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Record W3126118716 · doi:10.1111/jems.12283

Moral management in competitive markets

2018· article· en· W3126118716 on OpenAlexafffund
Steve Martin

Bibliographic record

VenueJournal of Economics & Management Strategy · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaLG DisplayUniversity of Ottawa
KeywordsCorporate social responsibilityShareholderProsocial behaviorDelegateProfitability indexBusinessCompetition (biology)Social responsibilityMarket competitionCrowdsInternalism and externalismIndustrial organizationMarketingPublic relationsEconomicsMarket economyCorporate governanceFinanceSocial psychology

Abstract

fetched live from OpenAlex

Abstract The intrinsic motivation of a firm’s management for engaging in prosocial behavior is an important determinant of a firm’s social conduct. I provide the first model in which firms run by morally motivated managers engage in corporate social responsibility (CSR) in a competitive setting. Moral management crowds out a competitor’s strategic CSR, increasing profitability and leading shareholders to strategically delegate moral managers, although necessary for socially optimal CSR is that shareholders be morally motivated as well. Shareholders appoint managers that engage in a socially excessive amount of CSR, counter to existing literature, whenever product‐market competition is sufficiently intense.

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)0.005
metaresearch head score (Gemma)0.009
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: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.009
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.037
GPT teacher head0.261
Teacher spread0.225 · 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
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

Citations4
Published2018
Admission routes2
Has abstractyes

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