Hey folks,
This month we're holding a special edition of the Research and Data showcase https://www.mediawiki.org/wiki/Analytics/Research_and_Data/Showcase. We've invited Dr. Yan Chen, Professor from the UMuch iSchool to present her work studying community dynamics with Kiva (micro-lending platform) and what her results might imply for Wikimedia's sites. To take advantage of her travel schedule, we'll be holding the event on *Friday** November 14 at 11.30 PST (UTC-8) *rather than the usually 3rd Wednesday. The event will be live streamed and recorded as usual. You can join the conversation via IRC on freenode.net in the the #wikimedia-research channel.
We look forward to seeing you there,
-Aaron
*Does Team Competition Increase Pro-Social Lending? Evidence from Online Microfinance.*By Yan Chen http://yanchen.people.si.umich.edu/In the first half of the talk, I will present our empirical analysis of the effects of team competition on pro-social lending activity on Kiva.org, the first microlending website to match lenders with entrepreneurs in developing countries. Using naturally occurring field data, we find that lenders who join teams contribute 1.2 more loans per month than those who do not. Furthermore, teams differ in activity levels. To investigate this heterogeneity, we run a field experiment by posting forum messages. Compared to the control, we find that lenders from inactive teams make significantly more loans when exposed to a goal-setting message and that team coordination increases the magnitude of this effect.In the second part of the talk, I will discuss a randomized field experiment we did in May 2014, when we recommend teams to lenders on Kiva. We find that lenders are more likely to join teams in their local area. However, after joining teams, those who join popular teams (on the leaderboard) are more active in lending.
I saw and experienced first-hand the motivational effect of Kiva teams. I can see a lot of positives for Wikipedia: increased contributions given competition (if the leader board is constructed in a way not to demotivate the lower performing teams) and perhaps the more experienced editors will help the newer editors (or at least give them "air cover")
Sent from my iPad
On 14 Nov 2014, at 2:51 am, Aaron Halfaker ahalfaker@wikimedia.org wrote:
Hey folks,
This month we're holding a special edition of the Research and Data showcase. We've invited Dr. Yan Chen, Professor from the UMuch iSchool to present her work studying community dynamics with Kiva (micro-lending platform) and what her results might imply for Wikimedia's sites. To take advantage of her travel schedule, we'll be holding the event on Friday November 14 at 11.30 PST (UTC-8) rather than the usually 3rd Wednesday. The event will be live streamed and recorded as usual. You can join the conversation via IRC on freenode.net in the the #wikimedia-research channel.
We look forward to seeing you there,
-Aaron
Does Team Competition Increase Pro-Social Lending? Evidence from Online Microfinance. By Yan Chen In the first half of the talk, I will present our empirical analysis of the effects of team competition on pro-social lending activity on Kiva.org, the first microlending website to match lenders with entrepreneurs in developing countries. Using naturally occurring field data, we find that lenders who join teams contribute 1.2 more loans per month than those who do not. Furthermore, teams differ in activity levels. To investigate this heterogeneity, we run a field experiment by posting forum messages. Compared to the control, we find that lenders from inactive teams make significantly more loans when exposed to a goal-setting message and that team coordination increases the magnitude of this effect. In the second part of the talk, I will discuss a randomized field experiment we did in May 2014, when we recommend teams to lenders on Kiva. We find that lenders are more likely to join teams in their local area. However, after joining teams, those who join popular teams (on the leaderboard) are more active in lending. _______________________________________________ Wiki-research-l mailing list Wiki-research-l@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
Hoi, I hope it will be inspiring.. We do know that challenges work. Mrs Chen describes that involvement has people contribute more. It had me thinking and I wrote this blog post. [1]. To prepare for follow up activities in both WIkidata and Wikidata, I added the winners for "Thanks for the book", I added dates for the last 5 and I added the university the 2014 winner went to and added fellow alumni in Wikidata.
We can pose challenges, I did that in my blogpost. I know of many other examples where we can engage our community to do better by bringing the challenge to them. Writing the new article that will be most read in the next month for your language is one. This notion that by posing targeted challenges is nothing new. What will be new is when this becomes a best practice. When we make the data we have WORK for us. Thanks, GerardM
[1] http://ultimategerardm.blogspot.nl/2014/11/wikidata-thanks-for-book-award.ht...
On 13 November 2014 17:51, Aaron Halfaker ahalfaker@wikimedia.org wrote:
Hey folks,
This month we're holding a special edition of the Research and Data showcase https://www.mediawiki.org/wiki/Analytics/Research_and_Data/Showcase. We've invited Dr. Yan Chen, Professor from the UMuch iSchool to present her work studying community dynamics with Kiva (micro-lending platform) and what her results might imply for Wikimedia's sites. To take advantage of her travel schedule, we'll be holding the event on *Friday** November 14 at 11.30 PST (UTC-8) *rather than the usually 3rd Wednesday. The event will be live streamed and recorded as usual. You can join the conversation via IRC on freenode.net in the the #wikimedia-research channel.
We look forward to seeing you there,
-Aaron
*Does Team Competition Increase Pro-Social Lending? Evidence from Online Microfinance.*By Yan Chen http://yanchen.people.si.umich.edu/In the first half of the talk, I will present our empirical analysis of the effects of team competition on pro-social lending activity on Kiva.org, the first microlending website to match lenders with entrepreneurs in developing countries. Using naturally occurring field data, we find that lenders who join teams contribute 1.2 more loans per month than those who do not. Furthermore, teams differ in activity levels. To investigate this heterogeneity, we run a field experiment by posting forum messages. Compared to the control, we find that lenders from inactive teams make significantly more loans when exposed to a goal-setting message and that team coordination increases the magnitude of this effect.In the second part of the talk, I will discuss a randomized field experiment we did in May 2014, when we recommend teams to lenders on Kiva. We find that lenders are more likely to join teams in their local area. However, after joining teams, those who join popular teams (on the leaderboard) are more active in lending.
Wiki-research-l mailing list Wiki-research-l@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
I'm stoked that you guys also see the potential here. I look forward to continuing this discussion with Yan as her team looks to Wikipedia as a space to try out similar methods.
I hope that I'll see you guys in #wikimedia-research http://webchat.freenode.net/?channels=#wikimedia-research. :)
-Aaron
On Fri, Nov 14, 2014 at 2:25 AM, Gerard Meijssen gerard.meijssen@gmail.com wrote:
Hoi, I hope it will be inspiring.. We do know that challenges work. Mrs Chen describes that involvement has people contribute more. It had me thinking and I wrote this blog post. [1]. To prepare for follow up activities in both WIkidata and Wikidata, I added the winners for "Thanks for the book", I added dates for the last 5 and I added the university the 2014 winner went to and added fellow alumni in Wikidata.
We can pose challenges, I did that in my blogpost. I know of many other examples where we can engage our community to do better by bringing the challenge to them. Writing the new article that will be most read in the next month for your language is one. This notion that by posing targeted challenges is nothing new. What will be new is when this becomes a best practice. When we make the data we have WORK for us. Thanks, GerardM
[1] http://ultimategerardm.blogspot.nl/2014/11/wikidata-thanks-for-book-award.ht...
On 13 November 2014 17:51, Aaron Halfaker ahalfaker@wikimedia.org wrote:
Hey folks,
This month we're holding a special edition of the Research and Data showcase https://www.mediawiki.org/wiki/Analytics/Research_and_Data/Showcase. We've invited Dr. Yan Chen, Professor from the UMuch iSchool to present her work studying community dynamics with Kiva (micro-lending platform) and what her results might imply for Wikimedia's sites. To take advantage of her travel schedule, we'll be holding the event on *Friday** November 14 at 11.30 PST (UTC-8) *rather than the usually 3rd Wednesday. The event will be live streamed and recorded as usual. You can join the conversation via IRC on freenode.net in the the #wikimedia-research channel.
We look forward to seeing you there,
-Aaron
*Does Team Competition Increase Pro-Social Lending? Evidence from Online Microfinance.*By Yan Chen http://yanchen.people.si.umich.edu/In the first half of the talk, I will present our empirical analysis of the effects of team competition on pro-social lending activity on Kiva.org, the first microlending website to match lenders with entrepreneurs in developing countries. Using naturally occurring field data, we find that lenders who join teams contribute 1.2 more loans per month than those who do not. Furthermore, teams differ in activity levels. To investigate this heterogeneity, we run a field experiment by posting forum messages. Compared to the control, we find that lenders from inactive teams make significantly more loans when exposed to a goal-setting message and that team coordination increases the magnitude of this effect.In the second part of the talk, I will discuss a randomized field experiment we did in May 2014, when we recommend teams to lenders on Kiva. We find that lenders are more likely to join teams in their local area. However, after joining teams, those who join popular teams (on the leaderboard) are more active in lending.
Wiki-research-l mailing list Wiki-research-l@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
Wiki-research-l mailing list Wiki-research-l@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
wiki-research-l@lists.wikimedia.org