Knowledge extraction from a large on-line survey: a case study for a higher education corporate marketing

For a higher education public institution, young in relative terms, featuring local competition with another private and both long-established and reputed one, it is of great importance to become a reference university institution to be better known and felt with identification in the society it belongs to and ultimately to reach a good position within the European Higher Education Area. These considerations have made the university governors setting up the objective of achieving an adequate management of the university institutional brand focused on its logo and on image promotion, leading to the establishment of a university shop as it is considered a highly adequate instrument for such promotion. In this context, an on-line survey is launched on three different kinds of members of the institution, resulting in a large data sample. Different kinds of variables are analysed through appropriate exploratory multivariate techniques (symmetrical methods) and regression-related techniques (non-symmetrical methods). An advocacy for such combination is given as a conclusion. The application of statistical techniques of data and text mining provides us with empirical insights about the institution members’ perceptions and helps us to extract some facts valuable to establish policies that would improve the corporate identity and the success of the corporate shop.     

Proposal for a Dynamic Composite Indicator: Application in a Comparative Analysis of Trends in the EU Member States Towards the Europe 2020 Strategy

This paper contributes, with a dynamic approach, to the research on the creation of comparable composite indicators by presenting a proposal for an exploratory factor analysis protocol to enable a comparative trend analysis. The originality of the study lies in the three dimensions of information for analysis: observations, variables and units of time. The proposal involves various stages of analysis with the ultimate, albeit not exclusive, aim of obtaining what is known as a Global Dynamic Indicator. The analysis process begins by structuring the data into a three-dimensional global matrix, thereby conditioning, while also, and primarily, enriching the later stages. A combination of multiple factor analysis and a clustering technique is the selected approach for successfully meeting the challenges involved. The appropriateness and versatility of the proposal are validated through the analysis of the trends of the EU member states towards the targets set by the 2020 Strategy. The study period runs from 2009 to 2018. The empirical work enables the visualisation and quantification of trend differences and similarities across member states collectively and individually, and across all the variables and years selected for analysis. The relevant findings will be quantified by means of a synthetic indicator for each unit of time and a global indicator for the period as a whole. Some of the conclusions reached by this paper are consistent with those already published by various authors.