Data-Centric Collaborative Innovation to Address Social Problems: The Experience and Lessons of "data for Social Good" Projects

Abstract

A collaborative approach involving public, private sector and citizen volunteers has been used to generate new ideas and design new solutions for government management and service. This study explores how collaborative innovation works in the field of data analysis by examining five data-centric collaborative projects initiated by a private company in Taiwan. These projects address important social issues such as domestic violence and poverty. Interviews of government and non-government actors revealed that these collaborative projects are suitable for experiments with new ideas and systems. The transformative learning process that occurred in the collaboration project illustrates the advantage of enlisting the support of people from different fields. The outcomes of collaboration brought new insights to predict the risk of domestic violence or other threats to children and adolescents, or the success rate of participants in job training programs. The diffusion of the collaborative model was also observed. However, there are several barriers to collaborative innovation and issues of equity, challenges to privacy and accountability, and the application of collaborative outcomes. This study brings us closer to understanding how collaborative innovation operates and can inform public managers on how to promote data-centric collaborative innovation in the future.