Abstract
This research analyses the degree of maturity of project management practices. The study object were the organizations that develop their main activity in manufacturing machines for the mining and quarrying and construction industries, in the region of Tamega e Sousa (Portugal). The theoretical background is the Organizational Project Management Maturity Model (OPM3 (R)). The methodological approach adopted was case study research (multiple case study). The results give us cues to believe that the degree of maturity of this industry is very low. The three companies analysed revealed levels of maturity around 15%. The reasons behind the low level of maturity are related with the lack of adoption of organizational enablers and the low level use of project management practices: 48% of project management processes are not used, and 28% are classified into an ad-hoc stage. In a management context this research can be a starting point to improve project management in organizations. Yet, one can conclude that these organizations develop their daily operations within a project management perspective but this is not recognized by them. Even identifying processes that are part of recognized good project management practices, these organizations score for a relatively low maturity level. This research brought important practical contributions, by raising at least awareness of the project management value in the organizations studied.

Abstract
The challenges of financial systems have immediate or medium-term social effects. The financial industry is constantly searching for measures to reduce these challenges, especially for those with little or no access to financial services. While current communication technologies make services more accessible through digital mobile platforms, there are still difficulties in establishing viable customer arrangements. In addition to the increased investment in financial technologies, nonbanking financial institutions have now expanded to offer more flexible services tailored to individual circumstances, especially those in isolated rural areas. This research outlines the network model of nonbanking financial institutions in Romania, as well as a microfinance model, based on the financial analysis of four national indicators of nonbanking financial institutions. Data used are presented in absolute values, from the annual numerical series for the reference period 2007-2017. The new initiatives and features incorporated in this Romanian model should be applicable elsewhere and will actively contribute to the expansion and sustainability of financial services, with a positive inclusive impact on society.

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This paper analyses the directions and causes of student migration in the information society at economic universities in Poland and Portugal. An international survey was conducted among students born between 1981 and 1995. The research included a group of 121 Polish and 55 Portuguese students. The conclusions of the study confirm the hypothesis that the place of residence/country of origin has a significant effect on the direction of migration. Furthermore, the causes of migration are largely dependent on the gender of the respondents and their professional status.

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In order to investigate the factors influencing the sustainable guarantee network and its differences in different spatial and temporal scales, logistic regression algorithm is used to analyze the data of listed companies in 31 provinces, municipalities and autonomous regions in China from 2008 to 2017 (excluding Hong Kong, Macau and Taiwan). The study finds that, overall, companies with better profitability, poor solvency, poor operational capability and higher levels of economic development are more likely to join the guarantee network. On the temporal scale, solvency and regional economic development exert increasing higher impact on the companies' accession to the guarantee network, and operational capacity has increasingly smaller impact. On the spatial scale, the less close link between company executives and companies in the western region suggests higher possibility to join the guarantee network. The predictive accuracy test results of the logistic regression algorithm show that the training model of the western sample enterprises has the highest prediction accuracy when predicting enterprise behavior of joining the guarantee network, while the accuracy is the lowest in the central region. When forecasting enterprises' failure to join the guarantee network, the training model of the central sample enterprise has the highest accuracy, while the accuracy is the lowest in the eastern region. This paper discusses the internal and external factors influencing the guarantee network risk from the perspective of spatial and temporal differences of the guarantee network, and discriminates the prediction accuracy of the training model, which means certain guiding significance for listed company management, bank and government to identify and control the guarantee network risk.

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