Maurizio Carpita

Incentives, Job Satisfaction and Performance: Empirical Evidence in Italian Social Enterprises

The paper offers a contribution to the understanding of the relations between incentives, satisfaction and performance of employees in social enterprises. It starts by criticizing the general hypotheses of the principal-agent theory and especially that employee satisfaction is determined exclusively by the level of salary received. These criticisms are explained both by looking to the organizational definition of job satisfaction by Locke and by taking a behavioural economics perspective. Job satisfaction is thus assumed to derive from a composed mix of incentives received on the job, equity perceived and employee motivations. It is no longer possible to assume that the wage is the sole (not even the most important) variable influencing worker performance. This claim is especially valid in social enterprises, where worker performance is difficult to monitor and evaluate, while high intrinsic motivations can better explain job satisfaction. The empirical analysis helps to shed light on the determinants of job satisfaction and individual performance. Data was collected on 4,134 employees working in 320 Italian social cooperatives. The paper introduces the methodologies of categorical principal components analysis, factor analysis, and Rasch models to group the items of intrinsic and extrinsic satisfaction, motivations and fairness. The data was then analysed by means of linear regression where the dependent variables are not only the stated degree of job satisfaction, but also satisfaction with extrinsic and intrinsic aspects of the job. The models come to demonstrate the particular relevance of employee motivations and fairness perceived in explaining job satisfaction and its sub-dimensions. Furthermore, organizational perceptions and the work environment are found to be significant as are individual perceptions and motivations.

Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches

The objective of this paper is to describe and analyse with appropriate statistical models the links between work quality latent factors. Due to the complexity of the task, the analysis is carried out through a two-step approach: In the first step, we construct some multidimensional measures of the subjective quality of work, using nonlinear principal component analysis (NPCA) and Rasch analysis with the Rating Scale Model (NPCA-RSM); In the second step, we adopt a Structural Equation Model based on generalized maximum entropy (SEM-GME) to integrate the measures achieved with the previous step and to evaluate the relationships between the subjective work quality latent factors. Therefore, the novel aspects of this paper are the following: (i) The integration between the NPCA-RSM and SEM-GME, which allows reduction of the variables analysed and evaluation of the measurement errors; (ii) The formalization of a Job Satisfaction Model for the study of the relationships between the subjective work quality latent factors in the Italian social services sector.

The Italian Social Cooperatives in 2008: A Portrait Using Descriptive and Principal Component Analysis

This paper describes the role of social cooperatives in Italy as a type of economic, nonprofit organization that is assuming an increasingly central role in the country, by contributing to its economic and social growth. In the last decade many agencies, institutions and research centres (Istat � Italian National Statistic Office, Ministry of Economic Development, Confcooperative Legacoop, Unioncamere) have provided studies on the evolution of the cooperative movement in the Third Sector, in order to monitor the development of these organizations over time and to evaluate their economic and employment impact over the country. Following a similar path, this study analyzes the contribution of social cooperatives in Italy at a regional level, highlighting the differences related to their age and fields of activity. Moreover, the paper evaluates the efficiency and profitability of the social cooperative by conducting further analysis based on a number of economic and financial indexes.

Discovering the Drivers of Football Match Outcomes with Data Mining

Abstract In this paper the relationship between the outcome of a football match (win, lose or draw) and a set of variables describing the game actions is investigated across time, by analyzing data from 4 consecutive yearly championships. The aim of the study is to discover the factors leading to win the match. More precisely, the goal is to select, from hundreds of covariates, those that most strongly affect the probability of winning a match, to recognize regularities across time by identifying the variables whose importance is confirmed in different analyses, and finally to construct a small number of composite indicators to be interpreted as drivers of match outcome. These tasks are carried out using the Random Forest machine learning algorithm, in order to select the most important variables, and Principal Component Analysis, in order to summarize them into a small number of drivers. Variable selection is performed using the novel approach developed by Sandri and Zuccolotto [33–34].

A generalized maximum entropy estimator to simple linear measurement error model with a composite indicator

We extend the simple linear measurement error model through the inclusion of a composite indicator by using the generalized maximum entropy estimator. A Monte Carlo simulation study is proposed for comparing the performances of the proposed estimator to his counterpart the ordinary least squares “Adjusted for attenuation”. The two estimators are compared in term of correlation with the true latent variable, standard error and root mean of squared error. Two illustrative case studies are reported in order to discuss the results obtained on the real data set, and relate them to the conclusions drawn via simulation study.

MEM and SEM in the GME Framework: Statistical Modelling of Perception and Satisfaction

Abstract This paper presents a review of the original method recently developed by the authors with the Generalized Maximum Entropy (GME) estimator for the simple linear Measurement Error Model (MEM) and the Structural Equation Model (SEM). In socio-economic research, these two models often concern subjective or psychological variables (composite indicators), and represent relations between latent variables. In this review, two applications to the statistical modelling of economic perception and job satisfaction are presented.

Football Mining with R

This chapter presents a data mining process for investigating the relationship between the outcome of a football match (win, lose, or draw) and a set of variables describing the actions of each team, using the R environment and selected R packages for statistical computing. The analyses were implemented with parallel computing when possible. Our goals were to identify, from hundreds of covariates, those that most strongly affect the probability of winning a match and to construct a small number of composite indicators based on the most predictive variables. These two tasks were carried out using the Random forest (RF) machine learning algorithm and principal component analysis, respectively. Variable selection was performed using the novel approach developed by Sandri and Zuccolotto in 2008. Finally, we compared the results of several different classification models and algorithms (RF, Classification Neural Network, k -Nearest Neighbor (KNN), Naive Bayes classifier, and Multinomial Logit regression), assessing both their performance and the insightfulness of their results.

Design, Implementation and Validation of a Questionnaire for University Teaching Evaluation

This paper summarizes the results of a research project supported by CNVSU in 2010. The aim of the project was, firstly, to review the questionnaire used in Italy for university teaching evaluation and, secondly, to propose a guideline for implementing this survey on the web.

On the Nonlinearity of Homogeneous Ordinal Variables

The paper aims at evaluating the nonlinearity existing in homogeneous ordinal data with a one-dimensional latent variable, using Linear and NonLinear Principal Components Analysis. The results of a simulation study with Probabilistic and Monte Carlo gauges show that, when variables are linearly related, a source of nonlinearity can affect each single variable, but the nonlinearity of the global solution is negligible and, therefore, can be left out to construct a measure of the latent trait underlying homogeneity data.

Statistica per l'impresa sociale di qualità

Statistics for the social enterprise of quality (by Maurizio Carpita, Elena Poli). Objectives The present study firstly proposes to extend the standard total quality approach, to take into account the peculiarities of the social enterprises, as the non profit organiza- tions offering social utility services are called in Italy since 2006 (DL n. 155/2006). Furthermore, the study aims at evaluating the characteristics of the quality systems in the certified social enterprises, in order to obtain a picture of the actual quality systems developed in the social service sector. Methods and Results The theoretical model proposed is called Social Relationship Value Management (SRVM) and is developed starting from the analogous model proposed in the for profit literature (Payne et al., 2001). The theoretical investigation, also concerning the statistical instruments to evaluate quality, is completed by a online survey involving the quality responsible of the certified social enterprises belonging to the CGM network. In this context, the SRVM approach can be developed by the linkage research techniques, using appropriate statistical indicators and models. The results of the survey clearly show that (i) the quality is considered as an internal instrument of organization and management rather than as an instrument useful to strengthen the relationships with the stakeholders and (ii) the weakness of the quality system is mainly due to the lack of recognition and interest from the external subjects. Conclusions The SRVM model can be adopted to develop effective quality systems in the social enterprises and can be supported by using appropriate statistical techniques. In our opinion, the proposed model can help to face and partly solve the problems pointed out by the survey.