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- Genre: Doctoral Dissertation
- Creators: Liu, Huan
- Creators: Mossberger, Karen
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
Public organizations have been interested in tapping into the creativity and passion of the public through the use of open innovation, which emphasizes bottom-up ideation and collaboration. A challenge for organizational adoption of open innovation is that the quick-start, bottom-up, iterative nature of open innovation does not integrate easily into the hierarchical, stability-oriented structure of most organizations. In order to realize the potential of open innovation, organizations must be willing to change the way they operate. This dissertation is a case study of how Arizona State University (ASU), has adapted its organizational structure and created unique programming to incorporate open innovation. ASU has made innovation, inclusion, access, and real world impact organizational priorities in its mission to be the New American University. The primarily focus of the case study is the experiential knowledge of administrative leaders and administrative intermediaries who have managed open innovation programming at the university over the past five years. Using theoretical pattern matching, administrator insights on open innovation adoption are illustrated in terms of design stages, teamwork, and ASU's culture of innovation. It is found that administrators view iterative experimentation with goals of impact as organizational priorities. Institutional support for iterative, experimental programming, along with the assumption that not every effort will be successful, empowers administrators to push to be bolder in their implementation of open innovation. Theoretical pattern matching also enabled a detailed study of administrator alignment regarding one particular open innovation program, the hybrid participatory platform 10,000 Solutions. Creating a successful and meaningful hybrid platform is much more complex than administrators anticipated at the outset. This chapter provides administrator insights in the design, management, and evaluation of participatory platforms. Next, demographic assessment of student participation in open innovation programming is presented. Demographics are found to be reflective of the university population and provide indicators for how to improve existing programming. This dissertation expands understanding of the task facing administrators in an organization seeking to integrate open innovation into their work.