Response to: "Managing the Crowd: Towards a Taxonomy of Crowdsourcing Processes." This paper proposes a generic taxonomical scheme for crowdsourcing process. This is intended to give better insights on deciding methodologies for crowdsourcing. The development of the classification is done by following: 1) Identification of four dimensions (preselection rules, mutual accessibility of participants, aggregation strategies, and rewarding policies) that determine the characteristics of crowdsourcing. The paper also determines types of crowdsourcing projects on each dimension, as a criteria for classification. 2) Using the taxonomical framework, the authors grouped 19 distinct process combinations from 46 examples. 3) They did cluster analysis to get five clusters of crowdsourcing processes. The result of analysis implies aggregation and rewards are more impacting on grouping than preselection and accessibility. Above all, I have learned the taxonomy that can be useful in development of crowdsourcing. Furthermore, I have learned the general process of establishing taxonomical framework and applying it. Establishing criteria for each dimension was the most interesting part of this paper. When there is a confliction between the authors' point of view and the previous works, this paper tried to reason, and I agreed with the reasons. For example, this paper "No reward" type in remuneration dimension, and this rebuffs previous works’ point of view that having "Voluntary Contribution" type in the dimension. I agree with the authors' idea, because I also think motivational factors are not easy to be measured quantitatively. Reading this paper, I was worried about the number of examples used for this research because it is too small. This is already mentioned by the authors because it could lead different cluster analysis. In my opinion, however, when there are more examples, this could also impact the establishment of dimensions and possible types of crowdsourcing in each dimension. What if there is a crowdsourcing project that has a striking characteristic that leads to adding new dimension? I think this could affect the basis of grouping and clustering. Additionally, there could be some dimensions that tightly related to others. Verifying the relationship or influence of one dimension on each dimension also could answer why some combinations are more frequently appear while some combinations are even never appear, or cause an establishment of a new dimensions to consider.