How do involvement and product knowledge affect the relationship between intangibility and perceived risk for brands and product categories?
Bibliographic record
Abstract
Purpose Intangibility has long been studied in marketing, especially its physical aspect. This paper seeks to verify whether a branding strategy is efficient in reducing the risk perceived by customers. Design/methodology/approach A sample of university students answered the measurements considering both perspectives (brands and product categories). The paper uses a three‐dimensional approach of intangibility and explores its relationships with evaluation difficulty (ED) and perceived risk (PR). These relationships were tested in two different perspectives: brands and product categories. Findings Two analyses were made to test the hypotheses which were generally supported. Several relationships between the variables were found, but three should be highlighted. First, it was shown that brands are more mentally intangible than product categories, which may lead to a difficulty to evaluate. Second, it was found that evaluation difficulty increases the perceived risk in the product category perspective. Third, it was found that higher involvement generates a stronger relationship between evaluation difficulty and perceived risk for the product category perspective. Practical implications Theoretical and managerial implications to the literature are discussed along with examples of how managers could use the findings. Originality/value The research incorporates prior knowledge and involvement as moderating variables of the proposed framework and reinforces their relevance to the field. The results not only show the importance of branding, but also support the argument of considering evaluation difficulty in future research.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".