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Cited by 318

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Informants in Organizational Marketing Research: Why Use Multiple Informants and how to Aggregate Responses

Van Bruggen, Gerrit H., Lilien, Gary L., Kacker, Manish·2002·Journal of Marketing Research

Abstract

Organizational research frequently involves seeking judgmental response data from informants within organizations. This article discusses why using multiple informants improves the quality of response data and thereby the validity of research findings. The authors show that when there are multiple informants who disagree, responses aggregated with confidence- or competence-based weights outperform those with response data-based weights, which in turn provide significant gains in estimation accuracy over simply averaging informant reports. The proposed methods are effective, inexpensive, and easy to use in organizational marketing research.

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Crossref · 10.1509/jmkr.39.4.469.19117

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Metadata

Authors
Van Bruggen, G. H., Lilien, G. L., & Kacker, M.
Venue
Journal of Marketing Research
Published
2002-11
Volume
39
Issue
4
Pages
469-478
ISSN
  • 0022-2437 (ISSN-L)
  • 1547-7193
OpenAlex
W1976123198
Cited by
318
References
38
FWCI
19.53
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Informants in Organizational Marketing Research: Why Use Multiple Informants and how to Aggregate Responses. https://app.cochat.ai/papers/10.1509/jmkr.39.4.469.19117
The paper
Van Bruggen, G. H., Lilien, G. L., & Kacker, M. (2002). Informants in Organizational Marketing Research: Why Use Multiple Informants and how to Aggregate Responses. Journal of Marketing Research. https://doi.org/10.1509/jmkr.39.4.469.19117

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.