Matching Returning Donors to Projects on Philanthropic Crowdfunding Platform

Management Science, Forthcoming

38 Pages Posted: 8 Nov 2018 Last revised: 28 Nov 2020

See all articles by Yicheng Song

Yicheng Song

University of Minnesota - Twin Cities - Carlson School of Management

Zhuoxin Li

Boston College - Carroll School of Management

Nachiketa Sahoo

Boston University - Questrom School of Business

Date Written: October 12, 2018

Abstract

We propose an approach to match returning donors to fundraising campaigns on philanthropic crowdfunding platforms. Our approach is based on a structural econometric model of utility-maximizing donors who can derive both altruistic (from the welfare of others) and egoistic (from personal motivations) utilities from donating--a unique feature of philanthropic giving. We estimate our model using a comprehensive dataset from DonorsChoose.org--the largest crowdfunding platform for K-12 education. We find that the proposed model more accurately identifies the projects that donors would like to donate to on their return in a future period, and how much they would donate, than popular personalized recommendation approaches in the literature. From the estimated model, we find that primarily egoistic factors motivate over two-thirds of the donations, but, over the course of the fundraising campaign both motivations play a symbiotic role: egoistic motivations drive the funding in the early stages of a campaign when the viability of the project is still unclear, whereas altruistic motivations help reach the funding goal in the later stages. Finally, we design a recommendation policy using the proposed model to maximize the total funding each week considering the needs of all projects and the heterogeneous budgets and preferences of donors. We estimate that over the last fourteen weeks of the data period, such a policy would have raised 2.5% more donation, provided 9% more funding to the projects by allocating them to more viable projects, funded 17% more projects, and provided 15% more utility to the donors from the donations than the current system. Counterintuitively, we find that the policy that maximizes total funding each week leads to higher utility for the donors over time than a policy that maximizes donors' total utility each week. The reason is that the funding-maximizing policy focuses donations on more viable projects, leading to more funded projects, and, ultimately, higher realized donors' utility.

Keywords: Recommender Systems, Crowdfunding Platforms, Online Philanthropy, Structural Econometric Model, Consideration Set, Revenue Optimization

JEL Classification: D64, D15

Suggested Citation

Song, Yicheng and Li, Zhuoxin and Sahoo, Nachiketa, Matching Returning Donors to Projects on Philanthropic Crowdfunding Platform (October 12, 2018). Management Science, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3280276

Yicheng Song

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

HOME PAGE: http://people.bu.edu/ycsong/

Zhuoxin Li

Boston College - Carroll School of Management ( email )

140 Commonwealth Ave
Chestnut Hill, MA Massachusetts 02467
United States

Nachiketa Sahoo (Contact Author)

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

HOME PAGE: http://people.bu.edu/nachi/

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