The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Knowledge Sourcing Effectiveness
Abstract
Much of the knowledge management (KM) literature focuses on ways to increase the volume of knowledge available to employees, ensure its quality, and improve its accessibility. Such supply-side arguments are limited to the extent that they do not address the demand for knowledge within organizations. This paper takes a novel approach to understanding how access to others' knowledge produces benefits by studying the extent to which individuals intentionally access each other's expertise, experience, insights, and opinions, which we term knowledge sourcing. A general model of knowledge sourcing, including contextual and dispositional antecedents and learning outcomes, is proposed and validated using survey data from a global organization. Knowledge sourcing explains a significant proportion of individuals' learning outcomes, but the strength of this effect is moderated both by the strength of individuals' learning orientations and the degree to which they find their jobs to be intellectually demanding. For researchers, this study extends existing knowledge by proposing, testing, and validating a new way to understand an important KM issue in organizations. Practitioners can use these findings to evaluate existing KM efforts and better target future KM interventions towards those individuals most likely to benefit.
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Cites
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Metadata
- Authors
- Gray, P. H., & Meister, D. B.
- Venue
- Management Science
- Published
- 2004-06
- Volume
- 50
- Issue
- 6
- Pages
- 821-834
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2014620128
- Cited by
- 423
- References
- 18
- FWCI
- 50.66
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.