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Artigo em Inglês | MEDLINE | ID: mdl-34886590


Students' commitment and engagement in the educational process are shaped by a dense combination of factors, with effects on educational attainment and on the length of their educational careers. Decisions of prolonging education by enrolling in master's degrees are beneficial for both individuals and societies, as such programs provide higher levels of specialized skills Longer educational careers are favored by a mix of factors acting at the level of individual, university, or wider environment. We focus our study on exploring factors conducive for students' intentions to pursue master's degrees considering longer educational careers as desirable outcomes. Thus, this article investigates how the individual and environmental factors interplay and shape the predisposition of students to prolong their educational career by enrolling in master's degrees. For this, we applied three-level logistic regression models for a sample of 502 students enrolled in their final year of bachelor studies grouped by universities and universities grouped by counties. The empirical results revealed that the final grade, the father level of education, the type of working contract, and job seniority are individual-level determinants influencing the decision of enrolment in a master's program. At the university level, the type of university and the university performance score positively impact the students' decision to enroll in a master's program. At the county level, the empirical evidence pointed out the significance of determinants such as the proportion of students enrolled in bachelor studies; participation rate in education and training; employment level in high-technology sectors (HTC), total-knowledge intensive sectors (KIS), and knowledge-intensive high-technology sectors (KIS_HTC); proportion of persons with tertiary education employed in science and technology; proportion of scientists and engineers; local development; R&D expenditure, personnel, and researchers in the business sector; average gross earnings; density of active firms; birth rate of companies; proportion of innovative enterprises or those introducing product innovations on the decision to enroll in a master's program.

Artigo em Inglês | MEDLINE | ID: mdl-34948908


This research paper aims to analyse how consumer emotions have evolved during the pandemic period in comparison with the pre-pandemic period in relation to restaurant demand in the Romanian fine-dining industry and uses valuable information based on social-media sentiment analysis and content analysis. Focusing on theories of consumer behaviour, the study aims to emphasize how, under the influence of an epidemic crisis caused by an infectious disease, individual behaviour adapts to the "new normal", embracing a series of changes in the preferences, attitudes, and cognitive choice-making processes. The article takes into account a comparative analysis of the consumer emotions between the pre-COVID-19 pandemic period (2010-2019) and the pandemic period (2020-present), based on the online reviews provided by customers for five fine-dining restaurants from Bucharest, the capital city of Romania: The Artist, Relais & Chateaux Le Bistrot Francais, Casa di David, Kaiamo, and L'Atelier. The research was based on two mining analyses-content analysis and sentiment analysis-and explored the emotional intent of words, with the data being collected from TripAdvisor through web-scrapping. The empirical results defined the fine-dining experience during the pandemic as being associated with the quality of the dishes and also with the quality of the service. The overall consumer sentiment in the direction of the restaurants analyzed is positive. The sentiment research found that throughout the epidemic, the consumers' attitudes about restaurants deteriorated. In this sense, consumers seem to be less satisfied with the restaurants' services than before the pandemic. This is another thing that the restaurants had difficulties in when adapting their operations for the pandemic.

COVID-19 , Restaurantes , Comportamento do Consumidor , Emoções , Humanos , Pandemias , Romênia/epidemiologia , SARS-CoV-2
Artigo em Inglês | MEDLINE | ID: mdl-34769684


Economic crises cause significant shortages in disposable income and a sharp decline in the living conditions, affecting healthcare sector, hitting the profitability and sustainability of companies leading to raises in unemployment. At micro level, these sharp decreases in earnings associated with unemployment and furthermore with the lack of social protection will impact the quality of life and finally the health of individuals. In time of crisis, it becomes vital to support not only the critical sectors of the economy, the assets, technology, and infrastructure, but to protect jobs and workers. This health crisis has hit hard the jobs dynamics through unemployment and underemployment, the quality of work (through wages, or access to social protection), and through the effects on specific groups, with a higher degree of vulnerability to unfavorable labor market outcomes. In this context, providing forecasts as recent as possible for the unemployment rate, a core indicator of the Romanian labor market that could include the effects of the market shocks it becomes fundamental. Thus, the paper aims to offer valuable forecasts for the Romanian unemployment rate using univariate vs. multivariate time series models for the period 2021-2022, highlighting the main patterns of evolution. Based on the univariate time series models, the paper predict the future values of unemployment rate based on its own past using self-forecasting and implementing ARFIMA and SETAR models using monthly data for the period January 2000-April 2021. From the perspective of multivariate time series models, the paper uses VAR/VECM models, analyzing the temporal interdependencies between variables using quarterly data for the period 2000Q1-2020Q4. The empirical results pointed out that both SETAR and VECM provide very similar results in terms of accuracy replicating very well the pre-pandemic period, 2018Q2-2020Q1, reaching the value of 4.1% at the beginning of 2020, with a decreasing trend reaching the value of 3.9%, respectively, 3.6% at the end of 2022.

Qualidade de Vida , Desemprego , Economia , Emprego , Humanos , Renda , Romênia , Fatores Socioeconômicos
Entropy (Basel) ; 23(3)2021 Mar 09.
Artigo em Inglês | MEDLINE | ID: mdl-33803384


Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt-Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000-December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018-2020. The forecast of unemployment rate relies on the next two years, 2021-2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt-Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold-Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.

Healthcare (Basel) ; 9(2)2021 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-33546111


Medicinal oxygen plays an important role in healthcare, being essential for the existence and maintenance of the health of millions of people, who depend on medicinal oxygen every day, both in hospitals and at home. Medicinal oxygen is the primary treatment administrated to the majority of patients suffering from respiratory problems and low levels of oxygen in the blood, and in the context of the actual health crisis caused by the new COVID-19, the challenge is represented by increasing the supply of medicinal oxygen while reducing cost so that it is accessible where it is needed most, free at the point of use. It will take increased investment and commitment to put oxygen at the center of strategies for universal health coverage. In this context, it becomes essential to investigate the main characteristics of the Romanian market of medicinal oxygen, highlighting top key players, market development, key driving factors, types of products, market perspectives as well as shedding light on the segmentation of this particular market based on considerations regarding regions, hospital competence class and hospital specialization. Also, the research aims to explore the regional disparities in the decision of using O93%medicinal oxygen, revealing the main factors related to the usage of this type of product among Romanian public hospitals. The research relies on the first quantitative survey regarding medicinal oxygen usage among 121 public hospital units from a total of 461 public hospitals in 2018, which meet the specific requirements: includes the entire population according to the list published on the website of the Ministry of Health, is the most recent data and does not show repetition. The sampling was of probabilistic stage-type stratification, with the following sampling layers: hospital county distribution, hospital competence class officially assigned by the Ministry of Health and also area of residence (urban/rural). In order to analyze the main characteristics of the Romanian oxygen market, the following methods have been used: analysis of variance (ANOVA) together with Kruskal-Wallis, Pearson correlation coefficient as well as Goodman and Kruskal gamma, Kendall's tau-b and Cramer's V, as well as multilevel logistic regression analysis using hierarchical data (hospitals grouped in regions). The Romanian market of medicinal oxygen is rather an oligopoly market characterized by the existence of a small number of producers and two types of products currently used for the same medical purpose and having a substitutable character: medicinal oxygen O99.5%, and medicinal oxygen O93%. An overwhelming proportion of public hospitals agree that both types of medicinal oxygen serve the same therapeutic purpose. The Romanian market of medicinal oxygen highlighted a significant segmentation on considerations based on regions, hospital competence class and hospital specialization. Regarding the main perspectives, the Romanian market of medical oxygen keeps the growth trend registered globally, with development perspectives for competitors. Exploring the regional disparities in the decision of using O93 medicinal oxygen, the empirical results acknowledged the important role of unitary price, hospital capacity and the relevance of this product seen as a medicine. Medicinal oxygen is vital in sustaining life, proving its utility mainly in the context of the actual health crisis. In this context, the Romanian local market exhibits prospects for further development, being characterized by an important segmentation depending on regions, hospital competence class and hospital specialization.