Disentangling the Innovation - Internalization Process Through a Structural Equation Model

Innovation virtuously impacts on the degree of international growth, which in turn positively influences innovation activities and then firms�™ performance (Filipescu et al., 2009). Many authors have tried to identify and explain the relationship between these two phenomena at firm level. Only recently, few empirical studies investigate them at a more aggregate level (see e.g. Mariotti et al., 2008). Moreover the literature focuses only on one direction of causality, while scant attention has been paid to inspect empirically innovation and internationalization together (Kafouros et al., 2008; Filippetti et al., 2009; Frenz and Ietto-Gillies, 2007). This paper provides an empirical analysis of the mutual relationship of these two phenomena, taking into account various features of the regions themselves. The empirical study is conducted on data concerning 20 Italian regions covering the period 2000-2008. To better understand the complex relationship between internationalization and innovation, we refer to the Structural Equation Models (SEM). These are multivariate regression type models, in which response variables could in turn act as dependent and predictor within a system of equations, and all variables are assumed to influence one-another reciprocally, either directly or through other variables as intermediaries (Bollen, 1989; McAdam et al., 2010). Through the SEM the relationships are expressed by a set of parameters which explain the magnitude of the effect (direct or indirect) between independent (either observed or latent) and dependent variables. Indeed, internationalization and innovation could act as both dependent and predictor which measurement could be difficult then suggesting the use of latent variables, and where the system of indicators is complex enough to lead at a model specified through two-way relations intrinsically connected. Using SEM approach we are able to specify flexible models dealing with non-standard relations stylized along panel data structure, in which spatial and temporal dimensions do matter

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.

Chapter Prediction of wine sensorial quality: a classification problem

When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, context) terms (Jackson, 2017). In this paper, the wine quality was evaluated by experienced judges who scored the wine on the base of a 0-10 scale, with 0 meaning very bad and 10 excellent, so, the resulting variable was categorical. The models applied to predict this variable provide the prediction of the occurrence probabilities of each of its categories. Nevertheless, jointly with this probabilities’ record, the practitioners need the predicted value (category) of the variable, so the statistical problem to be covered refers to the way in which this probabilities’ record is transformed into a single value. In this paper we compare the predictive performances of the default method (Bayes Classifier - BC), which assigns a unit to the most likely category, and other two methods (Maximum Difference Classifier and Maximum Ratio Classifier). The BC is the optimal criterion if one is interested in the accuracy of the classification, but, given that it favors the prevalent category most, when there is not a category of interest, it cannot be the best choice. The data under study concern the quality of the red variant of the Portuguese "Vinho Verde" wine (Cortez et al., 2009), measured on a 0-10 scale. Nevertheless, only 6 scores were used, with 2 scores with a very few number of observations, so this is the right context for predictive performance comparisons. In the study, we investigated different merging of categories and we used 11 explanatory variables to estimate the probabilities’ record of the wine quality variable

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

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

On the Imputation of Missing Data in Surveys with Likert-Type Scales

EnThe aim of this paper is two-fold: to propose the imputation procedure named ABBN for replacing missing data in likert-type scales and to compare its performance with some well-known imputation methods. ABBN is a hot-deck imputation procedure which modifies the Approximate Bayesian Bootstrap method by sampling the donor in the neighbourhood of the nonrespondent. The comparison among the imputation procedures is based on a simulation study with data on job satisfaction and procedural fairness scales coming from the recent survey of workers employed in the Italian social cooperatives (ICSI2007). The effects of the imputation procedures on the respondents’ score and on the quality of the scales are investigated

A strategy for the matching of mobile phone signals with census data

Administrative data allows us to count for the number of residents. The geo-localization of people by mobile phone, by quantifying the number of people at a given moment in time, enriches the amount of useful information for "smart" (cities) evaluations. However, using Telecom Italia Mobile (TIM) data, we are able to characterize the spatio-temporal dynamic of the presences in the city of just TIM users. A strategy to estimate total presences is needed. In this paper we propose a strategy to extrapolate the number of total people by using TIM data only. To do so, we apply a spatial record linkage of mobile phone data with administrative archives using the number of residents at the level of sezione di censimento.Comment: 8 pages,5 figures; Conference: SIS 2019 - Smart Statistics for Smart Applications - Book of short papers, editors: Giuseppe Arbia, Stefano Peluso, Alessia Pini, Giulia Rivellini. ISBN 978889191510

Constructing indicators of unobservable variables from parallel measurements

The social and economic research often focuses on the construction of composite indicators for unobservable (or latent) variables using data from a questionnaire with Likert-type scales. Within the variety of procedures, we focus on the data analysis technique of Principal Components Analysis, in its Linear and NonLinear versions. This paper shows that when the variables are parallel measurements of the same latent unobservable variable, Linear and NonLinear Principal Components Analyses practically lead to the same composite indicators

Chapter Modelling the spatio-temporal dynamic of traffic flows with gravity models and mobile phone data

The analysis of origin-destination traffic flows may be useful in many contexts of application (e.g., urban planning, tourism economics) and have been commonly studied through the gravity model, which states that flows are proportional to ''masses" of both origin and destination, and inversely proportional to distance between them. Using data on the flow of mobile phone SIM among different aree di censimento, recorded hourly basis for several months and provided by FasterNet in the context of MoSoRe project, in this work we characterize and model the dynamic of such flows over the time in the strongly urbanized and flood-prone area of the Mandolossa (western outskirts of Brescia, northern Italy), with the aim of predicting the traffic flow during flood episodes. Whereas a traditional ”static” mass explanatory variable is represented by residential population (Pop), or by gross domestic product (GDP), here we propose to use a most accurate set of explanatory variables in order to better account for the dynamic over the time. First, we employ a time-varying mass variable represented by the number of city-users by area and by time period, which has been estimated from mobile phone data (provided by TIM) using functional data approach and already adopted to derive crowding maps for flood exposure. Secondly, we include in the model a proper set of factors such as areal and time dummies, and a novel set of indices related to (e.g.) the number and the type of streets, the number of offices, restaurants or cinemas, which may be retrieved from OpenStreetMap. The joint use of these two novel sets of explanatory variables should allow us to obtain a better linear fitting of the gravity model and a better traffic flow prediction for the flood risk evaluation

Twitter user geolocation using web country noun searches

Several Web and social media analytics require user geolocation data. Although Twitter is a powerful source for social media analytics, its user geolocation is a nontrivial task. This paper presents a purely word distribution method for Twitter user country geolocation. In particular, we focus on the frequencies of tweet nouns and their statistical matches with Google Trends world country distributions (GTN method). Several experiments were conducted, using a recently created dataset of 744,830 tweets produced by 3298 users from 54 countries and written in 48 languages. Overall, the proposed GTN approach is competitive when compared with a state-of-the-art world distribution geolocation method. To reduce the number of Google Trends queries, we also tested a machine learning variant (GTN2) that is capable of matching the GTN responses with an 80% accuracy while being much faster than GTN.Research carried out with the support of resources of Big and Open Data Innovation Laboratory (BODaI-Lab), University of Brescia, granted by Fondazione Cariplo and Regione Lombardia. The work of P. Cortez was supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2019. We would also like to thank the anonymous reviewers for their helpful suggestions