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Innovating knowledge communities

An analysis of group collaboration and competition in science and technology

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Abstract

A useful level of analysis for the study of innovation may be what we call “knowledge communities”—intellectually cohesive, organic inter-organizational forms. Formal organizations like firms are excellent at promoting cooperation, but knowledge communities are superior at fostering collaboration—the most important process in innovation. Rather than focusing on what encourages performance in formal organizations, we study what characteristics encourage aggregate superior performance in informal knowledge communities in computer science. Specifically, we explore the way knowledge communities both draw on past knowledge, as seen in citations, and use rhetoric, as found in writing, to seek a basis for differential success. We find that when using knowledge successful knowledge communities draw from a broad range of sources and are extremely flexible in changing and adapting. In marked contrast, when using rhetoric successful knowledge communities tend to use very similar vocabularies and language that does not move or adapt over time and is not unique or esoteric compared to the vocabulary of other communities. A better understanding of how inter-organizational collaborative network structures encourage innovation is important to understanding what drives innovation and how to promote it.

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Notes

  1. Appendices with other details on the 21 knowledge communities identified are available from the author.

  2. To check Model 2 for this effect we kept the same controls and ran the model with both knowledge and rhetorical cohesiveness alone. Each variable retained its direction but became slightly less significant.

    In Model 3 the individual inclusion of each variable sees knowledge uniqueness flip to the positive when included individually; however, it is not statistically significant. This could indicate that our joint significance reveals a secondary trend in knowledge uniqueness that is only evident after controlling for the rhetorical uniqueness of a cluster. Rhetorical uniqueness retains its significance and direction when it is included alone in Model 3.

    In our investigation of Model 4 we found that Knowledge Flexibility retained its significance and direction when included individually while Rhetorical Flexibility did flip to the positive direction but without statistical significance. This individual flip explains why the original Model 4 including both variables finds neither to be significant.

    Finally, to verify our results in the final model are not unduly influenced by these we included just one of each pair and found our directions remained fairly consistent with the expected changes in significance already detailed in the earlier models.

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Authors and Affiliations

  1. The Wharton School, University of Pennsylvania, Philadelphia, PA, 19104-6370, USA

    S. Phineas Upham & Lori Rosenkopf

  2. CIS Department, University of Pennsylvania, Philadelphia, PA, 19104-6309, USA

    Lyle H. Ungar

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  1. S. Phineas Upham
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  2. Lori Rosenkopf
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  3. Lyle H. Ungar
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Correspondence to S. Phineas Upham.

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Table 6 Cluster descriptions
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Upham, S.P., Rosenkopf, L. & Ungar, L.H. Innovating knowledge communities. Scientometrics 83, 525–554 (2010). https://doi.org/10.1007/s11192-009-0102-2

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