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Record W2160143265

A Social Responsiveness Approach to Stakeholder Management:Lessons from the Canadian Banking Sector

2010· article· en· W2160143265 on OpenAlexaboutno aff
Mark A. Fuller

Bibliographic record

VenueJournal of Leadership Accountability and Ethics · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderNature versus nurtureProfitability indexBusinessStakeholder managementMarketingEmpirical researchCorporate social responsibilityBanking industryStrategic managementPublic relationsAccountingFinanceSociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper explores the relationship between business objectives and social responsiveness toward stakeholders in terms of stakeholder management activity. It employs an empirical grounded study of the Canadian banking sector that involved semi-structured interviews combined with archival documents analysis. Findings suggest managers in mature industries with business growth-oriented objectives and a proactive responsiveness toward stakeholders are likely to engage in stakeholder management activities commonly associated with corporate social responsibility. Implications for managers include the need for multi-dimensional strategic planning. Implications for researchers suggest a need to re-conceptualize management studies to nurture interdisciplinary research on business strategy. We looked at the correlation across twenty industries between profitability and size. In only one industry is there a statistically significant correlation between size and profitability and that industry is the banking industry and the correlation is negative. So the bigger they get, the less money they make.

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)0.008
metaresearch head score (Gemma)0.010
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 score0.103
Threshold uncertainty score0.746

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0190.017
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.323
GPT teacher head0.348
Teacher spread0.024 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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