A survey on the impacts of brand extension strategy on consumers’ attitude for new products development
Bibliographic record
Abstract
Marketing planning has been one of the most important components of production planning.A good marketing strategy can increase sales of products, which leads to higher profitability.The proposed study of this paper investigates how consumer's mental capability reacts when a new product is introduced along with a well known brand.The proposed study of this paper selects 196 people who choose 12 hypothetical new product.Dependent variable is consumer perception towards new product development.Independent variables include customers consider newly offered product as replacement or supplement one.They surveyed people are also asked whether they think the new technological characteristics can incorporate older ones' or not.Finally, participants are asked about their perception on product.Correlation ratio between technology transfer capabilities and perception towards the new product is calculated as 0.454, which is much more than other variables and P-value is significant when the level of significance is one percent.The other observation is that there is a positive and meaningful relationship between potential for product substitution and perception towards the new product, which has been calculated as 0.227.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".