Consumers' evaluations of co‐branded products: the licensing effect
Bibliographic record
Abstract
Purpose This paper seeks to establish the importance of studying the effects of licensing brand alliances from a customer's standpoint, to investigate the effectiveness of licensing as a strategy by comparing it with a brand extension of a well‐known parent brand, and to provide a theoretical explanation for the licensing effects. Design/methodology/approach In Study 1, subjects' attitudes were measured towards a lesser known brand with and without licensing by Sony, and Sony alone in a three‐factor (licensing, no licensing, and Sony) between‐subjects design. Study 2 compared a licensed brand with a brand extension of a well‐known brand using the Chow test. Findings A brand “licensed by Sony” was evaluated higher than without licensing. Moreover, no difference was found between evaluation of a brand licensed by Sony and Sony alone. Study 2 revealed no significant difference between the data collected from a licensed brand and a well‐known brand extension, suggesting that being a licensed brand in some cases may be as effective as being an extension of a well‐known brand. Research limitations/implications The research examined the effects of strong brand names (e.g. Sony). It would be interesting to extend the findings by examining the brand names that are perhaps less strong (e.g. Samsung) to test the generalizability of the research. Practical implications For lesser‐known brands, licensing could be a viable strategy to increase their brand evaluation. Originality/value For new brands, this paper provides evidence that licensing is a viable strategy, and also provides a theoretical explanation for the licensing effects.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 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".