RRBM Dare to Care Dissertation Scholarships 2025 Winners

Many organizations are committed to addressing the global challenge of inequality. Despite this, a 2022 United Nations report to the Human Rights Council declared a worldwide crisis surrounding the underutilization of aid, which, “significantly undermines the effectiveness of social protection in reducing poverty and inequalities.” This highlights a pressing need for organizations to understand how to improve their equality-enhancing practices.

This dissertation investigates a descriptive-prescriptive paradox – in which widespread practice may not be the most effective – in organizational attempts to address inequality. Namely, does the common practice of emphasizing autonomy in the processes of giving and receiving aid undermine individuals’ motives to advance broader social welfare?

This dissertation leverages full-cycle research in partnership with organizations to build and test novel theory around the consequences of focusing on the personal control and independence that an agent can derive from giving or receiving aid. The first chapter challenges the common belief that to increase donations, charities should let donors choose how their donation will be used. Using experiments in field and online settings, this chapter distinguishes between the psychology of deciding what to give versus who to help. Unlike the former, which is a choice among objects, the latter choice among human lives instills a feeling of emotional discomfort, which reduces donation behavior. This reveals one downside to prioritizing donors’ autonomy. The second chapter studies the receipt of government benefits using qualitative (interviews, focus groups) and quantitative (archival data, surveys, field experiment) methods. While the most common practice of promoting government benefits emphasizes how aid helps recipients independently fulfill their need for essential resources, the most effective approach instead describes how aid can advance recipients’ goals to support their communities. Interdependence, rather than autonomy, emerges as an unexpected motive for aid recipients.

Organizational practices that emphasize autonomy in the processes of giving and receiving aid may undermine individuals’ social motives and stunt related behaviors. By leveraging interdisciplinary literatures and methods, and conducting stakeholder engagement in every research stage, this dissertation meets all seven principles of responsible research. Collectively, this research offers novel discoveries and actionable solutions for organizations committed to addressing inequality.

Persistent inequalities within organizations raise fundamental questions about the role of leadership representation in shaping equitable outcomes. In this project, I ask: When and how does the presence of Black managers help reduce—or inadvertently widen—wage disparities between Black and White employees? Motivated by the growing backlash against DEI (diversity, equity, and inclusion) efforts and the enduring racial wage gap, this research investigates whether increased racial representation in management leads to fairer outcomes, or whether underlying organizational dynamics continue to sustain inequalities despite surface-level diversity. Brazil offers a critical setting for this investigation: a country marked by profound racial disparities despite its majority non-White population, where Black workers remain underrepresented in leadership and concentrated in lower-wage roles.

This project addresses racial injustice by moving beyond symbolic diversity to assess whether and when representation disrupts inequalities or reproduces them under a different guise. Its findings will have important implications for organizational policies, helping companies design strategies that create more equitable workplaces. It aims to benefit historically marginalized employees, inform corporate DEI efforts, and contribute to broader societal debates around racial justice and economic inequality.

Methodologically, the project primarily relies on quantitative panel data analysis to examine how racial managerial representation affects wage disparities within organizations. A complementary qualitative approach is employed to deepen understanding of the mechanisms behind these patterns, drawing on interviews with managers and employees. This combination captures both large-scale trends and the organizational processes shaping racial inequalities in wage outcomes.

The project contributes to the literature on organizational inequalities and DEI by reframing leadership diversity around substantive outcomes rather than representation alone. It offers insights into when minority leadership fosters—or fails to foster—equity for marginalized groups. Rather than assuming diversity at the top automatically improves outcomes, this study critically examines the processes through which change occurs.

Ultimately, this research generates actionable ideas for firms and policymakers, providing evidence-based recommendations to advance racial equity. It aligns closely with Responsible Research in Business and Management (RRBM) principles, aspiring to produce credible, useful, and transformative knowledge at the intersection of leadership, inequality, and social justice.

Globally, 1.4 billion people still live in “banking branch deserts”¹ – areas with little or no access to formal financial services. In these markets, local shopkeepers increasingly act as last-mile banking agents, providing essential services such as remittances, deposits, and bill payments. My dissertation investigates how general-purpose AI models, such as large language models (LLMs), can promote financial inclusion by sustaining these merchant networks, which often serve as the only accessible financial touchpoints for underserved communities.

To explore this, I conducted a randomized field experiment with over 30,000 merchants in India, delivering AI-personalized financial product guidance via WhatsApp. I examine both the promise and limitations of AI personalization in data-scarce markets. While general-purpose AI offers a cost-effective approach for firms to deliver personalized guide at scale, models trained primarily on public web data risk reinforcing existing inequalities when structural data frictions are ignored. I develop a theoretical framework explaining how these frictions limit algorithmic effectiveness and test interventions to mitigate bias and improve its effectiveness across diverse merchant segments.

My dissertation directly addresses economic inequality by democratizing access to advanced AI tools for small businesses serving marginalized populations. The findings have significant implications for the estimated 700 million people living in extreme poverty who depend on these merchant networks for financial access². The study contributes new insights to the literature on AI personalization, B2B relationship management, algorithmic bias, and financial inclusion in low-data environments, providing novel field-based evidence that LLM-powered personalization can improve merchant engagement and business sales in B2B financial services at scale. My research exemplifies responsible scholarship by addressing real-world financial inclusion challenges, employing rigorous experimental methods, maintaining deep stakeholder engagement through continuous collaboration with industry partners, and creating clear pathways from research to practice.

¹ World Bank Blog, The Global Findex Database 2021 identifies opportunities for increasing financial inclusion

² World Bank Group, Poverty, Prosperity, and Planet Report