Beyond the Post: How Profile Picture Changes Shape Consumer Perceptions of Brand Activism
Bibliographic record
Abstract
ABSTRACT Brands are increasingly engaging in brand activism on social media, creating campaigns and taking public stances on social and political issues that sometimes can be divisive (Sarkar and Kotler 2018; Moorman 2020). Due to the division in consumers' stances on these issues, extant research has identified a backlash effect of brand activism. In this study, we examine how brands can communicate and uphold their stances authentically on social media without alienating the consumers who stand opposite to a brand's stance. Building on prior research on brand activism and social media, we examine the impact of using a profile image to advocate a cause on consumers' attitudes toward the brand. In a series of experimental studies and an online field study, we found that advocating for a cause through a change of profile picture can increase brand authenticity and liking without alienating consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".