Consumer generated brand extensions: definition and response strategies
Bibliographic record
Abstract
Purpose Sometimes consumers express their enthusiasm for a brand by creating brand extensions, products or services in new categories that are closely affiliated with the “parent” brand. This paper aims to examine the positive or negative impact that consumer-generated brand extensions (CGBEs) can have on brand image and revenue, and the options that companies have available to deal with them. Design/methodology/approach The paper presents a case study of the collectible strategy card game – Magic: The Gathering – and discusses how the company responded to five different brand extensions that were created by players. This case study was used to develop a framework that allows managers to evaluate CGBEs based on their benefits and risks and to select an appropriate response. Findings Four possible responses were identified: challenge, criticize, commend and catalyze. Which of these responses is appropriate for companies depends on whether the CGBE has a positive or negative impact on the brand image and revenue and whether this impact is large enough to merit an active response. Originality/value This study shows that it is essential for managers to understand how to evaluate CGBEs. Managed properly, they can improve product usage, help generate new customers and have a positive impact on revenue and brand image. However, CGBEs can also have a negative effect, in particular if they are substitutes for the original product.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".