Abstract
Although several models of the innovation process have been proposed, those inspired by biological evolution
have been shown to better capture the most distinctive features of business innovation. We claim that non-conventional methods developed in bio-inspired engineering and computing provide robust and effective mechanisms to understand, foster and evaluate business innovations within organizational environments.
These methods, we argue, act as innovation accelerators. Adaptive business intelligence is introduced as a methodological framework to study the innovation process.
have been shown to better capture the most distinctive features of business innovation. We claim that non-conventional methods developed in bio-inspired engineering and computing provide robust and effective mechanisms to understand, foster and evaluate business innovations within organizational environments.
These methods, we argue, act as innovation accelerators. Adaptive business intelligence is introduced as a methodological framework to study the innovation process.
| Original language | English (US) |
|---|---|
| State | Published - Jun 30 2017 |
| Event | Conference on Computational Management Science - University of Bergamo, Georgia Institute of Technology, Bergamo, Italy Duration: May 30 2017 → Jun 1 2017 https://dinamico2.unibg.it/cms2017/ |
Conference
| Conference | Conference on Computational Management Science |
|---|---|
| Abbreviated title | CCMS2017 |
| Country/Territory | Italy |
| City | Bergamo |
| Period | 5/30/17 → 6/1/17 |
| Internet address |