Optimal Visualization Aids and Temporal Framing for New Products
Bibliographic record
Abstract
Conventional wisdom suggests that more concrete and detailed information is helpful in evaluating new products. The current research, however, demonstrates that when consumers use visualization to evaluate new products, the value of concrete versus abstract visualization is dependent on the temporal perspective taken by the consumer. Specifically, concrete information is beneficial when prod-uct visualization is retrospective in nature (i.e., focused on the past), whereas abstract information is found to be more helpful when product visualization is anticipatory in nature (i.e., geared toward the future). This occurs because the match between visualization aids and consumers ’ temporal construal facilitates the extent of imagery processing realized, which, in turn, enhances new product evaluation. When the new product is very difficult to visualize, this pattern of effects is attenuated. Further, the effect is reversed when the product is highly familiar (i.e., not a new product), as preexisting memories are shown to hinder imagery processing. Theoretical and practical implications are discussed. Imagine that you are the product manager for the GoogleGlass, a new product where a small screen is placed in
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".