Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Scene di violenza, esperienze emotive e condotte aggressive

The aim of this research is to estimate the impact of violent film excerpts on university students (30 f, 30 m) in two different sequences, a “justified” violent scene followed by an “unjustified” one, or vice versa, as follows: 1) before-after sequences, using Aggressive behaviour I-R Questionnaire, Self Depression Scale and ASQ-IPAT Anxiety SCALE; 2) after every excerpt, using a self-report to evaluate the intensity and hedonic tone of emotions and the violence justification level. Emotion regulation processes (suppression, reappraisal, self-efficacy) were considered. In contrast with the “unjustified” violent scene, during the “justified” one, the justification level was higher; intensity and unpleasantness of negative emotions were lower. Anxiety (total and latent) and rumination diminished after both types of sequences. Rumination decreases less after the JV-UV sequence than after the UV-JV sequence. Self-efficacy in controlling negative emotions reduced rumination, whereas suppression reduced irritability. Reappraisal, self-efficacy in positive emotion expression and perceived emphatic selfefficacy did not have any effects.

A Multilevel Model with Time Series Components for the Analysis of Tribal Art Prices

In the present work we perform an econometric analysis of the Tribal art market. To this aim, we use a unique and original database that includes information on Tribal art market auctions worldwide from 1998 to 2011. In Literature, art prices are modelled through the hedonic regression model, a classic fixed-effect model. The main drawback of the hedonic approach is the large number of parameters, since, in general, art data include many categorical variables. In this work, we propose a multilevel model for the analysis of Tribal art prices that takes into account the influence of time on artwork prices. In fact, it is natural to assume that time exerts an influence over the price dynamics in various ways. Nevertheless, since the set of objects change at every auction date, we do not have repeated measurements of the same items over time. Hence, the dataset does not constitute a proper panel; rather, it has a two-level structure in that items, level-1 units, are grouped in time points, level-2 units. The main theoretical contribution is the extension of classical multilevel models to cope with the case described above. In particular, we introduce a model with time dependent random effects at the second level. We propose a novel specification of the model, derive the maximum likelihood estimators and implement them through the E-M algorithm. We test the finite sample properties of the estimators and the validity of the own-written R-code by means of a simulation study. Finally, we show that the new model improves considerably the fit of the Tribal art data with respect to both the hedonic regression model and the classic multilevel model.