Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of Who and What Really Counts

The Academy of Management Review(1997)10.5465/amr.1997.9711022105Source: DataRank Database

Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of Who and What Really Counts is a research paper published in The Academy of Management Review (1997). On theSindex it has a DataRank of 1.4. It has been cited 9,465 times.

N/A

1.4DataRank · unranked

1.4

9465 citations · base score 9.2

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Stakeholder theory has been a popular heuristic for describing the management environment for years, but it has not attained full theoretical status. Our aim in this article is to contribute to a theory of stakeholder identification and salience based on stakeholders possessing one or more of three relationship attributes: power, legitimacy, and urgency. By combining these attributes, we generate a typology of stakeholders, propositions concerning their salience to managers of the firm, and research and management implications.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

1.4

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →