The development of employability skills has dominated the educational research the recent years. This study reviews the notion of employability skills and their emergence in the Higher Education Institutions (HEIs) through the analysis of the relevant literature. Furthermore the study explores employability skills development in the Greek HEIs and the difficulties associated with it considering work-placements structure and current attitudes especially in the area of accounting education.

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Purpose The purpose of this study is to explore the underlying dimensions of environmental behavior (EB) and examine how environmental education (EE) and ecological sensitivity (ES) motivate the EB of Business Administration and Accounting students (BAS). Design/methodology/approach A questionnaire survey was conducted and a sample of 190 BAS was randomly selected from the departments of Business Administration and Accounting and Finance at the University of West Attica (UNIWA), Greece. Findings The analysis was structured upon four underlying components under the EB of the sample: information seeking, recycling, green consuming and active participation. A positive relationship between EB and EE was revealed, while EB and ES were moderately interrelated. An important result was the hesitation of students to convert EE and ES to active participation and green consuming behavior, thus reaffirming similar results from other studies. Research limitations/implications The findings should be further developed using larger samples among other higher education institutions. Future research could be extended to students who reside at sub-urban or rural regions or students who are educated upon diverse academic disciplines. The basket of questions can be enriched with issues of immediate concern among future business executives such as the “ethical” role of accountants or the value creation for local societies. Originality/value The significance of this study lies on associating students’ EB with formal EE with personality characteristics such as ES. Educational policy-makers can enrich the curricula of BAS with environmentally oriented courses and teaching methods that can increase the active participation of students.

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Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents’ perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest.