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Record W1986870102 · doi:10.1509/jppm.29.1.27

Stakeholder Marketing and the Organizational Field: The Role of Institutional Capital and Ideological Framing

2010· article· en· W1986870102 on OpenAlexaff
Jay M. Handelman, Peggy Cunningham, Maureen Bourassa

Bibliographic record

VenueJournal of Public Policy & Marketing · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of SaskatchewanDalhousie UniversityQueen's University
Fundersnot available
KeywordsFraming (construction)StakeholderIdeologyMarketingPublic relationsStakeholder analysisOrganizational fieldPerspective (graphical)BusinessStakeholder theoryStakeholder managementSocial capitalSociologyInstitutional theoryPolitical sciencePoliticsSocial science

Abstract

fetched live from OpenAlex

This article adopts an institutional-based perspective to stakeholder marketing. This perspective directs attention to an organizational field level of analysis in which an organization's environment is punctuated by trigger events that prompt the assemblage of a particular mix of stakeholders. A thematic, interpretive, and longitudinal analysis of more than 2000 articles from 45 years of grocery retail trade journals reveals that the ensuing stakeholder dynamics that constitute an organizational field serve to afford or deny the marketer vital cultural, social, and economic capital. In turn, the capital possessed by or denied to the marketer influences the ideological frame the marketer may use in coming to terms with how to interact with stakeholders. Importantly, the authors find that strategic and institutional factors interpenetrate, presenting important implications for how stakeholder marketing should be understood.

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)0.023
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.003
Science and technology studies0.0060.041
Scholarly communication0.0180.018
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations9
Published2010
Admission routes1
Has abstractyes

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