Small and medium-sized enterprises (SMEs) are crucial for local economic development, playing a noteworthy role in job creation, poverty alleviation and economic growth, but they encounter many funding barriers. The purpose of the current paper is to investigate the impact of investments and innovation on territorial economic growth, as measured by turnover, for Romanian active enterprises, especially SMEs, over the period 2009–2017. By estimating several log–log linear regressions, the quantitative outcomes provide support for a positive influence of investments on turnover. The association was confirmed both for all active enterprises at the national level, as well as for micro, small, middle-sized and big companies. As regards expenditures on innovation, a positive impact on turnover was acknowledged for all enterprises and particularly for big companies, but there was an absence of any statistically significant relation in the case of SMEs. The impact of firm size on turnover was positive for all active enterprises at the national level, along with active micro-units. Also, the estimation results show a positive impact of the number of active micro-units on territorial economic growth. The empirical findings are relevant to managers and policymakers in order to stimulate, encourage and offer support to SMEs’ development through their strategies.

The well-being of children and young people has been affected by the COVID-19 pandemic. The shift to online education disrupted daily rhythms, transformed learning opportunities, and redefined social connections with peers and teachers. We here present a qualitative content analysis of responses to open-ended questions in a large-scale survey of teachers and students in Romania. We explore how their well-being has been impacted by online education through (1) overflow effects of the sudden move to online classes; (2) identity work at the individual and group levels; and (3) Students’ and teachers’ presentations of self in the online environment, with a focus on problematic aspects of webcam use. The results indicate that both students and teachers experienced ambivalence and diverse changes in well-being, generated by the flexibility, burdens, and disruptions of school-from-home. The identities associated with the roles of teacher and student have been challenged and opened for re-negotiation. Novel patterns have emerged in teachers’ and Students’ identity work. Failure or success at the presentation of self in online situations is relevant for the emotional valence of learning encounters, impacting well-being. Online classes have brought about new ways to control one’s presentation of self while also eliminating previous tactics and resources. The controversy regarding webcams has captured this duality: for some, the home remained a backstage that could not be safely exposed; for others, the home became a convenient front stage for school. Well-being was affected by the success of individual and collective performances, and by student-teacher asymmetries. Overall, our study of online learning indicates powerful yet variable influences on subjective well-being, which are related to overflow effects, identity work, and presentation of self.

Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue.

The Roma constitute the second largest ethnic minority in Romania, after the Hungarian minority, representing approximately 3% of the total population, according to the most recent census data (Ins...