Publication database

This paper analyzes attempts to build user ideation communities aimed at inviting users to submit ideas and suggestions for future organizational actions. While earlier work has elaborated on the advantages of communities once they are created, our findings show that the ‘average’ organization struggles to build a vibrant community: most simply wither or die. We develop an argument about the importance of committing resources in the forms of (1) employees who submit suggestions to the community and (2) accepting suggestions from people in the community, and particularly suggestions from newcomers. Our findings suggest that creating communities often requires significant attention from the organizations seeking to develop them. However, our results are contingent upon the stage of the community, where we see different effects depending on whether the community has a history of accumulating suggestions or not. Our work has implications for scholars of open innovation by highlighting the importance of considering the innumerable failures, showing how focusing on communities that have reached a certain size can lead to misleading conclusions and specifying some conditions that explain why some are more successful than others.

With permission of the Academy of Management

We develop a model that relates self-control and conflict identification to cooperation patterns in social dilemmas. As predicted, we find in a laboratory public goods experiment a robust association between stronger self-control and higher levels of cooperation. This means that there is evidence for an impulse to be selfish and that cooperative behavior requires self-control effort. Free-riders differ from other contributor types only in their tendency not to have identified a self-control conflict in the first place.

Na tentativa de extrair informação da imensidão de dados hoje colhidos de fontes internas e externas, muitas empresas estão investindo pesado em ferramentas de TI e contratando cientistas de dados. A maioria, contudo, pena para conseguir um retorno digno do esforço. Isso porque estão abordando projetos de “big data” e analítica da mesma forma que abordam qualquer outro projeto de TI, sem perceber que são dois bichos completamente distintos.