Product-Oriented Web Technologies and Product Returns: An Exploratory Study
Bibliographic record
Abstract
Internet retailers have been making significant investments in Web technologies, such as zoom, alternative photos, and color swatch, that are capable of providing detailed product-oriented information and, thereby, mitigating the lack of “touch and feel,” which, in turn, is expected to lower product returns. However, a clear understanding of the relationship between these technologies and product returns is still lacking. Our study attempts to fill this gap by using several econometric models to explore the said relationship. Our unique and rich data set from a women's clothing company allows us to measure technology usage at the product level for each consumer. The results show that, in this context, zoom usage has a negative coefficient, suggesting that a higher use of the zoom technology is associated with fewer returns. Interestingly, we find that a higher use of alternative photos is associated with more returns and, perhaps more importantly, with lower net sales. Color swatch, on the other hand, does not seem to have any effect on returns. Thus, our findings show that different technologies have different effects on product returns. We provide explanations for these findings based on the extant literature. We also conduct a number of tests to ensure the robustness of the results.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".