Search results

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Attributes conveying cultural significance play a key role in heritage management, as well as in differentiating interventions in built heritage. However, seldom the relation between interventions and attributes, either tangible or intangible, has been researched systematically. How do both tangible and intangible attributes and interventions relate? What attributes make interventions on built heritage differ?

This paper conducts a systematic content analysis of forty-one international doctrinal documents—mainly adopted by the Council of Europe, UNESCO and ICOMOS, between 1877 and 2021. The main aim is to reveal and compare the selected eight intervention concepts, namely—restoration (C1), preservation (C2), conservation (C3), adaptation (C4), rehabilitation (C5), relocation (C6), reconstruction (C7) and renewal (C8)—and their definitions, in relation to attributes, both tangible and intangible. The intensity of the relationship between intervention concepts and attributes is determined based on the frequency of the mentioned attributes per intervention.

There were three key findings. First, although the attention to intangible attributes has increased in the last decades, the relationship between interventions and tangible attributes remains stronger. The highest frequency of referencing the tangible attributes was identified in “relocation” and “preservation,” while the lowest was in “rehabilitation.” Second, certain attributes play contradictory roles, e.g. “material,” “use” and “process,” which creates inconsistent definitions between documents. Third, as attributes often include one another in building layers, they trigger the intervention concepts in hierarchical patterns.

This paper explores and discusses the results of a novel comparative analysis between different intervention concepts and definitions, with a particular focus on the attributes. The results can support further research and practice, clarifying the identified differences and similarities.

The relationships between tourist resorts and transnational crime are rarely analyzed systematically. This paper begins to fill this gap by examining how organized crime groups and individuals linked to them can take advantage of tourist resorts to commit crimes.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.

The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.

Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.

The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.

The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.

The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.

These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

Covid-19 is a worldwide pandemic disease that changed the government communication to citizens about the health emergency. This study aims to provide in-depth research about regional Italian government communication through social media (SM) and its effects on citizens' engagement.

The study uses a case analysis, focusing on the Italian context. In detail, the authors analyse the more involved Italian regions in Covid-19 pandemic (Lombardy, Veneto, Piedmont, Emilia Romagna and Tuscany) applying the Crisis and Emergency Risk Communication (CERC) model.

The results reveal that SM is a powerful tool for communication during a health emergency and for facilitating the engagement with stakeholders. However, results also highlight a different perception about the timing of the Covid-19 crisis.

Findings suggest a gap between the answer of the public government compared to the citizens' needs that are clear since the first earlier stage of the pandemic event. The engagement level is very high since the first phase of the pandemic event; however, to be adequately developed, it requires specific and timing information that are not always in line with the citizens’ communication needs.

This is the first research that aims to study the citizens' engagement in the Italian regions during the Covid-19 pandemic.