countries with a particular emphasis on their economic growth. Although the sharing economy has been analyzed from different aspects in recent scientific articles, the impact of this phenomenon on national economies in the framework of SDGs is lacking. Firstly, based on the latest research on the sharing economy from the perspective of sustainable development, a theoretical model of the sharing economy was developed in this study. Secondly, SDG indicators in the economic dimension and other key economic growth variables for European Union countries were collected. Thirdly, a cluster analysis was performed to determine the impact of the sharing economy on European Union countries in terms of SDGs in the economic dimension. The current study contributes to the existing research by analyzing the sharing economy from the perspective of sustainable economic development and highlights that this business model positively impacts countries’ economic sustainability in terms of SDGs.

The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice.

The scope of the data presented in this study offers a comprehensive view of the issue of the HEI graduates employability in the Czech Republic and also in the Slovak Republic – related to determinants of economic development and their impact on them. This paper examines the impact of gross domestic product, gross domestic expenditure on research and experimental development by only higher education sector and foreign direct investment on HEI graduates employability. The results indicate that correlation between unemployment of tertiary educated Slovaks and GDP, GERD and FDI values was very big. Correlation relationship of similar determinants, except determinant GERD in conditions of the Czech Republic was characterized as weak. On the other hand, significantly stronger (very big to perfect) correlation affecting employment of tertiary educated Czechs regarding to indicators of gross domestic product, gross domestic expenditure on research and experimental development by sector of higher education and foreign direct investments as well. In conditions of the Slovak Republic, correlation relationship between employment of tertiary educated Slovaks and GDP was almost perfect.

The development of the concept of creative industries is related to the contribution of culture and creativity to the economy. Creative industries are often associated with innovation.Scientific literature reveals that creative business is more innovative compared to other sectors of the economy. Some authors also note that when assessing the level of innovation, the companies that are closely related to creative industries tend to have higher innovation rates. The concept of creativity is closely related to the major purpose of innovation, i.e. problem solving, while synergy between creativity and economy formulates creative industries theory. Based on the literature, problem solution requires creativity, and some solutions can inspire the emergence of innovation. The major purpose of this article is to provide the theoretical presumptions of the creative industries innovation productivity performance. Applying the method of comparative literature analysis, this article presents a conceptual model that allows to evaluate and compare countries economies in terms of the productivity in the creative industries innovation performance.

The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country’s economic activity is characterized by various indicators such as the gross domestic product, the level of employment or unemployment of the population, the price level in the country, inflation, and other frequently used economic indicators. The most popular were the gross domestic product (GDP) and industrial production. However, such traditional tools have started to decline in modern times (as the timely knowledge of information becomes a critical factor in decision making in a rapidly changing environment) as they are published with significant delays. This work aims to use the information in the Lithuanian mass media and machine learning methods to assess whether these data can be used to assess economic activity. The aim of using these data is to determine the correlation between the usual indicators of economic activity assessment and media sentiments and to forecast traditional indicators. When evaluating consumer confidence, it is observed that the forecasting of this economic activity indicator is better based on the general index of negative sentiment (comparisons with univariate time series). In this case, the average absolute percentage error is 1.3% lower. However, if all sentiments are included in the forecasting instead of the best one, the forecasting is worse and in this case the MAPE is 5.9% higher. It is noticeable that forecasting the monthly and annual inflation rate is thus best when the overall negative sentiment is used. The MAPE of the monthly inflation rate is as much as8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.