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Cited by 362

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Extended VAIC model: measuring intellectual capital components

Nazari, Jamal A., Herremans, Irene M.·2007·Journal of Intellectual Capital

Abstract

Purpose: In the intellectual capital (IC) literature, only a few studies have analyzed the relationships among the components of IC and organizational success. This study sets out to extend the current models to provide further insight into the role of IC in organizational performance. Design/methodology/approach: The study provides a theoretical discussion designed to push the measurement of IC into a more rigorous and comprehensive domain. Findings: As this is a theoretical paper, several hypotheses are presented for testing in the future. Practical implications: Recognizing the most influential elements of IC on organizational performance would help organizations to understand better the organizational capabilities they possess. In addition, the suggested extension would enable researchers to use archival resources to do cross‐company comparisons. Originality/value: The suggested extension to the VAIC model builds on several IC models that have not been well‐connected in the literature previously.

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Metadata

Authors
Nazari, J. A., & Herremans, I. M.
Venue
Journal of Intellectual Capital
Publisher
Emerald
Published
2007-10-23
Volume
8
Issue
4
Pages
595-609
ISSN
1469-1930 (ISSN-L)
OpenAlex
W1965533218
Cited by
362
References
49
FWCI
10.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Extended VAIC model: measuring intellectual capital components. https://app.cochat.ai/papers/10.1108/14691930710830774
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Nazari, J. A., & Herremans, I. M. (2007). Extended VAIC model: measuring intellectual capital components. Journal of Intellectual Capital. https://doi.org/10.1108/14691930710830774

Data sources

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.