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This study examines whether international tourism demand in the Visegrád countries is influenced by countries' risk rating on environmental, social and governance (ESG) factors, as non-economic factors relating to ESG risks have been ignored by previous researches on determinants of international tourism demand.

The study investigates panel data for the Visegrád countries comprising the Czech Republic, Hungary, Poland and Slovakia over the period 1995–2019. Recently developed techniques of augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators are employed so as to take care of cross-sectional dependence, nonstationary residuals and possible heterogeneous slope coefficients.

The regression estimates suggest that besides economic factors, the perception of international tourists regarding ESG risk is another important determinant of international tourism demand in the Visegrád countries. The study also established that income levels in the tourists' originating countries are the most critical determinant of international tourism demand to the Visegrád countries.

The research outcomes of the study include the need for the Visegrád countries to direct policies towards further mitigating their ESG risks in order to improve future international tourism demand in the area. They also need to ensure exchange rate stability to prevent volatility and sudden spikes in the relative price of tourism in their countries.

The purpose of this paper is to empirically investigate the effect of real credit ratings change on capital structure decisions.

The study uses three models to examine the impact of credit rating on capital structure decisions within the framework of credit rating-capital structure hypotheses (broad rating, notch rating and investment or speculative grade). These hypotheses are tested by multiple linear regression models.

The results demonstrate that firms issue less net debt relative to equity post a change in the broad credit ratings level (e.g. a change from A- to BBB+). The findings also show that firms are less concerned by notch ratings change as long the firms remain the same broad credit rating level. Moreover, the paper indicates that firms issue less net debt relative to equity after an upgrade to investment grade.

The study covers the periods of 2009 to 2016; therefore, the research result may be affected by the period specific events such as the European debt crisis. Moreover, studying listed non-financial firms only in the Tadawul Stock Exchange has resulted in small sample which may not be adequate enough to reach concrete generalization. Despite the close proximity between the GCC countries, there could be jurisdictional difference due to country specific regulations, policies or financial development. Therefore, it will be interesting to conduct a cross country study on the GCC to see if the conclusions can be generalized to the region.

The paper contributes to the literature by testing previous researches on new context (Kingdom of Saudi Arabia, KSA) which lack sophisticated comparable studies to the one conducted on other regions of the world. The results highlight the importance of credit ratings for the decision makers who are required to make essential decisions in areas such as financing, structuring or operating firms and regulating markets. To the best of the authors’ knowledge, this is the first study of its kind that has been applied on the GCC region.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

This study aims to explore the impact of controversial firms’ corporate sustainability assessments on their risk exposure according to the environmental, social and governance (ESG) paradigm.

This study conducts a cross-sectional study using the ordinary least squares approach to test how corporate social responsibility practices affect firms’ risk exposure, testing the three single impacts of ESG components and the impact of an overall ESG assessment. This study considers the largest Standard & Poor’s (S&P) 500 stock market index companies and focus on a double-risk measurement – systematic and idiosyncratic – developing an empirical study on 132 controversial companies listed on the S&P index.

Empirical findings indicate that the overall ESG assessment and the environmental and social sub-dimensions decrease idiosyncratic firm risk. At the same time, no significant results are found according to the systematic risk component.

This study fits into the domain of risk management research, investigating whether additional and non-financial disclosures regarding sustainability issues decrease information asymmetries, improving investors’ decision-making and stakeholders’ relations. Prior literature has shown limited evidence on the relationship between corporate social performance (CSP) and firm risk based on controversial companies. The main contribution is to consider the controversy as an independent factor from the industry sector, given that the implications of CSP actions and practices are mainly firm-specific.

The purpose of this study is to shed light on the relationship between patent applications and long-term risk for small firms across the global financial crisis of 2008. During a crisis, firm risk often skyrockets, and small and medium enterprises face significant dangers to their business continuity. However, managers have a set of strategies that could be implemented to increase a firm’s resilience, sustaining competitive advantages and improving access to financial resource. The authors focused on the investigating the impact of patenting activities on small business risk in a time of crisis.

This is a quantitative study based on a sample of Italian firms that applied for a patent in 2005. The changes in corporate credit ratings over a five-year period are related to different proxies of patent activity using multivariate regression analysis.

Firms that filed for a patent were more resilient, compared to the control sample, during the financial crisis. Innovative activities resulting in patent application seem to deliver strategic resources useful to tackle the crisis rather than increase riskiness. The moderating effect of patents on risk sensitivity is stronger for small firms and when the number of patents or the patent intensity is larger.

Limited evidence is available on how patent applications are related to risks for small firms during an economic crisis. The authors highlight that the innovative efforts resulting in patent applications can support small business resilience. The authors also point out that the implementation of patent information in small firms' credit score modeling is still an uncommon practice, while it is useful in estimating firm risk in a way more robust to exogenous credit shocks.