Jun 3, 2014 KV Kiva HQ
By Harry Pottash
Kiva’s Chicken-or-the-Egg Experiment in Team Building
It’s hard to stand stock still at a concert when the crowd around you is enthusiastically jumping around. Eventually your traitorous foot will start tapping out a beat and before you know it you’re proving Gloria Estefan right-- the rhythm is, in fact, going to get you.
 
Our peers influence our actions each and everyday in a similar manner, often times giving us that extra push we need to explore and experience life to the fullest.
 
Community is a driving factor in many of our lives and Kiva recognizes and appreciates this need for connection. On our site we have lending teams, which are a way for our lenders with mutual interests to band together and support each others’ lending endeavors. Interestingly, people who join teams tend to lend more often, but until recently we had no way to tell if that was a direct effect of participating in a team. 
 
We were burdened by the question: Do great teams engage lenders or do engaged lenders make great teams?
 
Luckily for us, Kiva’s unique and diverse lending team landscape caught the eye of University of Michigan Research Professor Yan Chen, who sought to answer this question for us. Professor Chen has been studying the motivations of lenders and effects of community on participation at Kiva since 2009. When we recently had the opportunity to welcome her to headquarters we jumped at the chance.
 
Once Professor Chen and our engineers got to talking the excitement was palpable, and before we knew it several new experiments were born.
 
After researching which teams lenders were most likely to join, Wei Ai, a graduate students working with Professor Chen, developed an algorithm to automatically suggest a lending team to join based on someone’s lending preferences and profile details. 
 
Eager to collaborate on scene, Wei and his colleague Yang Liu flew out to San Francisco and spent two weeks working side-by-side with our engineering team to fine-tune the recommendation engine.
 
Using Wei's algorithm, Kiva sent lending team suggestions to a group of lenders and observed what happened. An early look shows lenders identify strongly by geographic location and prefer teams that share their location; though it's still too early to say with any scientific certainty. Hopefully this test will help us understand what motivates lenders so that people can get the most out of their Kiva lending experience.
 
Researching and making improvements to our site is one of the innovative measures we’re taking to increase the outstanding impact our lending community has had around the world. If you have feedback on how we can improve your lending experience, please email contactus@kiva.org.


 


 

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Harry is a philosopher-programmer who has spent the last several years traveling the country and freelancing for clients in PHP and Javascript. He rangers at burning man, rock climbs, plays and designs board games, runs, brews beer, reads copious amounts of science fiction, and actively pursues both body and mind hacks.

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