Nowadays, when there is much concern about the demographic decline of Europe and the stringent need for public policies that support intelligent, sustainable, and inclusive growth in times of population ageing, this study aims to validate several hypotheses regarding the post-graduation migration intentions of students in economics. To analyse these intentions in the context of Romanian yearly increases of out-migration flows to Western countries, the answers to the questionnaire used for this study were obtained from three important Romanian universities. Using georeferencing, neural networks-based data mining, and two types of regression analysis, the results represent a relevant contribution to the limited body of literature. They empirically show that motivations and attitudes towards a meritocratic environment for professional advancement, and individual freedom are positive key factors for students’ migration intentions after graduation. In addition, the paper emphasises the secondary role of religiosity and intergenerational transfer of parental migration experience. It also finds that, although the income level has some influence on these intentions, its role is far less important than the one emphasised in the specific literature, which leads us to the conclusion that non-economic motivations matter more than the economic ones for the potential decision to migrate after graduation.
Our primary research hypothesis stands on a simple idea: The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics. And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago. We implemented our model based on Computer Science Ontology (CSO) and analyzed 44 years of publications. Then we derived the most important concepts related to Cloud Computing (CC) from the scientific collection offered by Clarivate Analytics. Our methodology includes data extraction using advanced web crawling techniques, data preparation, statistical data analysis, and graphical representations. We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology. Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.
In this paper, we explore the determinants of being satisfied with a job, starting from a SHARE-ERIC dataset (Wave 7), including responses collected from Romania. To explore and discover reliable predictors in this large amount of data, mostly because of the staggeringly high number of dimensions, we considered the triangulation principle in science by using many different approaches, techniques and applications to study such a complex phenomenon. For merging the data, cleaning it and doing further derivations, we comparatively used many methods based on spreadsheets and their easy-to-use functions, custom filters and auto-fill options, DAX and Open Refine expressions, traditional SQL queries and also powerful 1:1 merge statements in Stata. For data mining, we used in three consecutive rounds: Microsoft SQL Server Analysis Services and SQL DMX queries on models built involving both decision trees and naive Bayes algorithms applied on raw and memory consuming text data, three LASSO variable selection techniques in Stata on recoded variables followed by logistic and Poisson regressions with average marginal effects and generation of corresponding prediction nomograms operating directly in probabilistic terms, and finally the WEKA tool for an additional validation. We obtained three Romanian regional models with an excellent accuracy of classification (AUROC > 0.9) and found several peculiarities in them. More, we discovered that a good atmosphere in the workplace and receiving recognition as deserved for work done are the top two most reliable predictors (dual-core) of career satisfaction, confirmed in this order of importance by many robustness checks. This type of meritocratic recognition has a more powerful influence on job satisfaction for male respondents rather than female ones and for married individuals rather unmarried ones. When testing the dual-core on respondents aged 50 and over from most of the European countries (more than 75,000 observations), the positive surprise was that it undoubtedly resisted, confirming most of our hypotheses and also the working principles of support for replication of results, triangulation and the golden rule of robustness using cross-validation.
In this paper, we analyze the determinants of job satisfaction for employees over the age 50 or more, using the latest SHARE-ERIC dataset (Wave 7) filtered for Romania (over 2000 records). After applying logistic regressions with average marginal effects, we obtained an overall and seven regional models which emphasize that a good atmosphere at the workplace and the deserved recognition received for the work done are the most reliable predictors of career satisfaction, confirmed in this order of importance by many other robustness checks. Particularly, in the case of respondents from the Western part of Romania, we found that meritocracy-based influence, namely deserved recognition, counts almost as much as the workplace atmosphere. For these individuals, previous educational performance and lifetime employment at a single job matter more than the previous dual-core on job satisfaction. Unexpectedly, the adults from central romania present a negative influence of life satisfaction on job satisfaction due to an unbalanced work-family vision of life. The locus of control has different effects on job satisfaction in south and south-western regions, while in the north-east, meaning in life is negatively influencing job satisfaction. Bridge employment exerts a negative influence on career satisfaction in the north-west, and in the South-East, and interpersonal trust has a positive effect.
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