Start Date

10-12-2017 12:00 AM

Description

Online pro-social crowd funding platforms facilitate borrowers from economically weaker locations to access financial supports from lenders globally. However, geographical and cultural distances are still prevailing on these platforms. Loan transactions are more likely to occur between lenders and borrowers in the same country and lenders prefer to fund culturally similar and geographically proximate borrowers. Since, it prevents the borrowers from underrepresented populations to access sufficient loan funding, how to overcome this bias needs to be examined. This study aims to investigate how lenders’ lending motivations affect their lending decisions on the loan requests regarding the regional and cultural differences between lenders and borrowers. We contextualize our study at Kiva.org and lending transactions are retrieved via its API. Lenders’ motivations are mined and categorized based on lenders’ self-stated descriptions. This study potentially contributes to enrich the literature of cross-cultural studies in social lending by studying the effects of lending motivation.

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Dec 10th, 12:00 AM

Lending Motivation Meets Home and Cultural Bias: A Study on Kiva

Online pro-social crowd funding platforms facilitate borrowers from economically weaker locations to access financial supports from lenders globally. However, geographical and cultural distances are still prevailing on these platforms. Loan transactions are more likely to occur between lenders and borrowers in the same country and lenders prefer to fund culturally similar and geographically proximate borrowers. Since, it prevents the borrowers from underrepresented populations to access sufficient loan funding, how to overcome this bias needs to be examined. This study aims to investigate how lenders’ lending motivations affect their lending decisions on the loan requests regarding the regional and cultural differences between lenders and borrowers. We contextualize our study at Kiva.org and lending transactions are retrieved via its API. Lenders’ motivations are mined and categorized based on lenders’ self-stated descriptions. This study potentially contributes to enrich the literature of cross-cultural studies in social lending by studying the effects of lending motivation.