Towards a model of conscientious corporate brands: a Canadian study
Bibliographic record
Abstract
Purpose – This paper attempts to validate a conceptual model for conscientious corporate brands (CCB) by exploring environmental and climate change issues together with perceptions of the internal and external effectiveness of corporate codes of ethics as dimensions of CCBs. Design/methodology/approach – By surveying organizations, the paper attempts to extend and validate previous research in ethical branding by proposing an additional empirically grounded conceptual model of “the conscientious dimension” of corporate brands. Research limitations/implications – The CCB model was tested on a sample of small-, medium- and large-sized companies in Canada, which may indicate less generalizability to larger companies or in other countries and contextual settings. Practical implications – The CCB-framework provides insights into the relationship between the natural environment, climate change and corporate codes of ethics, which organizational managers might relate to their organization. Originality/value – This empirical study extends previous research by studying the willingness among business managers to support aspects of conscientious corporate brands (CCBs) in business-to-business relationships: when considering the impact of their brands on the natural environment and climate change, and when considering their corporate codes of ethics. Such findings imply that ethical conscientiousness is not just a rider to brand value; rather, it is an integral dimension in the manufacturer-supplier relationship.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".