Cultural Differences and Geography as Determinants of Online Prosocial Lending

By Gordon Burtch, Anindya Ghose, Sunil Wattal. MIS Q., 2014

372 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/c4ee5d13669ee21d2e3ccca0b5f49101d245e2aa

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Citations in the corpus

Digital Finance and FinTech: current research and future research directions (2017, positive): “For lending-based crowdfunding, lenders prefer projects of borrowers that are culturally similar and geographically proximate (Burtch et al. 2012, 2014).”
Dynamics of Lending-Based Prosocial Crowdfunding: Using a Social Responsibility Lens (2018, background): “Research is particularly sparse regarding prosocial platforms (e.g. Allison et al. 2013; Allison et al. 2015; Burtch et al. 2014) and online peer-to-peer (P2P) lending sites (Iyer et al.”
Reward-based crowdfunding of entrepreneurial projects: the effect of local altruism and localized social capital on proponents’ success (2017, neutral): “Culture influences backers ’ willingness to contribute to crowdfunding projects (Burtch et al. 2014).”
Social identity and signalling success factors in online crowdfunding (2016, background): “However, in a study of Kiva, a peerto-peer lending platform, Burtch, Ghose, and Wattal (2014) conclude that as physical distance increases, the relevance of cultural distances decreases, a substitution effect important in such electronic markets.”
Crowdfunding and social capital: A systematic review using a dynamic perspective (2021, background): “Burtch et al. (2014) explore the relationship between cultural distance – measured by the distance between two country pairs in a two-dimensional culture map – and the number of lending actions between them.”
Is the crowd sensitive to distance?—how investment decisions differ by investor type (2017, neutral): “The four primary types of crowdfunding models are donation-based, debt-based, equity-based, and rewardbased (Agrawal et al. 2011; Bradford 2012; Schwienbacher and Larralde 2012; Belleflamme et al. 2013, 2014; Burtch et al. 2013, 2014, 2015; Mollick 2014; Ahlers et al. 2015; Schweizer and Zhou 2016).”
Can You Gig It? An Empirical Examination of the Gig Economy and Entrepreneurial Activity (2018, positive): “For each of the non-logged specifications, we employ a panel Poisson Pseudo-Maximum Likelihood (or Quasi-Maximum Likelihood) estimator (PPML or PQML) (Azoulay et al. 2010, Burtch et al. 2014, Greenwood and Gopal 2015, Simcoe 2007).”