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In this paper, we investigate how the institutional setting affects the diffusion of green crowdfunding campaigns across countries. To this aim, we develop and test two competing hypotheses about the association between country environmental sustainability orientation and the diffusion of green campaigns. To identify green campaigns, we develop an original machinelearning algorithm. We apply this algorithm to the population of 48,598 campaigns presented on Kickstarter between July 1, 2009 and July 1, 2012. By means of econometric estimates, we show that green campaigns differ from others along several dimensions and are more diffused in countries with a limited environmental sustainability orientation. Implications for research, practice, and policy are discussed.

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Decision trees have been widely recognized as one of the most effective techniques for classification in the data mining context, particularly when dealing with business oriented applications, such as those arising in the frame of customer relationship management. We propose an algorithm for generating decision trees in which multivariate splitting rules are based on the new concept of discrete support vector machines. By this