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In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

Traditional purchasing best practices primarily follow a commercial logic and may not necessarily be applicable for social enterprises (SEs) supplier selection. This study examines how SEs focused on poverty alleviation select suppliers amidst competing institutional logics to achieve both social impact and economic performance.

A grounded theory methodology is applied to guide semi-structured interviews with 18 fair trade verified SEs. Constant comparison methods aided in determining the point of data saturation was reached.

The results of this study indicate that SEs select marginalized suppliers based on implicit criteria that is initially based on social-welfare logic and then through a blend of commercial and social-welfare logic based on company structure.

This study is the first to reveal that SEs addressing social issues do not follow the traditional criteria for supplier selection but have their own unique selection criteria when selecting suppliers.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

The purpose of this study is to contribute to the discussion surrounding impact measurement on social enterprises (SEs). The findings provide a more nuanced perspective on tensions that often emerge from SEs journeys by presenting the complexities which social entrepreneurs and investors should be attentive to.

This research used grounded theory as the means to explore how stakeholders accomplish the requirements for impact measurement, overcoming the challenges that arise in the process. Through 18 semi-structured interviews, the authors develop a conceptual model to better understand how a practice that is often taken for granted might compromise SEs achievements and sustainability in the long term.

The proposed model uncovered an unintended consequence of impact measurement: mission drift. The requirements to assess the social impact raise expectations on different actors and create challenges that affect the true purpose of SEs, the delivery of their social mission.

This study contributes to research and practice. First, the authors develop a theoretical model for social entrepreneurs and social investors to shed light on the hidden consequences of impact measurement. Second, the authors strengthen the knowledge in the field by conducting a study on SEs outside the mainstream Western-centric context.

The authors enrich the literature by exploring the tensions related to impact measurement in SEs in the Global South and unravel new perspectives on the subject.

This study aims to develop an in-depth understanding of how supplier selection helps social enterprises achieve their social missions while maintaining commercial viability.

The paper applies a multiple-case design to study the supplier selection processes of 15 Dutch social enterprises.

Social enterprises tend to build supply relationships through existing networks and evaluate suppliers based on value alignment, relationship commitment, resource complementarity, and cost. Depending on the possibility of social value creation in supplier selection, the importance of these criteria varies across different social enterprise models and between key and non-key suppliers. Moreover, suppliers’ long-term relationship commitment can help reconcile tensions between the social and commercial logic of a social enterprise and facilitate impact creation.

Data collection is limited to the perspectives of buyers – the social enterprises. Future research could collect supplier-side data to explore how they engage with social enterprises during the selection process.

Managers of social enterprises can use our research findings as guidance for selecting the most suitable suppliers, while organizations that want to collaborate with social enterprises should actively build network ties to be identified.

We contribute to the cross-sector collaboration literature by showing the underlying reasons for the preference for network reinforcing and indirect networking in supplier identification. We contribute to the social impact supply chain literature by revealing the critical role of supplier selection in shaping collaboration outcomes.

In prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.

This study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.

The main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.

This study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.

The purpose of this study is to examine the structure of the international fossil fuel trade network (IFFTN) and assess its effects on CO₂ emissions and global trade patterns. This research integrates complex network theory with econometric analysis to explore the dynamics of fossil fuel trade and its implications for environmental quality across various countries. Specifically, the study analyses the roles of different countries within this global network, examines the relationship between trade volumes and environmental impacts and evaluates how advancements in renewable energy generation could mitigate these effects. Through this comprehensive examination, the study seeks to provide an in-depth understanding of the trade-environment nexus.

The study uses data on international fossil fuel trade from 2005 to 2020, which includes 74 countries categorized as high-income, low-income and Asian economies based on their roles in the global market. This research constructs the IFFTN, where countries are depicted as nodes and trade links as edges. The authors analyse network parameters, such as degree, density and clustering coefficient, along with trade metrics like strength and centrality. These parameters are integrated into a panel fixed effects model, with the robustness of the findings confirmed through dynamic ordinary least squares (DOLS) analysis.

The study finds that the dynamic fossil fuel trade network includes key players such as the USA, China, France, India, the Netherlands and South Korea. It demonstrates increased connectivity and dependence among these countries, directly correlating with higher CO₂ emissions. However, this correlation is mitigated by the adoption of renewable energy, particularly in Asia and high-income countries. The impact on environmental quality is mediated through scale, technique and composition effects, suggesting significant environmental improvements through enhanced industry structure, technological progress and economies of scale.

This study recognizes several limitations. First, the categorization of countries into Asian economies, low-income and high-income groups may oversimplify the intricate effects of economic status on environmental impacts. Second, focusing primarily on per capita CO₂ emissions may neglect other critical environmental indicators. Future research should consider examining regional variations and including a wider range of environmental metrics. This approach would offer a more detailed perspective on the nuanced interactions between economic development and environmental sustainability, enhancing the depth and applicability of the findings.

To address the challenges of the IFFTN and CO₂ emissions, several practical policy measures are recommended. Governments should enhance international cooperation by establishing global platforms for sharing best practices and initiating technology transfer agreements to accelerate the adoption of energy-efficient technologies. Additionally, a phased transition towards more sustainable energy sources is crucial, involving increased investment in the renewable energy sector alongside incentives for adopting green technologies. On the trade front, governments should modify trade partnerships to address congestion externalities, fostering a shift towards more sustainable and environmentally friendly trade practices.

The social implications of the IFFTN are profound. As global reliance on fossil fuels continues, communities face heightened health risks due to increased pollution. Transitioning to renewable energy can alleviate these health concerns and the creation of green technologies, enhancing social well-being. Moreover, equitable access to energy-efficient solutions can reduce energy poverty, particularly in low-income countries, fostering greater societal resilience and inclusivity.

This study offers a pioneering examination of the trade-energy nexus across 74 countries, using complex network models to analyse diverse economic settings, particularly in Asian economies dominated by non-renewable energy. It identifies key market players and assesses their impact on dynamics such as congestion and market power. Additionally, the study explores the positive effects of renewable energy capacity on these relationships, highlighting its crucial role in driving sustainable energy transitions and enhancing the understanding of indirect trade-environment interactions.

Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.

This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.

Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.

This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.

The purpose of this study is to identify the key factors that drive social entrepreneurship (SE) among Ecuadorian social entrepreneurs.

The study was developed following a qualitative and exploratory research design based on the identification of social entrepreneurs through the nonprobabilistic sequential sampling technique, giving a total of fifteen social entrepreneurs who were given a structured interview that was later analyzed in the ATLAS.TI software for the identification, analysis and categorization of drivers.

The results will identify eight drivers for SE, including social exclusion, public support, sources of employment, economic income, family support, environmental awareness and fair trade, as standards and values. Overall, the findings help to improve the understanding of the factors that determine the creation and development of social enterprises in the country.

Deepen and increase the literature on SE in Latin America. Contribute to reducing the knowledge gap on the topic.

Taken together, the results provide empirical evidence and help to better understand the factors that determine the creation and development of social enterprises in the country.

Identificar los factores clave que impulsan el emprendimiento social en emprendedores sociales ecuatorianos.

El estudio se desarrolló siguiendo un diseño de investigación cualitativo y exploratorio basado en la identificación de emprendedores sociales a través de la técnica de muestreo secuencial no probabilístico, obteniendo un total de quince emprendedores sociales a quienes se les realizó una entrevista estructurada que posteriormente fue analizada en el software ATLAS.TI para la identificación, análisis y categorización de impulsores.

Los resultados identificarán ocho impulsores del empresariado social, entre los que se encuentran la exclusión social, el apoyo público, las fuentes de empleo, los ingresos económicos, el apoyo familiar, la conciencia medioambiental y el comercio justo, como normas y valores. En general, los resultados ayudan a mejorar la comprensión de los factores que determinan la creación y el desarrollo de empresas sociales en el país.

En conjunto, los resultados aportan evidencia empírica y ayudan a comprender mejor los factores que determinan la creación y desarrollo de empresas sociales en el país.

Profundizar e incrementar la literatura sobre Emprendimiento Social en América Latina. Contribuir a reducir la brecha de conocimiento sobre el tema.

Identificar os principais fatores que impulsionam o empreendedorismo social nos empreendedores sociais equatorianos.

O estudo foi desenvolvido de acordo com um projeto de pesquisa qualitativo e exploratório baseado na identificação de empreendedores sociais por meio da técnica de amostragem sequencial não probabilística, totalizando quinze empreendedores sociais que foram submetidos a uma entrevista estruturada, posteriormente analisada no software ATLAS.TI para identificação, análise e categorização dos fatores determinantes.

Os resultados identificarão oito motivadores do empreendedorismo social, incluindo exclusão social, apoio público, fontes de emprego, renda econômica, apoio familiar, consciência ambiental e comércio justo, como padrões e valores. Em geral, os resultados ajudam a melhorar a compreensão dos fatores que determinam a criação e o desenvolvimento de empresas sociais no país.

Em conjunto, os resultados fornecem evidências empíricas e ajudam a entender melhor os fatores que determinam a criação e o desenvolvimento de empresas sociais no país.

Aprofundar e aumentar a literatura sobre empreendedorismo social na América Latina. Contribuir para reduzir a lacuna de conhecimento sobre o tema.

The complexity of contemporary societal challenges in emerging countries reanimates the necessity of collective action to resolve them. What is required is system change, namely, transformations in policy, practice, power relationships, market dynamics and social customs that underlie social and environmental issues. Technological innovations, paired with intentional social changes, might play a transformative role in this effort. This paper aims to investigate the relationship between the adoption of technologies in social enterprises (SEs) and their contribution to achieving system change. It also addresses the effects of their hybrid nature on this relationship.

The analysis relied on data collected through a survey of the global population of Ashoka fellows, which is largely based in emerging economies. Three models were developed concerning different pathways to achieve system change identified in the theoretical framework. These were tested using Probit regressions.

The investigation confirms that technology can support SEs in navigating complex pathways to achieve system change rather than merely enabling linear scaling operational strategies. The pursuit of economic value creation, in conjunction with a social mission, decreases the ability of SEs to achieve system change. This is because the scaling paths which hardly create revenues are neglected.

The study conceptualises a multifaceted model of system change. It tests the framework empirically to show that SEs can adopt technologies to unleash complex system change processes to generate societal impact, on top of merely demonstrating linear approaches to scaling or replication. The paper questions the capacity of SEs to facilitate system change without appropriate financial support and the inherent tensions between hybridity and the depth of system change dynamics.