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This research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation.

Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet.

The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications.

The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability.

In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

The purpose of this article is to investigate if and how social capital offline – stemming from face-to-face interactions – and social capital online – stemming from social digital media – can influence early-stage entrepreneurs, i.e. ventures with up to 42 months of existence.

The authors used herein a qualitative research approach. The method used was the case study. The authors investigated three early-stage entrepreneurs in order to achieve the objective of the paper. These entrepreneurs are both the unit of analysis and the unit of observation.

The outcomes of this research indicate (1) the combined importance of social capital offline and online; (2) the different performance of the two different types of social capital (they seem to operate in relatively distinct ways) and (3) the existence of recursiveness between resources stemming from the two social spheres (offline and online).

As research limitations, the authors point out the following: (1) the use of semistructured interviews as the only data collection instrument; (2) the limitation of the outcomes to entrepreneurs only (3) the absence of information on the performance of the business ventures; the focus of the paper was only on establishing causality between social capital offline and online and entrepreneurial performance.

This paper provides important research contributions. Initially, the paper presents a range of offline and online variables, which can be used in further research. At the same time, the paper emphasizes the combined impact of social capital offline and online, expanding the literature related to entrepreneurship. Moreover, this study proposes the creation of an integrative model. Finally, the authors point out the need for new theoretical and empirical studies on the subject, which still presents a gap in the literature.