Sustainable innovation at a company level drives economic, environmental and social improvement at a national level. Recent evidence has shown that businesses have increased the managerial attention and investments dedicated to sustainability. This paper aims to identify the most important drivers supporting companies to develop innovation activities oriented towards making the business models more sustainable. We explore microdata from the 2016 Innobarometer “EU Business Innovation Trends” (Flash Eurobarometer 433), covering 8635 companies from 29 countries. Using statistical classification methods, we identify the most important factors that are related to innovation activities that have the potential to shape the efficiency of raw material usage and environmental protection. The most relevant factors emphasized by our analysis are: innovation performance of the country (innovation), percentage of the company turnover invested in innovation activities, percentage of total turnover invested in acquisition of machines, equipment, software or licenses, percentage of total turnover invested in company reputation and branding, including web design, percentage of total turnover invested in software development.. Also, our analysis highlights the skills that are needed the most by companies in order to support their innovation activities targeting sustainability. Our results are useful for better understanding the attention that is given to sustainability by innovative companies, and what the main factors that boost innovation dedicated to sustainability are.

Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&D professionals.

Perceived academic self-efficacy represents an important component of students’ mental health and well-being. The link between efficacy beliefs and level of academic performances relies on the fact that they regulate the anxiety and foster motivation, school engagement, effort, and perseverance of students. This paper aims to identify factors that are conducive for more efficacious beliefs in different socio-economic and educational contexts. We build our analysis on data collected from a sample of Romanian upper secondary education students on their beliefs in relation to their ability to perform at the baccalaureate exam. We employ decision tree models in order to unveil the way factors interact and predict perceived academic self-efficacy, with focus on the positive support received from parents and teachers, as well as on features of the school environment. Our results can be useful for building more resilient educational environments that support mental health and academic well-being of students.

Innovation and knowledge-intensive economy are considered crucial for addressing challenges and opportunities currently faced by Europe. The present paper aims to provide inputs for building a long-term vision aiming to promote innovation and knowledge economy for fostering smart, sustainable and inclusive growth. We analyze microdata from the 2016 Innobarometer “EU Business Innovation Trends” (Flash Eurobarometer 433), covering 14,112 companies from 30 countries in order to shed light on main drivers and results of business investments in innovation. We focus our analysis on high-tech industries and knowledge-intensive services. Additionally, expected positive impacts of innovation in knowledge-intensive sectors are explored in terms of job creation, digital economy, environment protection, health, transport, food quantity and quality, development of smart cities, etc. Our results are useful for designing an improved support for the emergence and impact of knowledge-intensive economy.

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.