Prosocial motivation and lending to the poor: evidence from an international crowdfunding platform

Luqun Xie, Yi Ding, Jiatao Li and Haifeng Xu ()
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Luqun Xie: Shanghai Jiao Tong University
Yi Ding: The University of Warwick
Jiatao Li: The Hong Kong University of Science and Technology
Haifeng Xu: Shanghai Jiao Tong University

Journal of International Business Studies, 2025, vol. 56, issue 4, No 7, 530-541

Abstract: Abstract Employing a prosocial perspective, this study examines how crowdfunders' prosocial motivation influences their lending decisions on international crowdfunding platforms, addressing the global challenge of poverty alleviation. We posit that prosocially motivated crowdfunders, concerned about economic inequality and others' well-being, are more likely to lend to poorer borrowers to minimize inequality and improve welfare. Analyzing a large dataset from Kiva.org with machine learning techniques, we find that higher prosocial motivation indeed leads to the lending choice of poorer borrowers across borders. However, cultural distance weakens this relationship by creating cognitive and emotional barriers, while crowdfunders' platform experience and women-owned businesses strengthen it. These findings highlight how digital platforms enable prosocial motivations to cross national borders in poverty-reduction efforts, while revealing barriers and enablers in cross-border lending. Our study contributes to the international business literature by introducing a prosocial perspective to digital platform research and advancing methodological approaches through machine learning. For practitioners, we suggest strategies to enhance prosocial crowdfunding platforms' effectiveness, including cultural bridge-building initiatives, leveraging experienced users as mentors, and highlighting women-owned businesses. Policymakers can use these insights to create frameworks maximizing prosocial crowdfunding's impact on poverty alleviation.

Keywords: Prosocial motivation; Poverty; Digital platform; Crowdfunding; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41267-024-00751-1

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