| Autor: |
Cao, Yiding, Dong, Yingjun, Kim, Minjun, MacLaren, Neil G., Pandey, Sriniwas, Dionne, Shelley D., Yammarino, Francis J., Sayama, Hiroki |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Transactions on Computational Social Systems, 2022, https://ieeexplore.ieee.org/document/9819965 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TCSS.2022.3184628 |
| Popis: |
Collective idea generation and innovation processes are complex and dynamic, involving a large amount of qualitative narrative information that is difficult to monitor, analyze, and visualize using traditional methods. In this study, we developed three new visualization methods for collective idea generation and innovation processes and applied them to data from online social network experiments. The first visualization is the Idea Cloud, which helps monitor collective idea posting activity and intuitively tracks idea clustering and transition. The second visualization is the Idea Geography, which helps understand how the idea space and its utility landscape are structured and how collaboration was performed in that space. The third visualization is the Idea Network, which connects idea dynamics with the social structure of the people who generated them, displaying how social influence among neighbors may have affected collaborative activities and where innovative ideas arose and spread in the social network. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
