Derivative beliefs and evaluations
Bibliographic record
Abstract
Purpose The purpose of this research is to examine how consumers form beliefs and evaluate derivatives (e.g. handheld computers) and branded derivatives (e.g. Palm handheld computers). The aim is to study how consumers combine two categories (e.g. “handheld products” and “computers”) to form beliefs, how the similarity between the categories influences beliefs, how the addition of a brand changes beliefs, and how the presence of brand associations impacts on evaluations. Design/methodology/approach Three laboratory experiments to test hypotheses were conducted. Findings Results of the studies show the modifier (e.g. “handheld” in handheld computer) dominates derivative beliefs, but the nature of its dominance changes with category similarity. Brand effects are surprisingly limited in belief formation due to modifier dominance. Brand beliefs only transfer to branded derivatives when the brand fits with the modifier category. The presence of brand associations induces more positive evaluations of branded derivatives when the brand fits with the modifier category and, under certain circumstances, when it fits with the header‐category. Research implications/limitations The presence of multiple concepts (e.g. Palm handheld computer) is common in line and brand extensions, yet little research has examined such complex products. Their comprehension can be better predicted by utilizing conceptual combination theory. Practical implications Managers can better determine what kinds of line and brand extensions are best suited for their brands. Originality/value The originality and value lay in utilizing the conceptual combination approach to more deeply understand which extensions are best suited for which brands. This helps fill a gap in the literature on consumer perception of multiple‐concept extensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".