Determinants and moderators of corporate reputation: A meta-analysis
Bibliographic record
Abstract
Corporate reputation has been researched for decades, and the diversity of studies generates various findings. Based on 81 empirical studies, 838 correlations, 13,358 companies and 31,499 survey respondents, we identifying what antecedents of corporate reputation are most relevant and what factors moderate these relationships. Using meta-analysis and Bayesian variance estimation, our results suggest that product and service quality, customer satisfaction, corporate financial performance, charity contributions, employee satisfaction, network performance and media exposure have dependably significant correlations across different industries and study contexts. With institutional and societal changes, these factors tend to have moderating effects on corporate reputation. Specifically, the influence of management performance, customer satisfaction and product and service quality is increasing over the years, while financial performance appears to be waning as a correlate or predictor of corporate reputation. We also suggest that corporate financial performance has a stronger effect on corporate reputation in studies using the Fortune database. Future studies should examine the influence of other moderators such as geography and industry on corporate reputation and pay attention to detailed analyses on specific dimensions of reputation instead of reporting it exclusively as an overall concept.
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 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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".