Engagement drivers in a lending marketplace : the case of Kiva
Abstract/Contents
- Abstract
- Online crowdfunding, facilitated by platforms such as Lending Club (with over $45 billion in loans originated by early 2019) and Kickstarter (over $4 billion in funds raised for 160,000 projects by early 2019), is an innovative application of Information Technology (IT) that uses peer-to-peer interactions to create an important channel for funding entrepreneurs, small businesses and consumers. As digitization has reduced the costs of interaction and trading in online marketplaces, crowdfunding platforms have grown dramatically over the past decade. Lending transactions alone consummated through crowdfunding reached $5.32 billion globally in 2018, funding over 26 million new loans, after facilitating $3.98 billion of borrowing in 2017. In this dissertation, we study data from Kiva, a philanthropic online crowdfunding marketplace that facilitates microcredit lending to borrowers in the developing world. Arguably the most visible new player in microfinance over the past decade, Kiva marries online technologies with the long-standing mission of microfinance to extend market access and fund availability to small entrepreneurs in the developing world. By aggregating lenders and small loans on its website, Kiva has successfully raised to date $1.3 billion in microcredit for 3.2 million borrowers, enabling microfinance institutions (MFIs) to draw upon ordinary web users as lenders. Kiva originates about $2.5 million in loans weekly. Funds are crowdsourced through Kiva's portal from web users in the developed world. This dissertation is dedicated to investigate the engagement drivers in online lending marketplaces from the lens of Kiva. In Chapter 1, we focus on the lender side of the market. In particular, we study how Kiva's lenders are influenced by rational, social and behavioral motivations and discuss how Kiva may exploit these effects when making user acquisition decisions and determining loan quality requirements. In Chapter 2, we focus on the practical problems faced by Kiva and its field partners to manage the loan requests and expedite the funding process. Utilizing the cutting-edge deep learning and natural language processing techniques, we develop a model to predict the funding speed of the loans and examine potential factors that have significant impacts the funding speed of loans. In Chapter 3, we look at Kiva as a platform from both sides of the marketplace and study network effects as an important engagement driver on Kiva. In particular, we divide Kiva's growth into two stages, a growth stage and a steady stage, and explore what role the network effects play in each stage.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Shen, Yuanyuan |
|---|---|
| Degree supervisor | Mendelson, Haim |
| Thesis advisor | Mendelson, Haim |
| Thesis advisor | Bayati, Mohsen |
| Thesis advisor | Weintraub, Gabriel |
| Degree committee member | Bayati, Mohsen |
| Degree committee member | Weintraub, Gabriel |
| Associated with | Stanford University, Graduate School of Business. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yuanyuan Shen. |
|---|---|
| Note | Submitted to the Graduate School of Business. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Yuanyuan Shen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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