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Record W3189164128 · doi:10.1017/beq.2021.19

Evidence of an Inverted U–Shaped Relationship between Stakeholder Management Performance Variation and Firm Performance

2021· article· en· W3189164128 on OpenAlexaff
André O. Laplume, Jeffrey S. Harrison, Zhou Zhang, Xin Yu, Kent Walker

Bibliographic record

VenueBusiness Ethics Quarterly · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of WindsorUniversity of ReginaToronto Metropolitan University
Fundersnot available
KeywordsStakeholderStakeholder theoryAssertionStakeholder managementVariation (astronomy)BusinessSample (material)Stakeholder analysisEmpirical evidenceEconomicsManagementComputer science

Abstract

fetched live from OpenAlex

Empirical research is largely supportive of the assertion of instrumental stakeholder theory that a positive relationship exists between “managing for stakeholders” and firm performance. However, despite considerable debate on the subject, the amount of variation across firm investments in stakeholders (stakeholder management performance) has not been adequately investigated. We address this gap using a sample of more than eighteen thousand firm-level observations over ten years. We find evidence to support an inverted U–shaped relationship between variation in stakeholder management performance and Tobin’s q , suggesting that firms that have some imbalance in their stakeholder management, but not too much, perform best. We discuss the implications of our study for instrumental stakeholder theory and managerial practice.

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)0.012
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.102
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.216
GPT teacher head0.314
Teacher spread0.097 · 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 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

Citations23
Published2021
Admission routes1
Has abstractyes

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