School factors helping disadvantaged students to succeed: empirical evidence from four Italian cities

This research investigates a particular category of disadvantaged students, namely those who are able to overcome a situation of socio-economical disadvantage and obtain good academic results (here named ‘resilient students’). We have used micro-data provided by the Italian National Evaluation Committee for Education and focused on class and school-level characteristics that help disadvantaged students to become resilient students when they move from primary (grade 5) to lower secondary school (grade 6), concentrating our analysis on four major cities. We employed a probit regression and a propensity score matching model, finding that class and school factors do matter. In particular, we looked at whether the performance of their peers has a positive impact on that of disadvantaged students, estimating the increased probability of these students becoming resilient.     

Symbolic computation for approximating distributions of some families of one and two-sample nonparametric test statistics

A conditional saddlepoint approximation was provided by Gatto and Jammalamadaka (1999) for computing the distribution function of many test statistics based on dependent quantities like multinomial frequencies, spacing frequencies, etc. The considerable complexity of the formulas involved can be bypassed by symbolic computation. This article illustrates the effectiveness of symbolic computation to evaluate the saddlepoint approximation for the likelihood ratio, the exponential score, and the Wald-Wolfowitz test statistics. The case of composite hypotheses is also discussed.     

Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

Bertrand competition with asymmetric costs: a solution in pure strategies

Theory and Decision - We consider a Bertrand duopoly with homogeneous goods and we allow for asymmetric marginal costs. We derive the Myopic Stable Set in pure strategies as introduced by Demuynck...     

Satisfaction and Tourism Expenditure Behaviour

In the literature, the quantification of the effect of satisfaction on tourists’ expenditure behaviour has not been extensively studied. This research aims to fill in this gap, providing additional information about this crucial relation by analysing it from a microdata perspective. In particular, the Fuzzy Double-Hurdle model, a new model which combines the well-known Double-Hurdle model and the fuzzy set theory, is suggested and presented, both technically and by means of a real case study. The proposed model gathers the advantages of the Double-Hurdle model and the fuzzy set theory together producing a suitable model for the analysis of censored observations in presence of imprecise data. Specifically, the Double-Hurdle model allows to efficiently estimate the average values of a non-negative, non-normally distributed variable characterised by high frequency of zero values, as tourists’ expenditure can be, considering the two-stages nature of the decision process. On the other end, the inclusion of the fuzzy set theory in the regression model allows to cope with the imprecision of both collected information (i.e. levels of satisfaction) and kind of measurement used (i.e. Liker-type scale). The results will help tourism managers to more accurately evaluate the efficacy of their policies and marketing strategies in enhancing tourists’ satisfaction and, consequently, in increasing the level of spending at the destination.

     

Correction to: Do women directors contribute to R&D? The role of critical mass and expert power

Journal of Management and Governance - An amendment to this paper has been published and can be accessed via the original article.     

The Resource-Infrastructure-Environment Index for Measuring Progress: An Application to Australia, Mexico and the US

The resource-infrastructure-environment (RIE) index was proposed as an alternative measure of progress which was then employed to: (1) compare the aggregate (single summary) index findings between Australia (mid-industrialised nation), Mexico (emerging economy), and the US (highly industrialised nation); and (2) compare the RIE index against the gross domestic product (GDP), human development index (HDI) and genuine savings (GS) measure. This paper builds on the previous work by assessing the seven themes and 21 dimensions which comprise the RIE index for the three aforementioned nations, as well as the associated policy implications. The results identified Australia’s strength in the human resource and infrastructure themes. For Mexico, strong contributions came from the natural and generated resource themes as well as the physical environment theme, while the US performed strongly in the infrastructure themes. The comparative results of the US and Mexico illustrated that it is possible to achieve high levels of progress without an excessive reliance on high levels of production and income.     

A neural network clustering model for miscellaneous components production planning

The growing importance of providing service to customers, e.g. post-sale assistance, supplying of spare parts, upgrading and integration of new elements in installed systems, enhances the importance of planning and management of upgrading parts in most manufacturing industries. These parts are generally characterized by high technical heterogeneity and have a highly variable and difficult to forecast demand. In some areas (especially the most dynamic, e.g. high-tech products), these kinds of components are quite common, and represent a very strong relation between the manufacturing firm and the market. These parts are generally too many to be efectively supported on a planning database system with individual records and too heterogeneous ( and sometimes with a too high value) to be supported all together in a single record. In this paper, we want to study the application of adaptive techniques for the clustering of these components in classes based on the similarities in their market behaviour in order to build an optimal database for planning production and supplying of these components.