Felt something, hence it works: Merely adding a sensory signal to a product improves objective measures of product efficacy and product evaluations
Bibliographic record
Abstract
Abstract Product efficacy is an important driver of product evaluation and product usage. This research examines how marketers can improve perceived and actual product efficacy. Given the managerial ease of adjusting product design, we demonstrate that adding a sensory signal (e.g., tingling, cooling, fizzing) to a product that promises positive outcomes would improve product evaluations and actual product efficacy. In five studies (and two additional studies reported in the Web Appendix), we show that sensory signaling (vs. nonsignaling) products elicit actual product choice and improve product evaluations, repurchase likelihood, recommendation likelihood, as well as objective measures of product efficacy (such as consumer performance). This occurs because the sensory signals make consumers feel a greater transfer of benefits to the body during product usage. We further demonstrate that the effect holds even when persuasion knowledge is activated. Together, this research provides important insights on product designs that benefit not only marketers but also consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".