Trends in consumer preferences for product customization and theirapplication in product design
Bibliographic record
Abstract
Consumer preferences play an important role in the decision to customize a product.The collection of consumer preferences can be a challenging task and most methods require significant effort while only focusing on one specific product or product type.A popular method of collecting consumer preferences is a survey as it is easy to administer and can be tailored to gather specific desirable information.In this paper, a survey is developed to gather consumer preferences for product customization with a focus on identifying trends amongst these preferences.User preferences are correlated to factors of success in customization and categorical definitions of product attributes.Twenty-five products were evaluated, with one hundred total responses collected.The results show the existence of product clusters that represent trends amongst consumer preferences for customization.The trends and clusters identified have potential applications in the design of novel customized products by using categorical representations to generalize the findings.This method of generalization can provide cost and time savings in the product design cycle for future customized products by reducing the effort required to elicit consumer preferences.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".