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.

Financial institutions are faced with the need to assess the creditworthiness of a borrower that applies for a loan. In this regard, data scientistscan produce valuable insights that can explain customer profile and behavior. This paper proposes an analysis of a database of customers where a part of them were unable to repay their loans and got into default status. By using the methodology of data mining and machine learning algorithms, a series of predictive models were developedusing classifiers such as LightGBM, XGBoost, Logistic Regression and Random Forest in order to evaluate the probability of a customer's enteringloan default. Three sampling scenarios were created to compare the classification between imbalanced and balanced data sets. Moreover, a model comparison analysis was performed to identify the best classifier by considering the model performance metrics: AUC score, Precision, Recall and Accuracy. The best results were observed for the Random Forest optimal classifier applied on the combined scenario under-over sampling, with a representative AUC of 0.89.

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.