Influence of Product Class on Preference for Shopping on the Internet
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
The study reported here examined the influence of product classification (i.e., search, experience, and credence) on consumer preferences for shopping on the Internet, and the importance of Internet retailers' attributes. In addition, the authors investigated whether the emphasis consumers place on Internet retailer attributes significantly influences their online purchase preference for the different product categories. Based on the review of the product classification literature, products are classified into four categories: search products, two types of experience products, and credence products. Data were collected from adult Internet users in two phases, through self-administered surveys. The findings of the present study support the hypothesis that product classes significantly influence consumers' online purchase preferences. Internet retailer attributes were found to be important as well. In addition, the findings confirm that the importance consumers place upon Internet retailer attributes significantly influences their online purchase preference for different product categories. Managerial and academic implications are discussed.
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 | 0.001 | 0.000 |
| 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 | 0.001 | 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 it