As a new paradigm of online communities, microfinance sites such as Kiva.org have attracted much public attention. To understand lender motivations on Kiva, we classify the lenders' self-stated motivations into ten categories with human coders and machine learning based classifiers. We employ text classifiers using lexical features, along with social features based on lender activity information on Kiva, to predict the categories of lender motivation statements. Although the task appears to be much more challenging than traditional topic-based categorization, our classifiers can achieve high precision in most categories. Using the results of this classification along with Kiva teams information, we predict lending activity from lender motivation and team affiliations. Finally, we make design recommendations regarding Kiva practices which might increase pro-social lending.

Online question-answering services provide mechanisms for knowledge exchange by allowing users to ask and answer questions on a wide range of topics. A key question for designing such services is whether charging a price has an effect on answer quality. Two field experiments using one such service, Google Answers, offer conflicting answers to this question. To resolve this inconsistency, we re-analyze data from Harper et al. [5] and Chen et al. [2] to study the price effect in greater depth. Decomposing the price effect into two different levels yields results that reconcile those of the two field experiments. Specifically, we find that: (1) a higher price significantly increases the likelihood that a question receives an answer and (2) for questions that receive an answer, there is no significant price effect on answer quality. Additionally, we find that the rater background makes a difference in evaluating answer quality.

In many disciplines success depends onaccess to scarce resources, such as unique instruments. Researchon computer-supported cooperative work has contributed to the development of technologies, such as collaboratories, that broaden access to scarce scientific and engineering resources. While broader access is often applauded, little attention has been focused on the problem of equitable and efficient resource allocation in the face of increased demand created through collaboratory use. This paper, then, uses the framework of mechanism design to evaluate different resource allocation schemes (RAD, Vickery, and knapsack). Experimental results show that knapsack achieves a more equitable distribution of resources than RAD or Vickery, but that RAD and Vickery are both more efficient than knapsack. The findings highlight the need for systematic exploration of allocation mechanisms within collaboratories, where simple optimization (e.g., knapsack) is likely to produce a sub-optimal match of resources to needs. More generally, the findings illustrate the utility of experimental economic approaches in understanding issues that emerge in large-scale collaborations, such as entire scientific and engineering communities.