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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.

This paper aims to serve as a commentary on the paper titled “Implementation of an Adapted Handwriting Without Tears® Programme for Children with Developmental Disabilities in a Special Education Setting.”

It provides an overview of educational strategies that can be used in school settings to support professionals in acquiring skills relevant to their practice and promoting the acquisition of new abilities for their students.

It explores three critical aspects of education: measuring academic progress, using evidence-based methods and enhancing educator training.

This work represents the personal perspective of a professional working in the service delivery field for children with developmental disabilities in schools and rehabilitative centres.

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.

This study aims to ascertain the intentions of risk managers to use artificial intelligence in performing their tasks by examining the factors affecting their motivation.

The study employs an integrated theoretical framework that merges the third version of the technology acceptance model 3 (TAM3) and the unified theory of acceptance and use of technology (UTAUT) based on the application of the structural equation model with partial least squares structural equation modeling (PLS-SEM) estimation on data gathered through a Likert-based questionnaire disseminated among Italian risk managers. The survey reached 782 people working as risk professionals, but only 208 provided full responses. The final response rate was 26.59%.

The findings show that social influence, perception of external control and risk perception are the main predictors of risk professionals' intention to use artificial intelligence. Moreover, performance expectancy (PE) and effort expectancy (EE) of risk professionals in relation to technology implementation and use also appear to be reasonably reliable predictors.

Thus, the study offers a precious contribution to the debate on the impact of automation and disruptive technologies in the risk management domain. It complements extant studies by tapping into cultural issues surrounding risk management and focuses on the mostly overlooked dimension of individuals.

Yet, thanks to its quite novel theoretical approach; it also extends the field of studies on artificial intelligence acceptance by offering fresh insights into the perceptions of risk professionals and valuable practical and policymaking implications.

The purpose of the paper is to analyse the performances of closures and compressibility corrections classically used in turbulence models when applied to highly-compressible turbulent boundary layers (TBLs) over flat plates.

A direct numerical simulation (DNS) database of TBLs, covering a wide range of thermodynamic conditions, is presented and exploited to perform a priori analyses of classical and recent closures for turbulent models. The results are systematically compared to the “exact” terms computed from DNS.

The few compressibility corrections available in the literature are not found to capture DNS data much better than the uncorrected original models, especially at the highest Mach numbers. Turbulent mass and heat fluxes are shown not to follow the classical gradient diffusion model, which was shown instead to provide acceptable results for modelling the vibrational turbulent heat flux.

The main originality of the present paper resides in the DNS database on which the a priori tests are conducted. The database contains some high-enthalpy simulations at large Mach numbers, allowing to test the performances of the turbulence models in the presence of both chemical dissociation and vibrational relaxation processes.

This paper aims to examine how proportional appropriation systems affect the quality of financial reporting in entities controlled by local governments.

The authors examine this issue using the setting of Italian municipally owned entities (MOEs) following the implementation of a new accounting regulation that limits the spending power of the participating municipality when the owned entity reports losses. The authors apply Benford's law on net income figures using the Chi-square and Z-tests on the adjusted version of the Mean Absolute Deviation (MAD) criterion to spot any sign of low data quality. The sample, which consists of 2,120 MOEs, covers the years 2010–2019 and is evenly divided into the periods pre- and post-policy introduction.

Widespread data anomalies were detected following the introduction of the new regulation for MOEs controlled by local governments. Evidence is stronger for entities owned entirely by municipalities. The results suggest that the extent of data manipulation grows as the municipality's ownership stake increases, consistent with the hypothesis that a decrease in spending power through the appropriation of financial resources affects earnings management practices in municipally controlled entities.

This paper sheds light on government-based accounting policies by documenting evidence of somewhat inefficient responses by those responsible for the preparation of financial statements on behalf of municipally owned entities, and, accordingly, insights are provided to help review these policies so as to forestall even indirectly detrimental repercussions on public services.

This paper extends prior research in public-sector earnings management by being the first to test whether MOEs manipulate their earnings as a consequence of participating municipalities' reduced spending capability. Understanding factors influencing earnings management practices driven by governments, other than political incentives, is still an open issue.

This paper explores how adaptive organizations, companies capable of continuously adapting their organizational model, dynamically solve the universal problems of organizing.

The authors applied grounded theory to data acquired from six interpretative case studies, collected in two rounds of interviews (15 in total), then completing and validating the study’s evidence through triangulation with several secondary data sources.

In adaptive organizations, polyarchies and intrapreneurial employees are essential to shape the division of labour, leading to high levels of autonomy and empowering individuals and teams, while reducing bureaucracy and hierarchy. In terms of the integration of effort, digital solutions are preferred to social proof in the provision of information, while the authors note that incentives are always geared towards developing strong higher-order dynamic capabilities.

This paper has some limitations that could be addressed in future research, including longitudinal studies to analyse the link between the universal problems of organizing and a company's dynamic capabilities.

Adaptive organizations go beyond tech firms in responding to the universal problems of organizing work by making specific use of digital technologies.

The paper studies how companies should organize themselves so that they continuously adapt to an ever-changing competitive environment.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

Integrating person-job fit theory with the stressor-emotion model of counterproductive work behavior (CWB), the current study aims to examine which behavioral pattern (fight: CWB vs flight: withdrawal) employees are more likely to adopt when they experience perceived overqualification (POQ). We further investigate anger as the underlying emotional mechanism for these relations because anger can be expressed and thus reflected in CWB, or constrained and thus reflected in withdrawal behavior. Furthermore, different stressor-attenuating strategies including relaxation during work breaks and mastery experiences at work are examined as mitigating factors of these relations.

Time-lagged data were collected from 176 full-time employees in China using a field survey research design.

We found that employees who experience POQ are more likely to engage in withdrawal than in CWB. Anger mediated the relations of POQ with both CWB and withdrawal. Relaxation moderated the relation between POQ and anger, as well as the indirect relations of POQ with CWB and withdrawal through anger.

This study enhances understanding of employees’ affective and behavioral reactions to POQ. However, the survey design was not longitudinal and causality cannot be established.

POQ is associated with undesirable employee behaviors and should therefore be avoided by organizations. If POQ is unavoidable, organizations can use job design and offer training to foster relaxation in between tasks among employees.

In the framework of person-job fit theory, our study provides insight about employees’ “fight” or “flight” responses to POQ, and further illustrates the mechanism and the attenuating factors in this processes.

This study aims to investigate the interaction effect between offering supply chain finance (SCF) programmes and sustainability ratings on the liquidity performance of buyers and suppliers.

The study uses a unique sample of buyers that each have an SCF programme. The sample is complemented with financial information and sustainability scores. The data is analysed through a random effects model.

Aligning with recent advances in SCF literature, the results confirm a tendency for SCF programmes to favour buyers over suppliers. However, the relationship between SCF programme adoption and liquidity performance for buyers and suppliers is positively moderated by the strong sustainability performance of both parties.

Buyers and suppliers are advised to implement and adopt effective SCF programmes that are beneficial for both parties. For buyers, the authors suggest leveraging on SCF programmes as incentives to foster sustainable behaviour among suppliers. For suppliers, the authors recommend caution before joining programmes offered by buyers that do not perform well on sustainability.

Enhancing sustainability within global supply chains and fostering favourable payment practices towards suppliers are crucial for policy development and regulation. The findings clarify the connection between both components, offering valuable insights for policymakers in this domain.

The study is built on a manually picked, unique database of buyers offering SCF programmes to their suppliers. This allows, across a large sample, an evaluation of the differences between buyers that offer SCF programmes and those that do not.