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Record W1510597996 · doi:10.1108/03090560510610725

Marketing stakeholder analysis

2005· article· en· W1510597996 on OpenAlexaff
Bill Merrilees, Don Getz, Danny O’Brien

Bibliographic record

VenueEuropean Journal of Marketing · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBusinessMarketingStakeholderMarketing managementGlobal marketingConnection (principal bundle)International marketingMarketing strategyPublic relationsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Purpose - The paper aims to explore a major issue in international marketing: how to build a global brand in a way that makes a strong local connection. Design/methodology/approach - Using qualitative research methods on a single case, the Brisbane Goodwill Games, the processes used in the staging of this major sport event are analyzed. In particular, the stakeholder relations employed by the marketing department of the Goodwill Games Organization are investigated and a process model is developed that explains how a global brand can be built locally. Findings - A major outcome of the paper is a revision to the four-step Freeman process to make it more proactive; and three major principles for effective stakeholder management are articulated. The findings demonstrate that stakeholder analysis and management can be used to build more effective event brands. Stakeholder theory is also proposed as an appropriate and possibly stronger method of building inter-organizational linkages than alternatives such as network theory. Originality/value - Previous literature has generally dealt with the global brand issue in terms of the standardization versus adaptation debate, and the extent to which the marketing mix should be adapted to meet local needs in foreign countries. This research provides a unique extension to this literature by demonstrating how the brand itself needs to be modified to meet local needs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.215
Teacher spread0.189 · 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 teacher head, not a consensus.

Study designObservational
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

Citations90
Published2005
Admission routes1
Has abstractyes

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