The relevance of considering and analyzing financial stability and competitive immunity at the meso-level in modern conditions is increasing due to changes in the state of both the economic and social spheres. It was found that the “competitive immunity of the territory” reflects a number of new characteristics of modern territorial-regional-interregional competition in the global economy, which distinguishes it from the concept of economic security both at the macro and meso levels. The paper considers the category of “competitive immunity of the region”, which implies the possibility of survival of the peripheral territories of the regions of Ukraine and maintaining their high level of competitiveness. In accordance with the accepted concept of competitive immunity, three problemarea blocks were identified: information-digital approach; information and digital technologies; cost and reputation management, which include objects of managerial influence necessary to evaluate the transition of competitive immunity to sustainable functioning. The main aspect in the study of the financial stability of the local regional budgets as an integral part of the competitive immunity of the region was the search for criteria and the development of a methodology for evaluating efficiency. The following performance indicators of local budgets were used: budget revenues; budget spending; intergovernmental transfers from the state budget; tax revenues; the amount of equalization subsidies; non-tax revenues; average population. An applied study of the methodology for assessing the financial sustainability of the budget as an object of managerial influence at the local level was carried out on the example of selected indicators of local budgets of all regions of Ukraine for 2018-2020. The calculation of the selected indicators was made on the basis of statistical data on the local budgets implementation, reports and decisions of regional councils on the regional budget. The distribution of the initial data set into clusters was analyzed with help of the Deductor business analytical platform, using the k-means clustering algorithm and Kohonen maps. Based on the results of the k-means algorithm, it was found that it is advisable to divide the sample for classifying regions into three groups. To compare and evaluate the effectiveness of the results obtained, as well as to supplement the analysis of the financial stability of the regions of Ukraine, Kohonen maps were used using the Deductor business analytical platform. It was revealed that both methods allow efficient clustering of data in a multidimensional space. The results of clustering obtained by different methods are consistent with each other and, when applied in a complex manner, make it possible to classify the elements of the sample with maximum likelihood and minimum error. The regions of Ukraine were grouped according to the financial stability of the local budget into three groups: regions with high financial stability, regions with medium financial stability and regions with low financial stability. The correct interpretation of the results obtained through a comprehensive analysis of financial stability in relation to the local budget using clustering or using neural networks allows not only to analyze the obtained values, but to compare them with the standard and conduct a comparative analysis relative to other regions, identify the influence of factors on the change in the integral indicator, give a predictive assessment for the future and justify the chosen strategy for strengthening competitive immunity for a particular region.
Significant changes are taking place in the structure of tourism participants. Due to the ageing of societies, the tourism sector has to respond to the increasing tourist activity of seniors. The main aim of our research was the recognition of the needs of senior tourists from selected regions of Poland, considering their health and financial situation as well as their physical activity. The study shows how to combine the knowledge of assumptions of active ageing with the actual views of senior tourists on tourism and active leisure. An additional objective was to determine the reasons why seniors gave up tourism and to compare the reasons why seniors from selected regions of Poland and seniors from other European countries did not participate in tourism. Based on Eurostat data, we identify the most common reasons for people not participating in tourism who are over 65 years of age. In 2020, we surveyed seniors. The respondents for the sample were selected as 65 years and older. In order to compare countries due to exclusion and non-participation of seniors in tourism, the results classification was used. To analyse the touristic behaviours of Polish seniors, we used correspondence analysis. As indicated by analysing the reasons for the non-participation of Europeans aged 65 and over in tourism, in most countries, financial and health reasons are ranked first or second in 2016 and 2019. In a survey of Polish seniors, except for the financial reasons responsible for non-participation in tourism, an additional obstacle was the language barrier in foreign tourism. The analysis of physical and tourist activity showed that non-participation in tourism is associated with low physical activity. Women reported that they were satisfied with their financial independence and most often used the opportunity of short-term tourism. The people who are fully or largely involved in organising their trips also willingly change their locations during their next travels.
The growing demand for ecological products is in line with the trend towards the ecologisation of consumption, which has become key in times of striving to achieve sustainable development that aims to satisfy consumer needs while respecting the natural environment and future generations. The shaping of pro-ecological attitudes and behaviours in consumers requires continuous monitoring of such behaviours on the market of ecological products and investigation of the factors that influence consumers’ decisions and market choices. The aim of the article is to present the motives behind the purchase of ecological products, and the factors that shape the purchasing decisions of these products by Polish consumers. The article is based on an in-depth study of the literature and the results of proprietary empirical quantitative research conducted on a national sample of 1032 respondents, of whom 509 had purchased an ecological product within the last 3 months, and 523 had not made such a purchase in this period. Analysis of the results revealed the motives for purchasing ecological products, divided into egotistical motives and altruistic motives. The variation in these motives was also indicated depending on the socio-demographic characteristics of the consumers studied. It was shown that there is a dependency between consumers’ self-assessment of their level of knowledge on the functioning of the natural environment and the effect of humankind on it, and the purchase of ecological products. Analysis was also conducted of the factors perceived by consumers as restricting the purchase of ecological products, as well as the likelihood of a growth in the demand for and consumption of such products. There was shown to be a dependency between the reasons perceived by respondents for restricting the purchase of ecological products or the decision not to make such purchases, and consumer attitudes towards ecology.
The aim of the research, the effect of which is this article, is to identify the hierarchy of selected approaches to building a strategy in companies from the sector of Energy and Utilities included in seven stock market indexes of the G7 countries The obtained results are related to the isolation of cognitive knowledge about the preferred approaches to the strategy in energy companies currently undergoing intensive changes and that are listed in the stock indexes of the G7 countries. The Authors proved that the strategy implementation in companies representing Energy and Utilities sectors is mainly based on the resource approach. Moreover, such an approach is supported by the classic tools of the positional school, resulting in shaping the competitive position in the sector of differentiating the Chamberlin’s rent.
Study of the literature and personal research experience have resulted in the identification of many challenges in the field of energy poverty, both in terms of social and technical dimensions. The research problems indicated in the paper and the proposed topics for further methodological and analytical work appear to be important not only from the perspective of the categories of energy poverty but also in the contexts of climate change, the ongoing energy transformation and attempts to implement a new energy model based to a large degree on unconventional and renewable sources of energy. This article also contains both methodological and scientific considerations.
Objective The aim of the study was to verify if soft-skills training is an effective intervention in reducing work-related stress among miners, that is, people who run the risk of losing health and/or life due to unpredictable natural forces or human error at work. Background The motivation for the intervention was based on Job Demands-Resources model where soft skills are job resources that help individuals to cope with or prevent high demands of the environment. The needed skills as well as work demands were first investigated and then a custom training was developed. The rationale for introducing soft-skills training into the work environment can be seen as compatible with the Human Capital Model (HCM) which is designed to stimulate positive organizational behaviour by providing an effective approach to ensure employees’ adequate coping with work-related stress. Method 96 volunteer employees were assigned to intervention (n = 48) and comparison (n = 48) groups. 16-hour tailored training covered tasks and simulation games related to communication, teambuilding, self-management and conflict resolution skills. Job Content Questionnaire, Occupational Stress Indicator (modified to fit the mining environment) and General Health Questionnaire were used in the study. A MANOVA with effect-size measures was conducted. Results Results revealed a significant increase in decision latitude and social support for the trainees. A substantial decrease in stress was also observed, along with a significant decrease in general health problems. There were no such changes in the comparison group. Conclusions A soft-skills training, including communication, teamwork, self-motivation and conflict-resolution skills, helped participants to cope better with the stressful environment and improved their mental health. These effects lasted three months later. Application The intervention improved miners’ psychosocial health and the strategies of coping with stress, which increased safety and health in the company. Investigating the effectiveness of such interventions included in the general Human Capital Model, as it was done in the study, might be a step forward towards building an interdisciplinary approach for health and safety and human resources.
Residents of rural areas buy products in the e-commerce market that are delivered to their homes (home deliveries) or to collection points (out-of-home deliveries). This poses last mile delivery challenges, which are of increasing interest to researchers. While urban research is widespread, a smaller number of rural studies are noticeable. The study aims to assess the factors differentiating the inhabitants of rural areas as to the familiarity and use of various methods of delivery of products purchased via the Internet and the reasons for choosing the preferred delivery methods. The paper uses the simplified SLR method in the literature section and multivariate data analysis in the empirical section. It contributes to the existing research in the form of the analysis of rural e-customers’ preferences for choosing a particular delivery method or parcel collection method when out-of-home delivery is conducted. It indirectly focuses on the environmental attitudes that may lead to the sustainable transition through reducing CO2 emissions while last mile delivery is performed. Regardless of choosing price or convenience over sustainable behavior for Polish rural e-customers, their preferences in last mile deliveries are focused on more eco-friendly methods of delivery. Such behavior is a good beginning for a more sustainable transition towards energy saving in Polish rural areas.
Foreign language learning has recently been transferred into an online or hybrid mode and this has brought many challenges for both the teachers and the students. Thus, the purpose of this study is to explore students’ subjective satisfaction with the use of digital media in their L2 acquisition conducted online, as well as to provide specific recommendations for meeting students’ needs in digital media L2 instruction. This is large-scale comparative research conducted in the Czech Republic, Poland, Romania, Iraq, and Malaysia. The data were collected through an online questionnaire in May, June, and July 2021 in the given countries. The findings reveal that students’ subjective satisfaction that is related to students’ attitudes toward the online learning process, the general usefulness of language, the role of the teacher, and the matters that affect the general process of teaching and learning all gained the positive answers. Whereas the items that are related to students’ subjective satisfaction toward language skills, digital-based reading, the effectiveness of online education over face-to-face, and communicating with teachers and peers via social media are all gained negative results. These results need further analysis but they can be an impetus for much larger research and further implications to optimize L2 acquisition.
Despite their nutritional value and increasing supply of oils from unconventional plants to the oil industry, edible niche oils do not have high sales. The market for niche oils is geared toward an ever-growing volume of conscious consumers distinguished by their sensitivity to product quality, packaging type, price, sourcing technology, and variety of product use. In the literature, there is a lack of research on consumer preferences and expectations of niche oils. This article continues a series of studies on niche oils aimed at determining the proper technological parameters for production, discussing the economic aspects of niche oil production, and researching consumer behavior concerning these products. It aims to determine Polish consumers’ awareness and expectations of edible niche oils, select the oils of most significant interest to consumers, and determine the factors influencing consumers’ propensity to try edible niche oils. The survey, in the form of a questionnaire, was conducted using the CAWI method on a representative sample of 1000 Polish consumers. Consumers were divided into four segments: those who regularly consume niche oils, consumers who have experience with niche oils, consumers who are familiar with them but have not tried them, and consumers who do not know niche oils. Data were analyzed collectively and separately for each segment using one-way ANOVA. Grape seed oils and edible castor oil are the most interesting to consumers. Consumers’ purchasing decisions are influenced by price, nutrient content, and sustainable production free of GMO products and harmful chemicals. Therefore, manufacturers should increase the availability of certain oils (e.g., peanut oil). Sensory qualities, place of production, and brand recognition are secondary selection criteria. Future research should focus on the organoleptic evaluation of products available on the market. Research results may be used to create production and marketing strategies to make niche oils more attractive to consumers.
Background and Objectives: The assumption of responsibility in dealing with chronic diseases is of relevance in a resource-oriented and not only deficit-oriented medicine, especially in dealing with chronic diseases, including patients with chronic heart failure. The aim of the present study is to examine, based on the model of “locus of control”, whether there are different patterns that would be relevant for a more targeted education and support of self-management in dealing with heart failure. Materials and Methods: For this purpose, a sample (n = 758) from 11 Polish cardiology centers have been assessed using the standardized self-assessment scale Multidimensional Health Locus of Control (MHLC), consisting of three dimensions: (i) internal localization of health control; (ii) external control by powerful others; (iii) external control by chance. Results: Using these three criteria, nine different clusters were extracted (mean size: 84 ± 33 patients, min 31, max 129). Three clusters included over 100 patients, whereas only two included less than 50 people. Only one cluster gathered 42 patients who will be able to cooperate with professionals in the most fruitful way. There were two clusters, including patients with beliefs related to the risk of ignoring professional recommendations. Clusters where patients declared beliefs about others’ control with low internal control should also be provided with specific help. Conclusions: The division into clusters revealed significant variability of belief structures about health locus of control within the analyzed group. The presented methodological approach may help adjust education and motivation to a selected constellation of beliefs as a compromise between group-oriented vs. individual approach.
Unrefined vegetable oils from niche oilseeds are now sought after by consumers because of their unique nutritional properties and taste qualities. The color and flavor intensity of niche oils is a big problem, and their refining is not industrially feasible due to the small production scale. The study undertaken aimed analyze the effect of changing the amount of phytosterols (PSs) after the bleaching process of hemp oils of the ‘Finola’, ‘Earlina 8FC’ and ‘Secuieni Jubileu’ varieties. Cold-pressed (C) and hot-pressed (H) crude vegetable oils were bleached with selected bleaching earth (BE) at two concentrations. The post-process BE was extracted with methanol. The amount of PSs in the crude oils and the extract after washing the BE with methanol was analyzed by GC (gas chromatography). The study shows that the bleaching process did not significantly affect the depletion of PSs in the oils. Trace amounts of PSs remain on the BE and, due to methanol extraction, can be extracted from the oil. The conclusion of the performed research is that the bleaching of hemp oil does not cause depletion of the oil, and it significantly improves organoleptic properties. The oil bleaching process results in an oil loss of less than 2% by weight of the bleached oil, while the loss depends on the type of BE used. The study shows that the loss of phytosterols after the bleaching process averages 2.69 ± 0.69%, and depends on the type of BE used and the oil extracted from different varieties of hemp seeds.
The article presents analysis and evaluation of information usefulness efficiency for recipients with secondary and higher education using the example of information and shopping websites. Different levels of quality and information usefulness efficiency have an impact on different ways of processing information by users which, in turn, may result in different consumer behavior and their decisions. The study describes the basic methodological assumptions, the research evaluation procedure of information usefulness efficiency, and the forms of informational content and various forms of information presentation and visualization, as well as the results of data analysis from the study conducted on a group of respondents. In order to determine the factors that have the greatest impact on the perception of information usefulness on websites by users, the data obtained from the study using various methods, such as online questionnaire, usability testing and heuristic analysis, were analyzed using the DEA method, which is usually used for the analysis and evaluation of efficiency. The results of the research presented in this article can be useful in creating assumptions for methods of content presentation and visualization of various forms of content building for the needs of different user groups for information and business websites.
The current production of water energy in Poland is much lower than the theoretical and technical potential. The aim of the article is to analyse the current state of hydropower in Poland as well as the prospects and conditions for development. Poland's total technical hydropower potential is estimated at 12,000–14,000 GWh/year, but currently, approx. 20% of this potential is used. The considerations undertaken in the study concern, for example, pumped-storage power plants and the development of small hydropower plants. Hydropower plants are not only important from the point of view of electricity production and storage, but also fulfill many other functions, including the general social, which is an essential element of the implementation of the concept of sustainable development. The analyses show that the hydropower sector in Poland may be an important element of low-carbon energy and an important element of energy security. Increasing the volume of electricity production from hydropower by 5% will contribute to the growth in CO2 reduction by 140,702 tons. The stabilization function of the power system in Poland is also significant.
Their possibilities in decision-making training cause the growing game's popularity. Management games allow for avoiding potential mistakes related to business choices, but they require several updates and improvements in terms of context and new trends. The business environment is constantly changing and requires new creative approaches to be trained. One of them is business greening and realizing the idea of sustainable behaviour and engagement in managerial games. This paper proposes managerial game improvements to improve game engagement and become more interesting for students because of its real business world connection. The object of the research and analysis is the Marketplace®, which is the educational, managerial game. This paper presented the game analysis of three levels to propose recommendations for the Marketplace game development. The main finding is that an assessment survey should occur in the game climax to engage and immerse students in the game. The survey questions should also be reexamined to develop a more dynamic game analysis. Then the game has to consist of sustainable development of practical solutions and enable students' creativity to raise their engagement.
The usage of machine learning methods in the financial sector, regarding repayment prediction or forecasting, is quite a new topic, constantly gaining importance. The concept of the alternative costs in the literature covering machine learning and deep learning occurs most often in connection with the non-financial areas as costs of lost benefits. This empirical paper presents research dedicated to deep learning used in forecasting the alternative costs of leasing represented by the variable KUK_PRC. The study is based on the experimental approach and uses real organization data to solve forecasting problems in the financial area with AI solutions. This research contributes to the science by identifying and exploring the research gap in applied economics and finances. The main finding of this paper is the proposed forecasting ACSeq-DNN model that forecasts opportunity costs with more minor deviations from actual values than the forecasting achieved by state-of-the-art models.
This paper delivers up-to-date bibliometric research dealing with deep learning to forecast alternative leasing costs. The study presents a theoretical approach that is developed by the bibliometric maps. The aim of this paper is the identification and exploration of the research gap. The study illustrates the relationships between different science areas, such as applied economics and finance, computer science, deep learning, and machine learning. The alternative costs in the literature are related to non-financial areas and the other Artificial Intelligence (AI) methods to calculate them. There is a scarcity of alternative costs calculation with deep learning procedures or their theoretical analysis. To fill this gap, this study uses VOSviewer software to explore query results in the Scopus database and to illustrate the knowledge about alternative costs in deep or machine learning. The results show that: (I) the number of publications tends to increase gradually; (II) prediction or forecasting of the alternative costs is connected directly neither to the lost benefits nor costs and risk assessment; (III) most studies concern the use of statistical methods in forecasting, but only a few use machine learning algorithms or decision trees. This scientific paper is a starting point for future studies dedicated to deep learning procedures to forecast alternative costs in leasing or other financial processes.
The paper presents issues related to methods used in breast histopathological images tumor changes detection. The problem is connected with sustainable health issues which focus on the improvement of health and better delivery of healthcare, rather than late intervention in disease, with resulting benefits to patients and to the environment on which human health depends, thus serving to provide high quality healthcare. The main purpose of the paper is to develop a model based on deep artificial neural networks for the cancer detection in histopathological breast images. The implementation of the proposed model fits in with the concept of sustainable health through the support of the work of doctors in their decisions, diagnosis and in the reduction of the human workload and time, which can be referred to improve the health services. Data set contains 277524 samples from 163 breast histopathological images taken with the WSI scanner. The model is based on a convolutional neural network in the ResNet-18 architecture, which consists of residual blocks. During the final validation on the test set, the network achieved an accuracy of 93.6% and a 87.3% sensitivity in the detection of cancer tissues. The overall performance of the model is characterized by an F1-score of 0.887. The obtained results indicate the possibility of using the system in clinical conditions.
The problem of student structure prediction is very important from the viewpoint of the fundamental planning and control functions associated with this specific form of management. The purpose of this study is to present the results of an experiment involving the prediction of student structure based on the use of a machine learning solution (GANs) and compare them against real data obtained from a registry system of a European public institution of higher education in economic sciences. The research attempt provided a wealth of knowledge and insight into practical skills related to the potential application of such solutions and revealed a number of problems associated with student structure prediction tasks. The experiments revealed that – for 11 out of the 48 examined datasets – the PSI index was in excess of 75% but was decidedly lower for the remaining sets (with 18 sets assessed below the margin of 50%).
Machine learning is nowadays popular area of science research. As the amount of accessible data is still increasing, therefore the machine learning methods can be used for many applications. One of the more detailed topics, which is commonly analyzed in practice in regard to financial decision supporting, is the support for decision making on stock market. It has been noticed that the main focus is on the developed markets like in Asia, West Europe or USA. As the Polish stock market is also recognized as the developed market, here in opposite to Asia, West Europe or USA markets, the shortage of practical implementation of machine learning algorithms is perceptible. With this paper various learning algorithms have been used to determine its backtesting performance. The main goal of this paper is to examine the effectiveness of selected machine learning algorithms and to find the best one for stock data by comparing several selected algorithms using a backtesting environment on the same data sets and general parameters. For this purpose, experiments were carried out on one random seed and then out on 100 different seeds.
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