Literature DB >> 27974610

Recommending teams promotes prosocial lending in online microfinance.

Wei Ai1, Roy Chen2, Yan Chen3,4, Qiaozhu Mei1, Webb Phillips5.   

Abstract

This paper reports the results of a large-scale field experiment designed to test the hypothesis that group membership can increase participation and prosocial lending for an online crowdlending community, Kiva. The experiment uses variations on a simple email manipulation to encourage Kiva members to join a lending team, testing which types of team recommendation emails are most likely to get members to join teams as well as the subsequent impact on lending. We find that emails do increase the likelihood that a lender joins a team, and that joining a team increases lending in a short window (1 wk) following our intervention. The impact on lending is large relative to median lender lifetime loans. We also find that lenders are more likely to join teams recommended based on location similarity rather than team status. Our results suggest team recommendation can be an effective behavioral mechanism to increase prosocial lending.

Keywords:  charitable giving; field experiment; microfinance; recommender systems; social identity

Year:  2016        PMID: 27974610      PMCID: PMC5206538          DOI: 10.1073/pnas.1606085113

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  5 in total

Review 1.  Community structure in social and biological networks.

Authors:  M Girvan; M E J Newman
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-11       Impact factor: 11.205

2.  Testing for altruism and social pressure in charitable giving.

Authors:  Stefano DellaVigna; John A List; Ulrike Malmendier
Journal:  Q J Econ       Date:  2012

3.  Business culture and dishonesty in the banking industry.

Authors:  Alain Cohn; Ernst Fehr; Michel André Maréchal
Journal:  Nature       Date:  2014-11-19       Impact factor: 49.962

Review 4.  Machine learning: Trends, perspectives, and prospects.

Authors:  M I Jordan; T M Mitchell
Journal:  Science       Date:  2015-07-17       Impact factor: 47.728

5.  Prediction Policy Problems.

Authors:  Jon Kleinberg; Jens Ludwig; Sendhil Mullainathan; Ziad Obermeyer
Journal:  Am Econ Rev       Date:  2015-05
  5 in total
  1 in total

1.  Resource sharing in technologically defined social networks.

Authors:  Hirokazu Shirado; George Iosifidis; Leandros Tassiulas; Nicholas A Christakis
Journal:  Nat Commun       Date:  2019-03-06       Impact factor: 14.919

  1 in total

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