Users represent an often untapped source of knowledge which companies can capitalize on during different stages of the innovation process. However, identifying helpful users for innovation projects is far from trivial as these individuals are often hidden within considerably larger populations. Thus, potential benefits of user integration may be outweighed by the cost of identifying them.
This research focuses on investigating the efficiency of pyramiding and screening, two search strategies aiming at acquiring rich knowledge from users. Combining empirical data and simulation, we investigate the efficiency of these search strategies when trying to identify users with complex, multi-faceted characteristics in groups of various sizes. Additionally, we explore how searchers may further increase pyramiding efficiency by selecting appropriate starting persons for the search or by abandoning unpromising search chains.
The findings will help to understand knowledge flows in social networks and how they can be exploited for reference based searches. This will point managers to ways of dealing with information overload and help them to more efficiently identify valuable external knowledge for corporate innovation processes.