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L’analisi dell’evoluzione della soddisfazione nel contesto di business, è di solito basato su studi pseudo-panel, perchè sono meno costosi e facili da costruire con i dati disponibili. Come in casi cross-section, informazioni dettagliate sui clienti vengono raccolte in ogni istante temporale, ma il confronto dinamico avviene generalmente considerando soli pochi ritardi temporali (a causa del breve ciclo di vita di prodotti e servizi). Di conseguenza, nel nostro contributo proponiamo l’utilizzo dell’analisi fattoriale multipla duale. Tale tecnica permette la sintesi delle tabelle multivariate e la visualizzazione delle stesse su uno spazio comune che fa luce su traiettorie di soddisfazione dei clienti. I vantaggi della tecnica vengono illustrati in un caso di studio relativo ad una banca italiana The analysis of the evolution of satisfaction in business context is usually based on pseudo panels studies, because they are less costly and easy to build with the available data. As in the cross-section case, detailed information about customers are collected at each time point, but the dynamic comparison generally involves few temporal lags (due to short life time of products and services). Accordingly, in our paper we apply the Dual Multiple Factor Analysis. Such a technique allows the synthesis of the multivariate tables and their visualization on a common space that sheds light on customers’ trajectories of satisfaction. A real case study of an Italian bank is illustrated

In this paper we explore the relationship between the Industrial Production Index (IPI), the confidence index for the manufacturing sector and its sub-indexes and Google searches for several words linked to the economic situation, for the period January 2004 - September 2016 on Italian data. Significant correlations between the selected indicators point to probable comovements of same. Adding one observation at a time since the first forewarning signs of the 2008 crisis, we find that a few Google searches and the IPI cointegrate, particularly during the strong downward trend leading to January 2009, while no confidence indicators cointegrate with the IPI. These findings suggest that concern about economic conditions expressed through searches in google and the IPI or the confidence indexes are influenced by common circumstances. Recursive forecasts of the IPI through VECM models suggest that the evolution of the IPI can be well mimicked using the real time Gtrends selected variables.

SARS-CoV-2 infection may impair behavior and mental health; we explored the psychological impact on parents and children who experienced COVID-19 within their families. A cross-sectional web-based survey was conducted on families attending the COVID-19 Follow-up Clinic at the Department for Women’s and Children’s Health, Padua (Italy). From April 2020 to August 2021, 75 surveys were collected from 66 families (97 parents and 129 children); almost 70% of participants had COVID-19, mostly asymptomatic/mildly symptomatic, and the median time from infection to survey compilation was 164.7 days (SD 56). Most parents (>87%) reported positive relationships with family members either before, during, or after COVID-19. More than one-third of children and adolescents were unable to adapt to isolation. Among 31 pre-school children with a median age of 3 (SD 1.7), a change of one or more functions was reported for 74.2% of cases irrespective of COVID-19 status, particularly a change in circadian rhythm (25%), in relationship with parents (42.8%), and poor emotional control (36%). Among 74 children with a median age of 10.9 years (SD 2.7), 8.1% had a score indicating a disease; however, significant impairment in attention was reported for 16.7%, along with anxiety/depression and problems with conduct in 5.6% and 6.5% of cases, respectively.

This paper addresses the issue of measuring tolerance, viewed as a multifaceted phenomenon involving several different social domains. We develop a multidimensional index for Likert-scale data, characterized by the following features: (i) it reflects the individual’s intensity of tolerant attitudes towards each social domain; (ii) the index can be broken down by dimension in order to determine the contribution of each dimension to overall tolerance; (iii) the index combines the different dimensions of tolerance using a weighted scheme that reflects the importance of each dimension in determining the overall level of tolerance. To show how this new measure of tolerance works in practice, we carry out a case study using an Italian recent survey asking the opinion of university students about different subjects, such as interreligious dialog, women/religion relationship, religion/death relationship, homosexuality, and multicultural society.

[EN] Small and Medium Enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. That is why the prediction of their survival is considered one of the economic pillars UE keeps under observation. Default prediction models, accounting for SMEs idiosyncratic traits, are based on several types of data, mainly accounting indicators. Balance sheet data, indeed, are considered the standard predictors for classification models in this field, although they do not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing credit. In our work, we explore the possibility of complementing accounting information with data scraped from the firms’ websites. We modeled the data using a nonlinear discriminant analysis and we benchmarked the results with the Logistic Regression. The evidence of our study is promising although the combination of online and offline data shows better results in case of survival firms than for defaulted companies., This work was partially supported by grants PID2019-107765RB-I00 and funded by MCIN/AEI/10.13039/501100011033.

The growth of Internet and the information technology has generated big changes in subjects' communication, which, nowadays, occurs through social media or via thematic forums. This challenges the traditional notion of Customer Relationship Management (CRM) and pushes businesses to prompt and accurate understanding of sentiments expressed, in order to address their marketing actions. In this paper, the authors propose a combined application of a supervised Sentiment Analysis (SA) with a probabilistic kernel discriminant to provide a robust classifier of texts polarization. The partition obtained is also described by means of a statistical characterization of the texts. Such an approach is very promising, not only in terms of classification accuracy, but also in terms of knowledge extraction. A real case study is illustrated in order to test and show the effectiveness of the proposed strategy.

Big Data are hard to capture, store, search, share, analyze, and visualize. Without any doubts, Big Data represent the new frontier of data analysis, although their manipulation is far to be realized by standard computing machines. In this paper, we present a strategy to process and extract knowledge from Facebook data, in order to address marketing actions of a pharmaceutical company. The case study relies on a large Italians sample, interested in wellness and health care. The results of the study are very sturdy and can be easily replicated in different contexts.

The most important company asset seems to be Customer Satisfaction (CS), which banks, in the recent years, have frequently analyzed. For reaching such target, a dynamic Factor Analysis offers an effective way of merging information about clients and their preferences evolution. In our work we performed a dynamic Customer Satisfaction study, by means of a three-way factorial analysis, and we also introduced a new index of shift and shape (SSI), to synthesize information about every customer, cluster or typology. We considered a national bank case, with spread network, evaluating results provided by a questionnaire framed according to the SERVQUAL model. The information employed was obtained via a Customer Satisfaction survey repeated three times (waves). We performed the dynamic factorial model and we illustrated the usage of SSI as a new measure of trajectories’ dissimilarity. Finally, we showed our results which highlight promising performances of our index.

The growth of Internet and the information technology has generated big changes in subjects communication, that, nowadays, oc- curs through social media or via thematic forums. This produced a surge of information that is freely available: it offers the possibility to com- panies to evaluate their credibility and to monitor the ”mood” of their markets. The application of Sentiment Analysis (SA) has been proposed in order to extract, via objective rules, positive or negative opinions from (unstructured) texts. Communication literature, instead, highlights how such polarization derives from a subjective evaluations of the texts by the receivers. In business applications the receiver (i.e. marketing manager) is leaded by the values and the mission of the company. In our paper we propose a strategy to fit brand image and company values with a subjective SA, a probabilistic Kernel classifier has been employed to get discrimination rule and to rank classification results.

Today, the most important asset for a bank is its customer and therefore, the main targets to achieve by management are: knowledge of his needs, anticipation of his concerns and to distinguish itself in his eyes. The awareness that a satisfied customer is a highly profitable asset effort to provide a satisfactory service to the customer by diversifying its services. This paper aims to analyze the customer evaluation evolution of the main attributes of banking services to catch differences among the clusters and time lags through a dynamic factorial model. We propose a new system of weights by which assessing the dynamic factor reduction that is not optimal for all the instances considered across different waves. An empirical study will be illustrated: it is based on customer satisfaction data coming from a national bank with a spread network throughout Italy which wanted to analyze its reduced competitiveness in retail services, probably due to low customer satisfaction.

An empirical study, based on a sample of 27.000 retail customers, has been curried out: The management of a national bank with a spread network across Italian regions wanted to analyze the loss in competition of its retail services, probably due to a loss in customer satisfaction. The survey has the aim to analyze weaknesses of retail services, individuate possible recovery actions and evaluate their effectiveness across different waves (3 time lags). Such issues head our study towards a definition of a new path measure which exploits a dimension reduction obtained with Dynamic Principal Component Analysis (DPCA). Results which shown customer satisfaction configurations are discussed in the light of the possible marketing actions. © Springer International Publishing Switzerland 2013.

In this short paper we shall consider the Kernel Fisher Discriminant Analysis (KFDA) and extend the idea of Linear Discriminant, Analysis (LDA) to nonlinear feature space. We shall present a new method of choosing the optimal kernel function and its effect on the KDA classifier using information-theoretic complexity measure.

The Report presents the main facts about labour market in the Bologna County during the first half of 2007. As it is presented five months after the release of the 2006 the analysis about the economic and socio-demographic structure of the County are not presented. The labour market of the County seems to go fairly well. At the regional (Emilia-Romagna) level, in front of a business cycle moderately good, the number of employees is still growing (+1% in a year), but some signals of discouragement among women labour force begins to be present (female labour force decreases of 0,6% in a year. The new fact in the Report is the it gives a complete track of every labour contract started during the fist six months of 2007, comphrensive of the non standard labour contracts. Under this point of view we find a critical situation linked to the diffusion of the fixed term contracts, that are the 62% of all the contracts started. If we look at workers age we find that for every age class this percentage still is above the 55%. So we can say that workplace instability embraces every worker that is seeking a job, and that it is not limited to young people, but yet is pervasive and embraces workes of every ages. Further, using survival analysis we were able to measure the durations of jobs according the type of contract (using data from 2004 to 2004). The expect duration of a job is 180 days, but if we rescrtict to fixed term contracts the duration falls to 70 days. Moreover, the probability of staying in the condition of fixed term worker, after 3 years, is still 90%.

NF-Y is a conserved sequence-specific transcription factor binding to CCAAT boxes. The chromatin-associated HMGI proteins influence promoter activities through positive and negative effects on binding of transcription factors. It was previously shown that HMGI(Y) synergizes the binding of NF-Y to the alpha2-collagen CCAAT box [Currie, R.A. (1997) J. Biol Chem. 272, 30880-30888]. Using recombinant proteins, we confirm that at low concentrations of NF-Y, HMGI(Y) acts synergistically on the alpha2-collagen CCAAT and we extend this observation to HMGI and HMGI-C. However, enhancement of DNA binding to gamma-globin, alpha-globin and MHC class II Ea CCAAT boxes was not observed. At high concentrations, HMGI proteins inhibit binding to alpha2-collagen and to gamma-globin, but not to high affinity Ea or a-globin CCAAT. In none of our experiments did we see a ternary complex between NF-Y, HMGI(Y) and DNA. In protein competition experiments, NF-Y affinity was at least two orders of magnitude higher, even in the context of the suboptimal gamma-globin CCAAT. Our data prove that HMGI proteins have complex positive and negative effects on NF binding to some, but not to all CCAAT boxes, suggesting that this phenomenon is dictated by the sequences flanking the pentanucleotide rather than direct protein-protein interactions.