Corporate reputation, stakeholders and the social performance‐financial performance relationship
Bibliographic record
Abstract
Purpose To increase understanding of the role of reputation in the corporate social performance (CSP) and financial performance (FP) relationship, including contingencies. Design/methodology/approach Stakeholder theory is drawn on to present a model of reputation's role in the contingent CSP‐FP relationship. Findings CSP is affected by stakeholders' resource allocation to the organisation. This allocation is based on stakeholders' assessment of the organisation's reputation relative to stakeholders' particular expectations, which may be instrumentally and/or normatively framed. Reputation, therefore, plays a key role in the CSP‐FP relationship. Additionally, the authors propose that the equivocal results of previous research into the CSP‐FP relationship may be partly explained by organisational and market contingencies. Specifically, the authors contend that strategic fit, competitive intensity and reputation management capability moderate the CSP‐FP relationship. Research limitations/implications Empirical measurement issues and future research directions are discussed. Originality/value This paper increases the understanding of the role of reputation in the CSP‐FP relationship. Owing to its rich pedigree in research in corporate branding and reputation, marketing is uniquely positioned to contribute toward the better understanding of this issue.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".