TY - JOUR
T1 - Innovation and learning performance implications of free revealing and knowledge brokering in competing communities
T2 - insights from the Netflix Prize challenge
AU - Villarroel, J. Andrei
AU - Taylor, John E.
AU - Tucci, Christopher L.
N1 - Funding Information:
Acknowledgements This research was first distinguished with a Best Student Paper Award from the North American Association for Computational Social and Organization Science (NAACSOS) in 2007 (http://www.casos.cs.cmu.edu/naacsos/awards.php). Further development of this work benefited from fellowships of the National Science Foundation of Switzerland (PBELP2-123027) and the MIT Sloan International Faculty Fellows program. The article incorporated helpful comments received at the Academy of Management Annual Meeting in 2008 and the Strategic Management Society 30th International Annual Meeting in 2010, where this research was presented. In particular, the authors would like to thank colleagues in the modeling community, Michael Prietula, David Sallach, and John Sterman, for their valuable feedback. The conceptual contribution of this work received constructive comments from Dietmar Harhoff, Eric von Hippel, Karim Lakhani, and Joel West, whom we thank dearly. Not the least, the authors extend their special appreciation to the editors of CMOT and the anonymous reviewers who contributed to improving the quality of this piece.
Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013/3
Y1 - 2013/3
N2 - Firms increasingly use open competitions to extend their innovation process and access new diverse knowledge. The Netflix Prize case we study in this paper is a multi-stage repeat-submission open competition involving the creation of new knowledge from across knowledge domains, a process which benefits from knowledge sharing across competing communities. The extant literature says little about the effects of different types and levels of knowledge sharing behavior on the learning and innovation outcomes of such a competitive system, or what the performance boundaries may be for the system as a result of such differences. Our research explores those boundaries unveiling important tradeoffs involving free revealing behavior-defined as voluntarily giving away codified knowledge and making it into a 'public good'-and knowledge brokering behavior-defined as using knowledge from one domain to innovate in another-on the learning performance of competing communities. The results, analyzing the system-level average and volatility of learning outcomes, lead to three conclusions: (i) greater knowledge sharing, as portrayed by greater free revealing and knowledge brokering, helps achieve better average learning for the system as a whole, however, (ii) achieving the best overall outcome possible from the system actually requires controlling the amount of knowledge brokering activity in the system. The results further suggest that (iii) it should not be possible to simultaneously achieve both the best overall outcome from the system and the best average learning for the system. The tradeoffs that ensue from these findings have important implications for innovation policy and management. This research contributes to practice by showing how it is possible to achieve different learning performance outcomes by managing the types and levels of knowledge sharing in open competitive systems.
AB - Firms increasingly use open competitions to extend their innovation process and access new diverse knowledge. The Netflix Prize case we study in this paper is a multi-stage repeat-submission open competition involving the creation of new knowledge from across knowledge domains, a process which benefits from knowledge sharing across competing communities. The extant literature says little about the effects of different types and levels of knowledge sharing behavior on the learning and innovation outcomes of such a competitive system, or what the performance boundaries may be for the system as a result of such differences. Our research explores those boundaries unveiling important tradeoffs involving free revealing behavior-defined as voluntarily giving away codified knowledge and making it into a 'public good'-and knowledge brokering behavior-defined as using knowledge from one domain to innovate in another-on the learning performance of competing communities. The results, analyzing the system-level average and volatility of learning outcomes, lead to three conclusions: (i) greater knowledge sharing, as portrayed by greater free revealing and knowledge brokering, helps achieve better average learning for the system as a whole, however, (ii) achieving the best overall outcome possible from the system actually requires controlling the amount of knowledge brokering activity in the system. The results further suggest that (iii) it should not be possible to simultaneously achieve both the best overall outcome from the system and the best average learning for the system. The tradeoffs that ensue from these findings have important implications for innovation policy and management. This research contributes to practice by showing how it is possible to achieve different learning performance outcomes by managing the types and levels of knowledge sharing in open competitive systems.
KW - Computer simulation
KW - Crowdsourcing
KW - Free revealing
KW - Knowledge brokering
KW - Managing online communities
KW - Organizational learning
UR - http://www.scopus.com/inward/record.url?scp=84874441773&partnerID=8YFLogxK
U2 - 10.1007/s10588-012-9137-7
DO - 10.1007/s10588-012-9137-7
M3 - Article
AN - SCOPUS:84874441773
SN - 1381-298X
VL - 19
SP - 42
EP - 77
JO - Computational and Mathematical Organization Theory
JF - Computational and Mathematical Organization Theory
IS - 1
ER -
