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Record W2993177240 · doi:10.1108/jgr-03-2019-0033

Good neighbor or good employer?

2019· article· en· W2993177240 on OpenAlexaff
Carol‐Ann Tetrault Sirsly, Elena Lvina, Cătălin Raţiu

Bibliographic record

VenueJournal of Global Responsibility · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsCarleton University
Fundersnot available
KeywordsCorporate social responsibilityOperationalizationReputationOriginalityStakeholderLeverage (statistics)BusinessStakeholder theoryStatus quoPublic relationsMarketingStakeholder engagementInstitutional theoryAccountingQualitative researchEconomicsPolitical scienceSociologyManagementComputer science

Abstract

fetched live from OpenAlex

Purpose This study aims to test Mattingly and Berman’s (2006) taxonomy of social actions and develops divergent expectations for corporate social responsibility (CSR) dimensions directed toward institutional and technical stakeholders, with an aim to determine when CSR directed to different stakeholders is most likely to improve corporate reputation. Design/methodology/approach A longitudinal sample of 285 major US corporations was used to quantitatively test the hypotheses. Data was sourced from KLD, Osiris and Fortune MAC. Findings Strengths in CSR and actions directed toward technical stakeholders influence corporate reputation in a more profound way, when compared to those directed toward institutional stakeholders. Contrary to the authors’ prediction, institutional concerns do not demonstrate a significant growth or reduction over the five-year period. Research limitations/implications This study provides a longitudinal test of Mattingly and Berman’s (2006) taxonomy of CSR actions and makes an important methodological contribution by operationalizing CSR not as a continuum from strengths to concerns, rather as two distinct constructs. Practical implications Management practice can benefit from a more fine-grained approach to stakeholder expectations and reputation outcomes. The results of this study leverage relevant stakeholder impact while allowing firms to appreciate the change in CSR actions and to measure it accordingly, such that the undesirable status quo that leads to potential loss in reputation growth can be avoided. Social implications As organizations explore ways to effectively engage stakeholders for mutual benefit, this research shows how firms can have a positive impact. Originality/value This study tests and extends theory through an integrated lens, built on the stakeholder and resource dependence theories, while directing management attention to the broader reputational outcomes of targeted CSR initiatives. It provides justification for CSR investments over time.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.005

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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