Visualizing Collective Idea Generation and Innovation Processes in Social Networks

Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, Hiroki Sayama

研究成果: Article査読

抄録

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 <italic>Idea Cloud</italic>, which helps monitor collective idea posting activity and intuitively tracks idea clustering and transition. The second visualization is the <italic>Idea</italic> 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 <italic>Idea</italic> <italic>Network</italic>, 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.

本文言語English
ページ(範囲)1-10
ページ数10
ジャーナルIEEE Transactions on Computational Social Systems
DOI
出版ステータスAccepted/In press - 2022

ASJC Scopus subject areas

  • モデリングとシミュレーション
  • 社会科学(その他)
  • 人間とコンピュータの相互作用

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