Managing Brand Relationship Plurality: Insights from the Nonprofit Sector
Bibliographic record
Abstract
The nonprofit sector is home to some of the most recognized and trustworthy brands, all competing for financial resources and volunteers. Akin to consumers, volunteers have relationships with nonprofit brands. These relationships have recently become more diverse as individuals increasingly look for more ephemeral and distant forms of involvement. Drawing on an extensive qualitative dataset of the Vienna Red Cross comprising participant observation, archival data, and in-depth interviews, the authors conceptualize this nonescalating, episodic engagement as a neither-growing-nor-fading (NGNF) relationship. This theorization adds to the literature on consumer–brand relationships, which has predominantly focused on the cultivation of strong relationships. Informed by practice theory, the authors elaborate distinct brand relationship practices key to successfully maintaining NGNF relationships (acquiring and activating) while catering to volunteers following the traditional path of relationship intensification (building and cultivating). The analysis identifies constellations of practice elements conducive to managing both types of brand relationships in a symbiotic manner. The authors argue for the importance of moving beyond an exclusive focus on relationship growth and embracing nonescalating relationships. This study thus contributes to nascent theorizing on brand relationships that do not follow an axiology that values growth and intensification.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".