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This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

The purpose of this paper is to apply the conjugate gradient (CG) method, together with the adjoint operator (AO) to the pulsating heat pipe problem, including some quite interesting experimental results. The CG method, together with the AO, was able to estimate the unknown functions more efficiently than the other techniques presented in this paper. The estimation of local heat transfer coefficients, rather than the global ones, in pulsating heat pipes is a relatively new subject and presenting a robust, efficient and self-regularized inverse tool to estimate it, supported also by some experimental results, is the main purpose of this paper. To also increase the visibility and the general use of the paper to the heat transfer community, the authors include, as supplemental material, all numerical and experimental data used in this paper.

The approach was established on the solution of the inverse heat conduction problem in the wall by using as starting data the temperature measurements on the outer surface. The procedure is based on the CG method with AO. The here proposed approach was first verified adopting synthetic data and then it was validated with real cases regarding pulsating heat pipes.

An original fast methodology to estimate local convective heat flux is proposed. The procedure has been validated both numerically and experimentally. The procedure has been compared to other classical methods presenting some peculiar benefits.

The approach is suitable for pulsating heat pipes performance evaluation because these devices present a local heat flux distribution characterized by an important variation both in time and in space as a result of the complex flow patterns that are generated in this type of devices.

The procedure here proposed shows these benefits: it affords a general model of the heat conduction problem that is effortlessly customized for the particular case, it can be applied also to large datasets and it presents reduced computational expense.

The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

The objective of this study is to assess if Italian fish consumers are sensible to shark protection and if they would contribute paying more for small pelagic fishes coming from fisheries that are certified as “shark-free”.

Contingent valuation is used to estimate willingness to pay with a double approach, including a dichotomous choice and an open-ended question. Inconsistency between the two answers is allowed. This allows the correction of two sources of bias (i.e. preference uncertainty and anchoring effect) and has permitted that the two estimation methods converged to the same result.

Consumers show interest for the “shark-free” label. Premium price is estimated at +26%. Variables affecting willingness to pay (WTP) in the sample are age, income, environmental attitude, knowledge of organic labels and frequency of small pelagics' consumption. Results need to be confirmed by a replication on a larger (probabilistic) sample and with a different distribution of bids.

Ecosystems provide different benefits to humankind, including non-use services, such as the satisfaction to know that a species is well conserved. Generally, appreciation is higher for what are considered charismatic species. In this paper, the authors investigate if sharks can be considered charismatic species despite their “bad reputation”. The interest in shark survival is measured indirectly using a “shark-free” label on a commercial species like anchovy, allowing to increase the value added of this low-price species.

The aim of the study is to explore the genesis of entrepreneurial ecosystems (EE) and highlight the role played by intellectual capital (IC) in that process. Specifically, the paper adopts the collective intelligence approach, and the study shows how human capital (HC), structural capital (SC) and relational capital (RC) interact to create an entrepreneurial ecosystem.

The paper adopts a single case study of an Italian EE. The data analysis is based upon the collection of different sources of data: semi-structured interviews with representatives of each actor of the ecosystem; email correspondence; meetings report; a 24-months period of direct observation. Given the novelty of the topic, the qualitative method seems well suited for studying innovation-based EE since the method offers rich data about a phenomenon in real-life context.

The case is a top-down, innovation-based EE in which all main components of the IC play a crucial role from the initial stage. Findings show how the constant interchange between IC components occurs at two different levels: the micro and the meso level. HC and RC play major roles at both levels, whilst SC only occurs at a meso level, representing the environment in which the whole ecosystem takes place. Additionally, the use case, a new intangible asset integrating all three components of IC, emerged as one of the main outcomes of this innovation-based EE.

The paper contributes to a rather unexplored topic in the existing literature on EE and IC, namely the formation process of EE and the role played by IC within that process. Additionally, through the application of the collective intelligence approach, the authors shed light on the need to manage IC at both micro and meso level in the creation of an EE.

“Social eating initiatives” are framed as a specific type of community-based food service that provides opportunities for people to eat together in local spaces using surplus food. These initiatives provide a meal that is fresh, affordable and more environmentally friendly than fast or convenience foods. In this research, we build upon the food well-being model to explore how food consumption is experienced in these community settings and the role of social eating projects in shaping the different dimensions of people's foodscapes. We adopted a community-based participatory approach and engaged in a series of dialogues with staff volunteers and coordinators at four “social eating initiatives”. We also conducted 45 interviews with service users and volunteers at three sites in the Midlands region.

The role of community-based food initiatives responding to hunger by utilising surplus food to feed local populations is often conceptualised critically. However, closer attention to the experiences of staff, volunteers and customers at these spaces, reveals them as sites where knowledge and experience of food is being developed with this contributing to a sense of well-being beyond nutrition. Shared food practices and eating together contribute to social capital and are important dimensions of food well-being that are significantly restricted by food insecurity. The “food well-being” model envisages a shift in focus from health, defined as the absence of illness, towards well-being as a positive relationship with food at the individual and societal level. In the concluding remarks of this article, it is suggested that this holistic conception is required to understand the role and function of social eating initiatives.

This paper aims to present the integrated teaching activity carried out in the Studios of the Master of Science “Architecture-Building Architecture” held at the School of Architecture Urban Planning Construction Engineering of the “Politecnico di Milano,” Milan, Italy. The integrated teaching activity related to the structural disciplines was in Sgambi et al., 2019; here the structure of the MSc training and its disciplinary synergies will be presented. Indeed, this type of activity characterizes all the Studios of this Master of Science and involves all the disciplines that contribute to the development of an architectural and cultural heritage preservation project.

In the Studios of the aforementioned Master of Science, teachers of different subjects are involved, working together to guide the student in the development of an architectural project sustainable in all aspects. The fundamentals of each discipline are taught using the best suited teaching methodology and the application phase of each discipline is carried out directly on the students' projects in the form of “learning by making.” The students are thus stimulated to deepen their basic knowledge of each single discipline, making their design choices sustainable.

This experience, born in 2003 and still active, has also achieved good results in employment. Students train using the “learning by making” method to acquire proficiencies in various disciplines of design, giving them the ability to communicate competently with experts belonging to different construction sectors.

The approach illustrated in this paper does not represent the didactic experimentation of a single discipline, but it is typical of the study program of an entire Master of Science. Although this approach is entirely built on a “learning by making” and “active learning” philosophy, it maintains the teaching of the theoretical contents of disciplines at a significantly high level when compared with the contents of a frontal-taught theoretical course. The development of this structure required a strong commitment on the part of the teaching staff in their search for effective teaching methods in each individual discipline and aimed at the architectural project. The results obtained give an added value to the training of future architects.

The purpose of this paper is to investigate the food and beverage automatic retail environment by analysing the impact of planograms, conceived as a visual merchandising practice and shopping time – the time spent making a purchase – as part of food consumer purchasing behaviour to further enrich the debate on the ability of companies to absorb customer knowledge.

A real-world experiment was conducted using a sample of 27,230 valid observations of consumer purchasing decision-making processes at automatic vending machines (AVMs). Data were collected by a shopper behaviour analytics system that allows for a better understanding of the AVM users' behaviour. Two sets of regressions were run to test the two hypotheses.

The experimental results demonstrated that planograms – the planned, systematic organisation of products in an AVM – positively impact food purchases. A planogram acts as a mediator in the relationship between shopping time and purchase, resulting in shorter shopping times and more purchases.

This work adds to the customer knowledge literature by focussing on customer behaviour in the food and beverage automated shopping environment. The shopper analytics technology adopted to collect real-time data leads to a better understanding of the purchasing behaviour of AVMs' users and provides new marketing and retail insights into AVMs' performance that retailers can use to improve their marketing strategies.

Digital voice-of-customer (digital VoC) analysis is gaining much attention in the field of quality management. Digital VoC can be a great source of knowledge about customer needs, habits and expectations. To this end, the most popular approach is based on the application of text mining algorithms named topic modelling. These algorithms can identify latent topics discussed within digital VoC and categorise each source (e.g. each review) based on its content. This paper aims to propose a structured procedure for validating the results produced by topic modelling algorithms.

The proposed procedure compares, on random samples, the results produced by topic modelling algorithms with those generated by human evaluators. The use of specific metrics allows to make a comparison between the two approaches and to provide a preliminary empirical validation.

The proposed procedure can address users of topic modelling algorithms in validating the obtained results. An application case study related to some car-sharing services supports the description.

Despite the vast success of topic modelling-based approaches, metrics and procedures to validate the obtained results are still lacking. This paper provides a first practical and structured validation procedure specifically employed for quality-related applications.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.