How does corporate social responsibility create value for consumers?
Bibliographic record
Abstract
Purpose Research examining corporate social responsibility (CSR) demonstrates a relatively consistent level of positive support by consumers. However, CSR is poorly defined and little is known about the mechanisms by which this response occurs. This paper seeks to understand how consumers define CSR and how it can enhance the overall value proposition for consumers. Design/methodology/approach The value typology developed by Sheth et al. is integrated with qualitative data to enhance understanding of these value paths. Interviews were conducted with consumers through the heart of the current recession, when consumers were particularly aware of value when making purchase decisions. Findings The way in which CSR manifests itself determines consumer support. CSR can provide three forms of value to consumers: emotional, social, and functional. Each of these enhances or diminishes the overall value proposition for consumers. Further, value created by one form of CSR can either enhance or diminish other product attributes. Practical implications The current research helps managers understand how CSR can create value for consumers. As a result, managers can better position products in order to enhance overall value. Further, practitioners can match the value with which consumers identify from CSR to the dominant value driver in their product category. Originality/value This study highlights that CSR includes a range of activities with differential means of adding value to consumers.
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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 | 0.003 | 0.015 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".