This research proposal examines why eligible and informed veteran entrepreneurs do not consistently participate in the Small Business Administration 7(a) loan program, asking how perceived program design and institutional decoupling shape institutional trust and participation intentions. Grounded in institutional theory, it plans a qualitative study using semi-structured interviews with approximately 30 veteran entrepreneurs in the Tampa, Florida area, supported by descriptive firm data and thematic coding. Empirical findings are not yet available; the work aims to inform how federal capital access programs are structured and communicated to improve engagement.

This doctoral study shifts the focus of financial inclusion research from access to outcomes, asking how digital financial usage is associated with financial wellness among unbanked and underbanked U.S. households and how financial literacy and institutional trust shape this relationship. Integrating Financial Capability Theory and the Technology Acceptance Model, it tests a moderated mediation model using nationally representative microdata from the 2021 and 2023 FDIC National Survey of Unbanked and Underbanked Households, with financial literacy as mediator and institutional trust, income and education as moderators. The findings are intended to inform fintech product design, financial institutions, CDFIs and policymakers seeking outcome-based measures of financial inclusion.

Air pollution in cities has been a major problem, and road transport is a major contributor to nitrogen dioxide (NO₂) and particulate matter (PM). LEZs have become a mainstream regulatory intervention, especially in Europe, but the effectiveness of these strategies is limited by their fixed geographical boundaries, slow feedback and their poor sensitivity to evolving urban dynamics. New developments in Digital Twin (DT) technologies provide a platform to approach adaptive, real-time governance of urban air quality through continuous sensing, predictive modelling and data-driven decision support. The present paper has provided a systematic analysis of the technological and Governance underpinnings needed to facilitate DT-driven LEZs. It investigates the purpose of sensing infrastructures and data structures, and machine learning, computer vision and algorithmic traffic models such as microscopic car-following models that convert raw urban data into useful environmental intelligence. In the review, these technologies are also placed in the larger framework of urban data governance, smart-city preparedness, and adherence to the European General Data Protection Regulation (GDPR). The main strength of this paper is the formulation of an integrated framework that connects perception, mobility modelling, emissions estimation, and policy enforcement in a consistent set of DT-supported LEZ ecosystem. The synthesis of technological innovations and institutional and legal aspects guides the paper to offer advice on the implementation of equitable, scalable, and empirically supported interventions to address air quality in European cities and outline important directions of future cross-city benchmarking and standardisation in the research field.

Despite sustained global investment in financial literacy initiatives, their impact on micro-business performance remains inconsistent and insufficiently explained, particularly in emerging markets. Existing approaches largely assume that financial knowledge and skills translate linearly into improved decision-making and firm outcomes. However, this assumption overlooks the complex, uncertain, and context-dependent environments within which micro-entrepreneurs operate. This paper develops a conceptual framework that advances the notion of financial wisdom as a higher-order construct explaining how individuals effectively translate financial knowledge into action. Financial wisdom is proposed as the capacity to decide well under conditions of uncertainty, constraint, and complexity — the quality of financial judgment that knowledge alone can inform but never replace. Formally defined, financial wisdom is the integrative capacity to apply financial knowledge, attitudes, and behaviors through context-sensitive judgment, experiential learning, and adaptive reasoning in ways that produce effective financial decisions under real-world conditions. Building on insights from behavioral decision theory, bounded rationality, ecological rationality, and practice-based perspectives, the paper positions financial wisdom as the critical mechanism linking financial literacy to micro-business performance. The framework contributes to theory by challenging cognition-centric views of financial capability and by offering a theoretically grounded explanation for persistent empirical inconsistencies in the financial literacy–performance relationship. Contextual factors are theorised as a four-dimensional moderating construct; encompassing resource constraints, institutional voids and market informality, socio-cultural norms and embeddedness, and environmental volatility — that shapes both the development of financial wisdom and its translation into performance outcomes. From a practitioner perspective, the framework reframes financial capability development by emphasising decision-making competence over knowledge acquisition alone. The paper concludes by outlining a set of propositions and a research agenda for empirically examining financial wisdom and its implications for improving micro-business outcomes in resource-constrained environments. The paper argues that if financial capability development is to produce lasting impact in emerging markets, the cultivated wisdom of the practitioner must be placed at the centre of both theory and intervention design.

This doctoral study examines how artificial intelligence is translated from isolated pilot projects into scalable organisational innovation capability within the Spanish pharmaceutical R&D ecosystem, and which organisational, governance and regulatory factors enable or constrain that process. Adopting a socio-technical systems perspective combined with general-purpose technology, dynamic capabilities and AI governance literature, it uses a qualitative design based on semi-structured interviews with 20–30 senior stakeholders from pharmaceutical companies, regulators, industry associations and technology providers. Preliminary observations point to data fragmentation, organisational silos, regulatory uncertainty and limited trust as barriers, and to governance frameworks and integration into decision processes as enablers of scaling.

Prior research has documented gender- and ethnicity-associated pricing differentials in art auction markets, but has generally treated these dimensions as independent and additive. This paper applies intersectionality theory to test that assumption directly. Drawing on 68,672 auction transactions at Christie's, Sotheby's, and Phillips in New York from 2015 through 2024, representing approximately $31.7 billion in hammer value across 2,631 unique artists, we estimate hedonic pricing models with explicit gender-by-ethnicity interaction terms, race-disaggregated specifications, difference-in-differences models anchored to the #MeToo (2017) and Black Lives Matter (2020) movements, and superstar sensitivity tests using cumulative-revenue thresholds. Four findings emerge. First, the relationship between demographics and price is not additive: a Female × Ethnic Minority interaction of +0.443 (p < 0.001) means the net price gap for women of color relative to white male artists is statistically indistinguishable from zero, even though both single-axis main effects are large and negative. Second, 27 superstar artists account for 61.3% of total revenue and are 96.2% male and 98.3% white; their exclusion attenuates or reverses the single-axis main effects while the intersectional interaction remains stable. Third, both cultural movements are associated with statistically significant narrowing of demographic price gaps in the immediate post-event period. Fourth, these gains were not equally distributed: women of color received no statistically measurable price improvement following either movement, a pattern consistent with the intersectional invisibility framework. The paper contributes a large-scale empirical demonstration that intersectional non-additivity holds in a major cultural market, establishes superstar sensitivity testing as a necessary protocol in markets with high revenue concentration, and offers practitioner-relevant guidance for collectors, auction specialists, gallery directors, and museum acquisition officers operating in markets where aggregate diversity statistics may obscure the experience of multiply-marginalized groups.

This study examines how student and experienced job seekers describe and interpret their recruitment experiences. Although these two groups’ recruitment processes differ structurally and temporally, little research has compared how they experience the job search process. Drawing on affective events theory, this study examines whether these differences translate into distinct job-search experiences. Using a qualitative research design, 66 first-person job-search narratives were passively collected from Reddit and analyzed using a reflexive, comparative thematic analysis. Findings reveal that, despite differences in recruitment processes, both student and experienced job seekers report similar experiences. Across both groups, job seekers describe a demanding, often discouraging job-search process resulting in limited employer communication. In response, job seekers adopt adaptive strategies such as applying en-masse to jobs and rely on technological tools to manage the process. These adaptation strategies, in turn, contribute to higher application volumes and reinforce the conditions that shape the job market. Practical implications suggest that organizations should prioritize clear and timely communication throughout the recruitment process to improve the candidate experience and reduce unintended consequences of current hiring practices.

Large-scale capital programs frequently underperform against cost, schedule, and risk expectations despite advances in technical planning and project controls. While traditional explanations emphasize structural and operational challenges, this research examines the behavioral drivers that influence decision-making in these environments. Grounded in behavioral economics, this study explores how cognitive biases—specifically optimism bias, loss aversion, and sunk cost fallacy—shape decision-making under conditions of uncertainty, complexity, and organizational pressure. Drawing on a practice-based case study of the City of Atlanta, the research identifies recurring decision patterns and examines their relevance in large-scale capital program environments. Findings demonstrate that decision-making distortions are systematic rather than incidental, influencing both the timing and quality of decisions across program phases. The study introduces a Behaviorally Informed Decision Framework that illustrates how bias manifests at key decision points and provides practical mechanisms for improving decision discipline. This research contributes to engaged management scholarship by bridging behavioral theory and executive practice, offering a transferable framework for improving decision quality and performance outcomes in complex, capital-intensive environments.

The banking industry is ripe for disruption (Gomber, Kauffman, Parker, & Weber, 2018). A primary threat to the banking model is industry leadership not adapting to the paradigm shift (Mackintosh, 2014) in consumer and employee demands towards a collective good and businesses that make a difference (Bhattacharya & Sen, 2004). Only a few studies have focused on values-based banking leadership. Thirty-four CEOs in seven countries were interviewed for this study. The findings are 1) positive organizational development, 2) doing well by doing good and creating a 3) vibrant ecosystem. The research contribution includes a framework for CEOs to transition to a values-based financial institution.

Why are skilled migrants at risk of precarity, despite their competence being in demand? We investigate this at the level of organisations and how they evaluate skilled migrants’ labour worth. Drawing on a 36-month qualitative investigation, our study explores how a HR initiative for recruiting skilled immigrants assesses their competences. With Lamont’s sociology of evaluation, we approach recruitment as a social process of recognition of migrants’ professional worth and identify four instances of recognition gaps: the worth of immigrants’ labour, the worth of their academic skills, their expected social position, and the value attached to the cultural diversity they bring. This low social valuation can be linked to precarious positions. Our contribution to research on skilled migrants’ precarity illuminates how organisational process play a role in their market worth devaluation. Contribution to HR inclusive recruitment literature stresses how the evaluative design of the process, rather than individual bias, reproduces discrimination.