Going against the Tide: How Self-Construal Moderates Receptivity towards Popular Brand Activism
Bibliographic record
Abstract
• Consumers’ self-construal can affect responses to brand activism. • Independents favor brands that oppose a popular view on a socio-political issue. • Both independents and interdependents favor brands that support the popular view. • Independents perceive brands that oppose the popular view as bold and courageous. Many brands in today’s marketplace choose to take a public stance on socio-political issues. For example, some brands have indicated support for or opposition to Israel, while there are brands that support or oppose same sex-marriages. This research examines the type of consumers to whom a brand supporting or opposing socio-political issues might appeal. Specifically, we focus on consumers with independent versus interdependent self-construal. Four studies offer evidence that independents consider brand activism that opposes prevailing public opinions regarding socio-political issues as “bold” and “courageous”. By contrast, independents and interdependents equally favor brands that support such majority public opinion. We discuss the limitations of these results and offer guidance for future researchers and brand managers. We situate our findings within the American political discourse and marketplace, discuss the relevance of our results for brands that are interested in activism, and acknowledge limitations to our work.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".