MétaCan
Menu
Back to cohort

From Instrumental Stakeholder Theory to Stakeholder Capitalism

2021· reference-entry· en· W3174238737 on OpenAlexaff
André O. Laplume

Bibliographic record

VenueOxford Research Encyclopedia of Business and Management · 2021
Typereference-entry
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStakeholderStakeholder analysisStakeholder managementStakeholder theoryBusinessCompetitor analysisVariety (cybernetics)EntrepreneurshipCompetitive advantageIndustrial organizationPublic relationsMarketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Instrumental stakeholder theory posits that managing for stakeholders using justice-based approaches produces competitive advantage for firms. However, achieving the ideals of stakeholder management may be challenging, and for some firms, unrewarding. Yet, when firms fail to manage for stakeholders, they contribute to stakeholder marginalization, a condition in which stakeholders feel unfairly treated and begin to scan for alternative arrangements with other firms. Stakeholder marginalization creates opportunities for competitors, but especially for new entrants, to pursue stakeholder innovation. Stakeholder innovation involves the creation of a business model that caters to marginalized stakeholder groups in a new way, by improving perceived conditions for those stakeholders (e.g., customers, employees, suppliers, or communities). Stakeholder innovations can threaten incumbencies as their ecosystems bloom and technologies improve, and they can start to draw a greater variety of resources away from incumbent networks. Because it can help to explain and predict both incumbent and new entrant behaviors, stakeholder capitalism is a useful frame for theorizing in the disciplines of management and entrepreneurship.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0000.001
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 head0.098
GPT teacher head0.307
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOxford Research Encyclopedia of Business and ManagementSame topicCorporate Social Responsibility ReportingFrench-language works237,207