Build or buy corporate social responsibility? Socially responsible brand acquisitions and firm CSR perceptions
Bibliographic record
Abstract
• Acquisition (versus development) of socially responsible brands impacts firm CSR perceptions. • Brand development (vs. acquisition) is more effective in increasing firm CSR perceptions. • When a socially responsible brands’ symbolic value is high, acquisition harms brand credibility. • Acquisitions of highly symbolic socially responsible brands thus decrease firm CSR perceptions. • Brand architecture and communication strategies can mitigate this negative effect. Because development of socially responsible brands is costly and risky, acquisition of socially responsible brands (SRB) has emerged as a frequently employed approach to enhancing firms’ CSR profile. This research explores the effectiveness of SRB acquisition (versus brand development) in increasing firm CSR perceptions and the moderating role of symbolic brand value in this relationship. This research demonstrates that SRB acquisition (vs. development) is generally less effective in signaling firm CSR and that the effect of SRB acquisitions on firm CSR perceptions depends on the acquired brand’s symbolic value. Seven experiments show that when symbolic brand value is high, acquisition (vs. development) of SRB harms the acquired brand’s credibility and subsequently reduces firm CSR perceptions. Two additional studies explore the role of brand architecture and communication strategies in mitigating the negative impact of highly symbolic, SRB acquisitions on consumers’ perceptions of firm CSR.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".