Internet Shopping Behaviors of Generation Y African-American Based on Apparel Production Involvement
Bibliographic record
Abstract
This study investigates internet shopping behaviors of Generation Y African-Americans (GYAAs), based on their levels of apparel product involvement associated with internet shopping orientations, internet situational influences, internet behavioral intentions, and previous internet shopping experiences. Data were collected from African-American college students of several universities in southeastern United States. Of the total surveys collected, 240 completed surveys were analyzed using multivariate analysis of variance (MANOVA), univariate analysis of variance (ANOVA), one-way ANOVA, chi-square, and phi-coefficient. This study attempts to understand internet shopping behaviors of GYAA. This research demonstrates that GYAAs have unique internet shopping behaviors toward on-line apparel products, showing that two involvement groups differ significantly in many ways. Internet shopping is highly attractive to high-involvement GYAA consumers due to its entertainment during their web-surfing as well as many other reasons such as its fashion-consciousness and personality rather than the reasons of convenience, expense, and familiarity, which are more sensitive to low-involvement GYAA consumers.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".