The Effect of Novel Attributes on Product Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Many technological innovations introduce attributes that are novel or completely unknown to a large number of consumers. For example, recently introduced attributes such as GPS in cars or I-Link in computers are likely to have been novel to many consumers. Past research suggests that the addition of novel attributes is likely to improve product evaluation and sales, since consumers interpret these attributes as additional benefits provided by the manufacturer. However, this article demonstrates that the positive effect of novel attributes holds only in the case of low-complexity products. In the case of high-complexity products, the addition of novel attributes can actually reduce product evaluation because of negative learning-cost inferences about these attributes. Further, the positive and negative effects of novel attributes on product evaluation are accentuated by external search for information when the information discovered through search is ambiguous in nature. Finally, it is shown that the negative effect of novel attributes on the evaluation of high-complexity products can persist even after consumers are given explicit information about the benefits of novel attributes. A key marketing implication of these findings is that novel attributes may contribute to technophobia, or consumer resistance toward technological innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.069 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it