Universities all around the world operate by following several institutional missions, with a central purpose on teaching and research activities. The importance of each aspect alongside the connection between them provide a disputed topic in the literature, many authors confirming or rejecting the intuitive inverse relationship by using various means, more or less quantitative. This paper aims to examine the teaching and research dimensions of the research-active European universities from a data mining perspective. For each dimension previously considered we employ the K-means Clustering in order to identify the groups of similar higher education institutions and we analyze the insights produced by the results. In addition, we build some target variables considering the teaching and research outputs and we investigate their drivers by employing the Logistic Regression. Furthermore, we explore the controverted relationship between the two institutional missions we considered through the use of Correspondence Analysis. Preliminary results illustrate that the dataset contains two types of universities: a category of very large and prestigious institutions and a second group of small and medium sized institutions, quite different from each other. Interest insights are given by the third part of the study, in which the Correspondence Analysis confirms an inverse relationship between teaching and research activities. Unfortunately, this is very likely a consequence of the time constraint – both activities require the same limited resources and therefore increasing the teaching burden for academics may diminish the time and energy dedicated to research.
Tobacco consumption is a problem of both health and economic interest nowadays. According to recent studies conducted by the European Commission approximate 700,000 deaths per year are caused by smoking. For this reason, the European Commission frequently conducts a survey in order to monitor the attitude towards tobacco addiction. Smoking addiction changes due to different factors such as budget, time or entourage. The evolution in time of these factors and the consumers’ preferences is studied using behavioral economics based on a small group of respondents. Through a survey, over 500 persons were asked to choose their preference for cigarettes characteristics. We employ correspondence analysis using combinations of age, type of cigarette, number of cigarettes smoked per day and nicotine concertation to see the type of responses the consumers’ have according to their habit. Moreover, we made a 5 persons selection from the initial group and we observed their behavior for 9 months period of time. The consumers were asked to classify a set of packages according to their preferences and we applied conjoint analysis in order to determine how or if the initial preferences change. Furthermore, we explain the changes in behavior by taking into account the nowadays global impetus towards a healthier lifestyle. The results provided allow to emphasize the role of a strong analysis for each single target consumer’s behavior as this is one of the main roles of Behavioral Economics.