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Record W2027133154 · doi:10.1177/0007650307306641

Building Chains and Directing Flows

2007· article· en· W2027133154 on OpenAlexaff
Charlene Zietsma, Monika Winn

Bibliographic record

VenueBusiness & Society · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of VictoriaWestern University
Fundersnot available
KeywordsStakeholderStakeholder theoryOrganizational fieldBusinessWork (physics)Focus (optics)Public relationsField (mathematics)Institutional theoryStakeholder analysisKnowledge managementMarketingPolitical scienceManagementEconomicsComputer science

Abstract

fetched live from OpenAlex

This article aims to deepen the understanding of the processes and specific actions aimed at influencing and shaping business practices through dynamic stakeholder relationships. An inductive, longitudinal study of all players involved in a stakeholder conflict identified four clusters of influence tactics that were used by both secondary stakeholders and their target firms: issue raising, issue suppressing, positioning, and solution seeking. The stakeholders studied built elaborate influence chains and worked to direct influence flows. The study contributes to stakeholder theory by offering a refined understanding of both bilateral and mutual-influence tactics, expanding the theory's focus beyond bilateral relationships, and highlighting the use of dependence relationships among multiple embedded organizations to build influence over a specific target, and more generally, an organizational field. These findings are discussed in light of work on social movement organizations and institutional theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0040.009
Scholarly communication0.0060.013
Open science0.0010.008
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.222
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations91
Published2007
Admission routes1
Has abstractyes

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