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Disaster risk reduction is of prime importance in informal settlements in the Global South, where several forms of vulnerability coexist. Policy and official programmes, however, rarely respond to the needs and expectations of citizens and local leaders living in these settlements. Even though these agents constantly attempt to reduce risks in their own way, we know very little about their activities, motivations and effective impact on risk reduction. Here we seek to conceptualize bottom-up initiatives to better grasp their origins, limitations and success.

Through a four-year action-research project in Colombia, Cuba and Chile, we theorize about the production of change by local agents. Through detailed case studies we explored the activism of 17 local leaders. Through narrative analysis we studied their motivations and explanations. Finally, by documenting 22 initiatives, we revealed effective changes in space.

In the face of risk and disasters, residents and leaders in informal settings engaged in symbolic, physical and social spaces of interaction. Their actions were guided by trust, emotions, time cycles and activism. Local agency was justified by narratives about risk and climate change that differ from those of authorities and scholars.

There is still limited understanding of bottom-up initiatives in informal settings. It is crucial to conceptualize their origins, limitations and success. The focus on three specific countries necessitates further research for broader applicability and understanding.

A better comprehension of bottom-up actions is crucial for informing policies and programmes aimed at reducing risk in informal settings. Stakeholders must recognize the political, social and cultural roles of these actions for more impactful climate action.

We borrow Simon’s concept of “artefact” to introduce the notion of “Artefacts of Disaster Risk Reduction”, providing insights into the multifaceted nature of bottom-up initiatives. We also emphasize the simultaneous political and phenomenological character of these actions, contributing to a deeper understanding of their origins and impact.

This study examines how perceived institutional support, parental role models, and entrepreneurial self-efficacy, representing both macro-level and personal-level factors, collectively influence students' intentions to pursue entrepreneurship in Mexico and Uruguay.

This research utilized quantitative methodology, specifically survey techniques, to collect data from students attending private universities. The study achieved a valid sample size of 419 respondents. Various reliability and validity tests were conducted before structural equation modeling was employed to test the hypothesized relationships between variables.

The analysis revealed that perceived institutional support does not directly impact students' entrepreneurial intentions (EI). Instead, its effect is mediated through entrepreneurial self-efficacy and the presence of parental role models, both of which are strong predictors of EI. Additionally, the study identified a direct correlation between students' nationality, their academic programs, and their EI. Age and gender, however, did not significantly influence EI.

This study provides theoretical insights into understanding EI by combining macro-level and personal factors. This integrative method contributes to a more comprehensive approach of predicting EI within the context of Latin America.

The study suggests boosting investment to improve the quality of institutions, fostering an environment that supports entrepreneurship, and offering students opportunities to learn from successful role models.

This study was conducted in the context of two economies in Latin America. The novelty lies in combining perceived institutional factors and individual motivators to understand EI in Latin America. It uniquely emphasizes the significance of familial influences, particularly parental role models, in its analysis.

This paper aims to identify future scenarios and convergent technologies regarding the plantain chain in the region of Quindío, Colombia. It proposes the definition of key variables, convergent technologies, future objectives, future scenarios and hypotheses based on stakeholders’ and experts’ opinions collected through questionnaires, surveys and workshops.

The present analysis seeks to identify and anticipate the future routes for the improvement of scientific, technological, innovative and skills management of the plantain agroindustrial chain in the region of Quindío, Colombia using the foresight-by-scenarios and, Delphi methodologies and finally validating the results with artificial intelligence code and natural language processing.

After the analysis of 100 initial variables, the results suggested the identification of five key variables defined by the stakeholders and matrix-based multiplication applied to a classification (MICMAC) analysis such as “weather,” “financing and economy,” “grouping and associativity,” “crop” and “territorial planning and raw material.” Moreover, the definition of four future objectives along with the matrix of alliances and conflicts, tactics, objectives and recommendations (MACTOR) analysis suggested scenarios according to the probability. The most possible, probable and desirable scenario, was the cooccurrence of the five proposed hypotheses. Furthermore, the Delphi analysis allowed us to define nine subgroups from 116 subtopics including: “plantain varieties,” “plantain agroindustry,” “waste use” and “crop,” among others.

Due to the specificity of the analyzed agro-chain, the study only encompasses the plantain and banana sectors. However, the implications are related to the generation of projects in the selected technologies.

This paper includes implications for the development of prospective studies combining two or more different methodologies such as foresight-by-scenarios and Delphi method at the same time and further comparing the results with artificial intelligence analysis.

The generation of public policies in the sector and input for governmental analysis and tools for decision-making with a well-grounded, systematic and rational point of view.

This work describes for the first time, the implementation of the Delphi method regarding an important agroindustry sector such as plantain and banana prospective study. Furthermore, it explains the alignment of two methodologies; foresight-by-scenarios and the Delphi method related to the sectorial approaches, and convergent technologies and innovations, respectively. Moreover, a complementary bibliometric analysis with global terms related to the plantain or banana agroindustry was also included. In a novel way, we also applied an artificial intelligence code using Python software to contrast the results previously obtained in the foresight-by-scenarios method.

Our comprehensive study, which is highly relevant to the current state of the tourism industry, investigated the extensive impact of tourism entrepreneurial ecosystems (TEE) on sales growth in European countries based on a sample of 109 entrepreneurs.

We studied 109 entrepreneurs in the tourism sector in Germany and France and analyzed the data using quantitative methods.

The results showed that financing and funding, networks, technological and international trade affect the sales growth of the tourism entrepreneurs. In addition, the uneducated workforce, legal and political factors as well as TEE do not influence the sales growth.

These findings have significant and practical implications for entrepreneurs and policymakers in the tourism industry, providing them with valuable insights for decision-making.

This study explores the impact of the residents' voice and social media brand engagement on coopetition in tourism destinations with the mediating role of knowledge sharing. The study's statistical population includes tourists who visited Iran. The sample was 243 tourists who visited Iranian tourist different villages in the spring of 2023. This empirical study adopted a descriptive correlational method and used PLS3 for data analysis. The effects of residents' voices, knowledge sharing, and engagement with social media improve cooperation between tourism destinations, help create platforms for creativity and innovation in this industry, and ensure the promotion of sustainability and attractiveness of tourism. Therefore, the mutual analysis of the effects of different factors in rural tourism in Iran is valuable in providing a new method to improve the tourism experience in this field.

Purpose: The study aims to enrich sustainable development assessments by integrating the Happy Planet Index (HPI) with Sustainable Development Goals (SDGs) using locally weighted scatterplot smoothing (LOESS) regression, revealing complex trade-offs among well-being, environmental efficiency, and broader sustainability objectives to guide more nuanced and effective policy actions.

Need for study: This study is needed to address the limitations of traditional economic metrics in sustainable development by offering a holistic framework that incorporates well-being and environmental sustainability, thus providing a more comprehensive understanding of and approach to achieving global sustainability goals.

Methodology: The methodology involves using LOESS (locally weighted scatterplot smoothing) regression to analyse the nuanced interactions between the HPI scores and SDGs, facilitating a deeper understanding of the complex relationship among environmental efficiency, well-being, and sustainability.

Findings: The optimal LOESS model adjustment of SDG scores with the HPI highlights stark national disparities: countries like North Macedonia, Cyprus, and Turkey underperform due to urbanization, inequality, instability, and environmental issues, whereas Finland, Sweden, and Denmark excel in SDG achievements, likely due to robust welfare, environmental policies, political stability, and infrastructure.

Practical implications: This study’s practical implications suggest that integrating HPI with SDGs can guide policymakers towards more effective, balanced strategies for sustainable development, acknowledging the trade-offs between well-being and environmental efficiency to achieve global sustainability goals.

This study explores distinct capital structure patterns between private and public companies, examining the varying influence of determinants on debt choices contingent upon a firm’s existing debt position.

Employing annual data from 2012 to 2022 for 142 public firms and 660 private firms in a large emerging economy, we use quantile regression within a panel data framework to study the heterogeneous effects of debt determinants, incorporating firm and time random effects.

Our findings indicate that such factors as size and operating margin contribute to higher levels of debt, while investment opportunities reduce the debt level. Further analyses, when accounting for a firm’s likelihood of being publicly traded, reveal that dividend payout and operating margin significantly influence debt levels, exclusively in the presence of high debt proportions. Conversely, investment opportunities emerge as a substantial determinant in all debt scenarios. In addition, we found a strong persistence in the indebtedness of companies, and we conclude that the effect of the determinants of indebtedness is heterogeneous according to the level of debt of companies.

This research provides a comprehensive comparison between private and public firms, not only in terms of debt levels but also in key capital structure determinants, highlighting their significance within the context of varying debt levels.