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Most countries face important economic, social and environmental challenges and are strongly committed to invest in research and development (R&D) activities to help support the long-run economic sustainable growth. This paper aims to extend the previous research on macro-economic growth models and introduces endogenous variables to determine the amount of investments in R&D activities.

The model considers four different criteria and six economic sectors and aims at finding the optimal allocation of labor across different sectors. The model also endogenously determines the amount of investments in pollution abatement activities together with energy-related R&D efforts. The paper presents an application to the case of Kazakhstan, an emerging Asian country, that aims to become one of the top 30 most developed countries in the world by 2050.

The model shows the limits of the Kazakh agenda that identified too ambitious goals as the country has to go through a sociotechnical transition that involves a range of modifications in institutional structures, together with changes in user practices and the technological dimension. Kazakhstan should invest more in R&D activities able to develop sustainable energy sources to face the current electricity consumption demand and to reduce the greenhouse gas emission in the future.

The paper provides valuable knowledge for researchers and policy makers interested in the impact of R&D on the long-run economic sustainable growth.

Energy and environmental concerns have gained a significant role in public policy agenda as well as in energy economics literature. As policies often rely on imprecise information on data and goals, fuzzy goal programming (FGP) modeling is a relevant choice to evaluate multi-criteria sustainability. This technique is suitable for the analysis of the Europe 2020 strategy plan dealing with several possibly conflicting objectives in economy, environment, energy and employment. The paper aims to discuss these issues.

The paper presents a FGP model for sustainable implementations for all European Union (EU) countries with respect to Europe 2020 policy goals and provides insights for decision makers to better satisfy conflicting criteria by suggesting optimal allocations of workers in several economic sectors.

The analysis shows an overall great performance of European Union countries in the environmental and social criteria and outlines the needs for significant additional policy measures to reduce energy consumption while increasing the economic output. Comparing the performance of countries within the European Union between those who adopted the euro and those who maintained national currencies shows that Euro countries tend to perform worse in terms of Europe 2020 sustainability, opening opportunities for further research to better investigate on the causes and determinants of these differences.

The paper presents a conceptual model of sustainable development that improves understanding of the concept and reconciles highly competing policy objectives in a common framework. Applying this model to all European Union countries offers both comparison and policy recommendations at a large new scale.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

The purpose of this paper is to describe the cross-fertilisation process between the concept of resilience and building conservation. The authors discuss how the conservation field can address new issues posed by climate change and whether the concept of resilience plays a role within the framework of sustainable building conservation.

Starting from the use of resilience as a “travelling concept”, different interpretations of the term emerging from different fields are compared and interrelated in order to understand how this concept can impact future research in building conservation.

In addition to summarising recent developments in conservation theory with a special focus on how sustainability has influenced the field, this work also suggests some lines of research where resilience could foster interdisciplinary approaches to building conservation and presents some controversial outcomes.

The paper raises a discussion on how the concept of resilience could renew the field of building conservation, helping contemporary society to address the challenges of climate change.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

The purpose of this paper is to explore the application of a hermeneutic-based approach as innovative way to study the Cultural Heritage management in a mesoeconomic space.

The paper builds a theoretical framework based on the analysis of relevant literature in the field of cultural economics, heritage economics and conservation and restoration techniques. Then, after having defined the conceptual hypothesis, a hermeneutical interpretative model is designed for the analysis of the processes of Cultural Heritage management with particular regard to the strategies of stakeholder engagement.

The research shows how the mesoeconomic space is that border area where it is possible to solve more easily the conflicts that arise as a result of the different expectations of stakeholders. Hermeneutical analysis, applied in iterative form, allows us to find common connections, points of contact and convergences between the interpretative horizons of the various stakeholders.

The application of the interpretative model allows the identification of the expectations of stakeholders, improving the knowledge of the tangible and intangible attributes of works of art, in order to design appropriate interventions of restoration, conservation and valorization.

The new model of analysis, based on hermeneutic methodology, is designed to understand and describe the social and economic relations between the different stakeholders involved in the management of Cultural Heritage.

This paper examines for the first time the Cultural Heritage sector within the mesoeconomic area between the micro and the macroeconomy. In addition to this mesoeconomic analysis and conceptual approach, the authors introduce as methodology the economic hermeneutics that represents an innovative tool in the field of economic and business disciplines.

This paper aims to trace subsequent steps of the sustainability reporting evolution in terms of changes in the organisation fields and professional jurisdictions involved. As such, it highlights the (interrelated) organisational and professional challenges associated with the progressive incorporation of “sustainability” within corporate reporting.

The paper draws on Suddaby and Viale’s (2011) theorisation of how professionals reshape organisational fields to highlight how organisational spaces, actors, rules and professional capital evolve alongside the incorporation of sustainability within corporate reporting.

The paper shows organisational spaces, actors, rules and professional capital mobilised during the recent evolution of sustainability reporting, starting from a period in which there was no space for sustainability, to more recent periods in which sustainability gained increasing momentum beyond initial niches, and culminating in more integrated forms of sustainability reporting.

Although the analysis is limited to empirical evidence collected by prior research and practice on sustainability reporting, the paper offers a view to imagine how the incorporation of sustainability within corporate reporting relies on and affects organisational fields and professional jurisdictions.

The paper offers a lens to interpret corporate and professional challenges associated with the more recent evolutions of sustainability reporting practice and standard setting. It also allows framing the papers accepted in the special issue on “new challenges in sustainability reporting” and concludes by suggesting an agenda for future research.

This paper aims to analyse stakeholder sentiment about the corporate social responsibility (CSR) actions implemented by Italian companies between February 20, 2020 and April 20, 2020, which was the first peak in the outbreak of the COVID-19 health emergency in Italy.

Using sentiment analysis, the impact of COVID-19 on CSR actions is analysed through reactions to the news published on Twitter by a sample of Italian news agencies.

The analysis indicates that the actions most appreciated are those that are more radical, e.g. where the company has converted part of its production to make goods that are useful in dealing with the COVID-19 emergency. The study identifies a new category of actions definable as “crisis-shaped CSR.”

This is one of the first studies concerning the effects of the pandemic on both CSR actions and organizational legitimacy.

This work explains which strategic approach to CSR is the most effective in supporting corporate reputation in times of crisis, this study identified which of the CSR initiatives adopted by companies in Italy were more effective in stimulating positive interactions and sentiment among the general public.

The purpose of this paper is to investigate the relationships in alternative food networks (AFNs) between the purchase of food, the motivations of consumers and their socio-demographic profiles.

The study includes a total sample of 1,200 individual questionnaires, administered to the customers of 34 AFNs in different urban areas. The methodology employed is multilevel regression analysis, which is useful for dealing with data with a nested structure.

The results allowed us to depict some findings: the most important motivations in purchasing decisions are the perceived quality as well as comfort with the location of the markets, shopping experience, variety of offered products and delivery methods. Other interesting results relate to the presence of children in the families and the role of women in the choice of quality food.

The observations could represent a basis for thinking about how to improve consumers’ behaviour and, at the same time, try to remove the obstacles to a greater recognition of the importance of AFNs by consumers.

The paper contributes to the debate on food policies. Indeed, the integration of the results into food policies could help to intercept the consumers’ trends and promote a transition of the food system towards a path of sustainability, in which the AFNs are the organisational expression of a change that concerns a wide geography and a large number of social and economic actors.