John Cabot University ScholarShip

Civic associations are expected to foster civic, pro-social behavior, but this optimistic view is increasingly contested. We argue that populist radical right parties can strategically target and infiltrate associations to diffuse anti-establishment rhetoric and anti-democratic attitudes. We illustrate this phenomenon by examining the relationship between civic associations and compliance with government rules during Germany's first Covid-19 lockdown with a difference-in-differences design. Results show that areas with denser sport, nature, and culture clubs recorded higher mobility under lockdown. We document the infiltration mechanism and the spreading of anti-democratic attitudes within associations, using survey and election data and qualitative evidence including interviews. In doing so, we shed light on a negative effect of social networks and an understudied strategy of challenger populist parties.

The information age of big data has increased the use of predictive data analytics for decision-making, leading to complex societal and legal debates with respect to statistical discrimination and the protection of civil liberties. This paper presents a simple pedagogic narrative in the context of asymmetric information. Students walk through a predictive-algorithmic decision-making process by a principal and agent. A simple predictive algorithm is constructed that leads to statistical discrimination and incentivizes principals to acquire more information confronting civil liberties of the right to privacy and protection from unnecessary surveillance. The lesson can be applied to labor, financial, and insurance markets.

Frame of the research: The concept of Purpose-driven Businesses (PDBs) has emerged as a response to pressing global challenges, including climate change, social inequality, and biodiversity loss, which call for a fundamental rethinking of the role of businesses in addressing societal and environmental needs. While the term has gained significant traction, its precise meaning remains ambiguous, often overlapping with existing constructs in management literature, such as Corporate Social Responsibility and Shared Value Creation. This study operates at the intersection of these theoretical perspectives, aiming to provide a structured analytical framework for understanding how PDBs incorporate social and environmental values into their strategies and balance the diverse expectations of stakeholders. Purpose of the paper: This paper is the end product of an extended conversation and debate between a number of business management scholars with a particular interest in the concept of Purpose. This concept has been increasingly used to characterise businesses which do not only have a conventional commercial purpose but also a social one. Despite the popularity of the term, the precise meaning of “purpose” remains somewhat unclear, especially when compared to the wide set of constructs which have already been used in business management literature to refer to similar types of business. A deeper analysis of the differences and similarities with other related concepts is thus needed. This paper summarizes the results of this discussion and provides a detailed definition of PDBs. Methodology: This paper is the result of an open debate on the concept of Purpose, which began with a series of face-to-face and online discussions between management scholars with an interest in this area. Firstly, the group of scholars who participated identified the existing literature on purpose-driven businesses. Next, the theoretical approaches that were most closely aligned with this concept were identified and each one of them was assigned to the person with the most expertise in that area so that they could examine it in more detail for the purposes of this article. Finally, an agreed definition of PDBs was produced. Findings: This paper sets out the differences between PDBs and other similar concepts. Further, we provide a list of characteristics that PDBs must have, may have, and cannot have. Research limitations: This article discusses theoretical approaches and is the result of collaboration between a number of scholars who specialize in the field. Our theoretical findings may suggest a way forward for future empirical studies. Practical implications: This paper provides a more nuanced and comprehensive understanding of what is meant by PDBs, with practical implications for managers working at both day-to-day and strategic levels. Originality of the paper: This analysis was the result of a process of discussion between scholars working in the area of PDBs. The checklist of the elements that characterise PDBs is intended to be a useful point of reference for scholars and professionals working in this field.

Frame of the research: Crowdfunding has emerged as a transformative fintech solution, significantly changing the financing of new projects. Among the various forms of crowdfunding, purpose-driven platforms like Kiva transcend traditional financial transactions by emphasizing social and economic empowerment. Despite its potential, there remains a gap in understanding how borrowers’ narratives influence lenders’ decisions, particularly in contexts where social motivations are paramount. This research aims to bridge this gap by analyzing the narratives used by borrowers to describe their projects on the Kiva platform. Purpose of the paper: This study seeks to explore and classify the different types of narratives employed by borrowers on Kiva to attract funding. By doing so, it aims to uncover the socio-economic impacts of these loans and the role of crowdfunding as a driver for social and economic empowerment. The research focuses on linking the intrinsic motivations of lenders and the narratives presented by borrowers. Method: This study employs a topic modeling approach to analyze a dataset of Kiva- funded projects, focusing on borrowers’ narratives. The methodology includes data cleaning, visualization, and topic modeling using Latent Dirichlet Allocation (LDA) applied to text from the “use” and “description” sections of Kiva projects’ pages from 2005 to 2021. Results: Our analysis uncovered eight distinct loan narratives, each associated with various types of entrepreneurial projects. These narratives range from agricultural advancements to educational pursuits, highlighting the diverse aspirations of borrowers. This diversity not only demonstrates the broad spectrum of entrepreneurial projects supported by crowdfunding, but also emphasizes the nature of these loans in meeting the specific needs and goals of individuals across different regions and sectors. Limits: Our study’s scope is limited to data from the Kiva platform, affecting the extent of our conclusions. Future research should expand to include multiple crowdfunding sources to enhance the diversity and applicability of findings. Practical implications: By comprehending the diverse entrepreneurial initiatives facilitated by Kiva loans, stakeholders can customize financial products and support services to better align with the requirements of borrowers. This insight is crucial for enhancing the efficacy of microfinance in fostering economic development and empowering underserved communities. Originality of the paper: Through the identification of specific borrowers’ narratives, this research contributes to the broader discourse on financial inclusion, economic empowerment, and the role of crowdfunding in facilitating access to capital for underserved communities. This study not only provides a deeper understanding of the narratives that drive purpose-driven crowdfunding but also highlights the importance of aligning financial support with the socio- economic goals of borrowers to foster sustainable development and inclusive growth.

This paper explores the determinants of greenhouse gas (GHG) emissions in Europe, focusing on transportation-related variables. By combining classical econometric models with Machine Learning (ML) techniques, we analyze data spanning from 2013 to 2021. The empirical findings highlight the complex relationship between newer passenger cars and GHG emissions, noting the significant impact of their production and increased usage. Conversely, the adoption of alternative fuel vehicles is found to significantly reduce emissions. This is further supported by ML models, which emphasize the critical role of car density and alternative fuel vehicles in determining emissions. Policy implications suggest the need for targeted interventions, including the promotion of electric and hybrid vehicles, enhancements in transportation infrastructure, and the implementation of economic incentives for clean technologies.

This article investigates the complex relationship between customer satisfaction and behavior in services, aiming to provide actionable insights for managers seeking to leverage satisfaction for improved business outcomes. The link between satisfaction and behavior is characterized by anomalies and complexities, such as satisfed customers switching brands, a disproportionate efect of low levels of dissatisfaction on behavior, non-linearity, asymmetry, and zones of low behavioral response. While these insights have signifcantly advanced scholarly discourse, they are often difcult to apply in practice, as they tend to focus narrowly on specifc aspects—such as non-linearity or the role of emotions—or prioritize satisfaction over dissatisfaction, leaving managers without a holistic framework to navigate these dynamics. This study emphasizes the need for a comprehensive understanding of how varying satisfaction levels, from extreme dissatisfaction to extreme satisfaction, infuence customer buying behavior. Using a longitudinal case study of a Fortune 500 fnancial services company, and adopting a multidisciplinary approach that integrates the above-mentioned fndings with insights from behavioral economics, neuroscience and brand equity, this article proposes a preliminary integrative model of the satisfaction-behavior curve that explains its non-linearity and asymmetry, as well as the cognitive and emotional factors that drive its infection points. Managerial implications include prioritizing actions that move customers beyond the “indiference zone”; addressing promptly and proactively even minor dissatisfaction episodes; closely monitoring defection rates and their underlying causes. As these early fndings are based on a single case study in an industry characterized by high involvement, future research should validate the proposed curve across industries and contexts, and considering moderating variables like culture, competition, and market maturity.

Higher Education Institutions (HEIs) are central to fostering innovative entrepreneurship and regional development by increasing human capital and facilitating knowledge transfer through R&D. This paper explores the interplay between graduate supply and R&D in driving innovative startup creation across Italian provinces (2015–2021). The findings highlight a synergy between these factors: public R&D boosts graduate impact in the South, while university R&D complements the dense graduate presence in the North. These dynamics call for tailored strategies to optimize HEIs’ roles as engines of economic growth, particularly in lagging regions.

This research investigates the macroeconomic determinants of crypto adoption, illuminating the potentials of cryptocurrencies to accelerate financial inclusion. By exploiting an extensive dataset from 165 countries between 2019 and 2021, this study employs various econometric methodologies, including Panel Feasible Generalized Least Squares (PFGLS), Robust Least Squares (RLS), and Quantile Regressions (QR). These classic econometric techniques are complemented by several machine learning techniques such as Bagging, Boosting, and Support Vector Machine (SVM) regressions, as well as Artificial Neural Networks (ANNs) and Naïve Bayes (NB) classification algorithms. The results show an interesting trend: cryptocurrency adoption is more prevalent in countries with robust financial markets and higher education levels. This suggests that crypto adoption is primarily a byproduct of sophisticated financial environments and an educated population, rather than a direct facilitator of financial inclusion.

The aim of this paper is to estimate the effects of the COVID-19 pandemic on the wage polarization in Italy, combining individual characteristics with their task content in terms of physical proximity within the workplace. We use an innovative dataset which combines data from two sample surveys, the Italian Labor Force Survey and Italian Survey of Professions, which provides information on nature and content of the tasks. First, by employing a non-parametric method (the Relative Distribution) we detect a general increasing wage polarization in the sub-period 2020–2019, driven by lowest deciles, after a reduction in the previous one (2019–10). Different groups have been also isolated. Workers with low education, high proximity to customers job, such as the immigrant, younger and female ones are the categories that more suffered the general downgrading of the Italian wages happened during the COVID-19 crisis.

In the race to harvest the power of Artificial Intelligence (AI) in virtually every field, researchers and practitioners are faced with an ever increasing supply of novel tools that have not undergone domain-specific tests. This paper informs the methodological choices of researchers in economics and finance by comparing the performance of three Natural Language Processing (NLP) methods at an important task, namely using text analysis for portfolio diversification. Portfolio management can benefit from analysing text data in the form of company descriptions, since the returns of companies with similar descriptions tend to be correlated and consequently, portfolios of dissimilar companies should have lower risk. In this paper, three NLP methods are used to construct so-called minimum semantic concentration portfolios, which are designed to leverage the semantic diversity of the business descriptions of constituent companies to reduce portfolio volatility. Two widely used large language models (BERT and GPT) and an alternative AI solution inspired by neuroscience, called semantic fingerprinting are put to the test of comparing meaningfully the business descriptions of the S&P 500 and respectively Europe 600 constituents in order to derive actionable investment insights. The results show that all three NLP methods are able to extract relevant information from company descriptions: the minimum semantic concentration portfolios have significantly lower volatility than portfolios constructed with randomly chosen weights. While no NLP method is able to claim absolute superiority over its peers, semantic fingerprinting appears the most consistent and robust performer, since BERT and GPT demonstrate not only their potential but also a caveat, as their performances are volatile even across very similar tasks.