Good neighbor or good employer?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".