The health crisis generated by the COVID-19 pandemic has induced, among other things, an increase in the importance of remote work or teleworking (TL) in the current period. The objective of this research is to identify the economic and social impact of telework in changing the behavior of employees in Romania. The research was conducted approximately one year after the onset of the pandemic until the beginning of the vaccination period in Romania. The research proposed includes three main directions of analysis of the extracted data, which are related to telework efficiency, this being considered one of the most important indicators for a company. In order to obtain conclusive results, we used a mixed methodology, combining results obtained through a survey based on a self-administered electronic questionnaire, with a data mining analysis. Detailed analysis of the groups identified based on work efficiency allowed us to highlight the most common employee profiles. This analysis was doubled by a second classification experiment, which provided us a more detailed analysis of the groups identified based on job satisfaction and highlighted the most common employee profiles. The expansion of telework in various economic areas is a result of adaptation to the new economic and social conditions caused by the COVID-19 pandemic.

The aim of this chapter is to explore the application of data mining for analyzing performance and satisfaction of the students enrolled in an online two-year master degree programme in project management. This programme is delivered by the Academy of Economic Studies, the biggest Romanian university in economics and business administration in parallel, as an online programme and as a traditional one. The main data sources for the mining process are the survey made for gathering students’ opinions, the operational database with the students’ records and data regarding students activities recorded by the e-learning platform are. More than 180 students have responded, and more than 150 distinct characteristics/ variable per student were identified. Due the large number of variables data mining is a recommended approach to analysis this data. Clustering, classification, and association rules were employed in order to identify the factor explaining students’ performance and satisfaction, and the relationship between them. The results are very encouraging and suggest several future developments.

Climate change (CC) represents a real fact with consequences that start to be seen more and more often and that is why it cannot be ignored anymore. It affects many domains of the human activities and also the health of the people. Climate-specific actions are needed to be taken in order to protect the people and to save the environment. For each affected domain, new regulations and actions regarding climate change prevention must be designed, promoted and implemented. Besides phenomena like heat waves, storms, increased temperature, forest fires, floods, etc. which represent direct results of the CC, also indirect results like human health may be encountered. Human health is affected by elements that are having a big impact over the environment of the people and over the resources that they need (resources like water, food, air, natural resources, etc.). CC has also implications on people migration, the fight over the natural resources, political and economic environments. This paper offers an overview of the most important factors that are affecting the health of the people from the CC point of view and which are the main challenges that most affected countries from EU are dealing with.

Greenhouse gas emissions (GE) represent an element that influences the lives of all people on the planet. This action must be controlled and prevented because the negative effects are starting to appear more and more in everyday life, sometimes with devastating consequences from a climate point of view and not only for the inhabitants of certain regions. At the European level, one of the main measures taken was the implementation of the Green Deal as a response to the fight against GE. The purpose of this article is to offer a description of the main elements that are influencing the GE, as well as the role of the Green Deal. It also aims to identify the characteristics of the EU countries from the GE point of view before and after the Green Deal was proposed. In this regard two more cluster analyses are also carried out regarding GE at the European level. One analysis concerns the identification and evolution of the main groups of countries from this point of view for years 2018 and 2020. The second analysis concerns the main fields in the industry for year 2020. The used methodology was DM-CRISP. In the final part of the article the obtained results are analyzed, a discussion is added based on them and also a conclusion section.

No abstract

No abstract

The chapter presents a study made in order to find out how the e-learning experience enhances the social presence in the community of practice. The study was carried out for the online master degree programme in project management, delivered by the Academy of Economic Studies, Bucharest. The main research method was a survey and the research instrument was a questionnaire. Statistics and data mining were applied. Statistics was applied to check hypothesis and quantify the correlation significance. Due to the large number of the variables and the indirect relationships, the analysis paths become very complex and it would be extremely difficult to manage the analysis workflow. So, the data mining approach was chosen. As a theoretical framework and analytical perspective for this research, Wenger’s theories of learning in Community of practice (CoP), and the social presence model of Garisson et al., are applied. The study revealed that the characteristics of the online social presence in learning environments enhanced the students’ interest for CoPs. Another finding of this study is that for project management area there is not a significant correlation between the learning domain and that of the CoPs chosen to get involved. The reason is that most of the project personnel hold a first degree in an area other than project management.

The present paper tries to identify the dynamics of the unemployment for 27 countries from European Union and which countries have encountered the biggest cluster fluctuations (their behavior in this regard) during the COVID-19 pandemic. In order to obtain the results, a data mining analysis was made, using the specific CRISP-DM methodology. The data analysis is made using the EM and Simple K-Means cluster algorithms. For each analyzed period of time, a cluster analysis is made and each country is distributed in the most appropriate cluster. The main findings of the paper are indicating the dynamics of the unemployment based on the identified clusters and also the behavior of each country related to the movement from one cluster to another. The paper offers an original approach in which a cluster data mining analysis is made in order to identify correlation for pattern behavior in the data about unemployment. Knowing that several countries have a similar behavior when they are exposed to a certain situation, maybe strategies and regulation can be designed for all of them, reducing this way the resources consumption.