Dataset on knowledge states and stages and their impact on innovation performance

doi: 10.4121/5f99d080-f049-483a-a9ad-07212045a80e.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/5f99d080-f049-483a-a9ad-07212045a80e
Datacite citation style:
Vlas, Cristina (2023): Dataset on knowledge states and stages and their impact on innovation performance. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Decisive for firms’ innovation performance is the way they process differentiated and integrated knowledge. Differentiation has been argued to create specialization advantages and integration to enable common ground. Biased by the advantages of producing and maintaining common ground over specialization, scholars overlooked the merits of differentiation. We use a congruence-incongruence approach that investigates whether matching-unmatching stages of knowledge differentiation and integration leads to different innovation performance. Our conceptualization proposes that matching stages of differentiation and integration (congruence) is more conducive of higher innovation performance compared to directing attention to one over the other (incongruence). We find support for this claim. Results show that a mismatch of knowledge states is conducive of lower levels of innovation performance. Our study proposes that awareness of the congruence and incongruence between the stages (producing vs maintaining) of different knowledge states (differentiation vs integration) plays a critical role for innovation research.

  • 2023-09-15 first online, published, posted
University of Massachusetts Amherst, Isenberg School of Management


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