“Sociologists have long been concerned with how to build the good society” (ASA, 2012). Altruism is one of the aspects of the answer to this question. Theoretical development and empirical research to study the relationship between altruism, social solidarity and the characteristics of social structures and of cultural systems is valued of great importance for science, policy makers and public. The ASA (2012) considers philanthropy to be a altruistic phenomenon.
A good case to study the relationship between altruism, social solidarity and the characteristics of social structures and of cultural systems, manifesting as philanthropic behavior in social structures, is Kiva.org. Kiva.org is an online lending platform (website) on which philanthropic micro credit transactions take place. Individuals from developed countries can loan money to individuals in developing countries (Flannery, 2007). This happens on a large scale. Since the founding of Kiva.org in 2005, more than 945.000 lenders* have together lent more than $443 million. Everyone can join Kiva.org and loan micro credit with the minimum amount being $25. A complete loan consist of multiple of such micro credit transactions by lenders on Kiva.org. Loan requests are posted on Kiva.org by Kiva’s field partners. About 200 microfinance institution (MFIs) are field partners of Kiva. These MFIs are spread over 68 countries. They facilitate the request for transactions on Kiva.org by posting a comprehensive description of the loan goal to Kiva.org. This description includes the borrower’s picture and description a short and long description of the loan goal, a total loan amount with deadline, the average amount of interest charged by the MFI and information about the scheduling of repayments. Four reasons substantiate why Kiva.org is a good case to study philanthropic behavior.
The first reasons is based on the nature of the transactions on Kiva.org. These are philanthropic transactions, because a lender can not charge interest on the loan. Therefore, the lender can not profit from lending on Kiva.org. Moreover, the worst case scenario would be a loss, when the repayment does not take place. This risk is that of the lender.
The second reason is the launch of a feature that allows the forming of social structures on Kiva.org, by ways of lending teams. In August 2008, Kiva.org launched it’s feature to form lending teams. In lending teams users (i.e. lenders) can group up under a shared name and motivation. Kiva.org users can join multiple teams. When a group is created, a description of the group and a common motivation for the group is specified. Furthermore, a group can be categorised. For reasons of privacy, groups can be listed as ‘open’ or ‘closed’. Activities that take place within the group can be viewed by team members. Memberships for closed groups are managed by the team captain(s). Membership for open groups is uncontrolled, thus making the activities that take place within open groups more accessible. Activities within teams include mutations in the list of team members, loans counted towards the lending team, and messages posted on a message board. Group members remain individual lenders but are given the choice to count their lending actions towards a joined team of choice. When a lender decides to count his lending action towards a team, his/her lending activity, including her profile information, is temporarily highlighted on the team’s page. Team memberships can be viewed for all lenders.
The third reasons is the accessibility of the transaction data of lending on Kiva.org. These data from Kiva.org’s database are largely publicly accessible. At the time of this writing, no other microfinancial institutions were known to the researcher, that publicize data on this scale. The database includes comprehensive details about transactions on the platform. Kiva.org’s database can be accessed through an Application Programming Interface (API). An API is a collection of methods that can programmatically be called to return data. In Kiva.org’s case, these methods return data from Kiva.orgs database. The dataset that can be constructed based on these data offers possibilities to test explanations for philanthropic behavior.
The fourth and final reason is the apparent theoretical relevance of research towards philanthropic behavior and the characteristics of social structures on Kiva.org given by existing academic literature on the subject of Kiva.org. Previous research towards Kiva.org includes descriptive research towards Kiva.orgs place in the (micro)financing spectrum (Cloninger, Cook, Laidlaw, O’Connor & Simons, 2006), the business models employed by niew microfinance institutions (Bruett, 2007; Carrick & Santos, 2009), Kiva.orgs place in the development of development aid (Kharas, 2009), Web 2.0 platforms aimed towards development aid (Carlman, 2009) and solidarity as mechanism to stimulate collaboration (Hartley, 2010). Furthermore, empirical research is done towards the comparison between private development aid and official development aid (Desai & Kharas, 2009), how publicity through traditional and social media influence lending frequency on Kiva.org (Stephen & Galak, 2009), how social similarities between burrower and lender relate to lending preferences (Galak, Small & Stephen, 2011), how Kiva.org’s success can be explained (Jardina, 2011), if burrowers groups and the size of these groups influence the repayment schedule (Jefferson, 2011), which factors substantially influence lending behavior (Wallingford, 2011) and the effect of income thresholds of burrowers on lending behavior (Hassoun & Lubchenco, 2012).
Finally, exploratory empirical research is done by Liu, Chen, Chen, Mei and Salib (2012) towards individual lender’s motivations on Kiva.org. This research is of particular interest of this thesis, because it gives rise to question which this thesis aims to answer. Explicitly, Liu et al. (2012, p. 8) point out that no systematic research is done towards the effect of team memberships on lending behavior. Their study does not suffice in this area, because they cannot rule out the possibility that lenders that join lending teams tend to lend more in the first place (Liu et al., 2012).