Background Sustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding. Methods Research was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx—Microsoft Research’s Bayesian Network Authoring and Evaluation Tool. Results Due to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks. Conclusion Testing of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.

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The aging workforce challenges companies to keep their aging employees employable in the workforce. This paper gives an indication as to which employees are more likely to be interested in further learning and employability. Specifically, the aim of this study was to investigate the role of chronological age and achievement goal orientations for informal and formal learning and employability. It was found that informal learning has a significant positive relation with several dimensions of employability. Furthermore, mastery-approach goal orientation also shows a significant positive relation with informal learning and employability. In addition, age had no significant relation with the achievement goal orientations. The paper stresses the need to consider characteristics other than chronological age, such as goal orientations, when considering employees’ learning behavior and employability.