A Case Study on Factors Influencing Online Apparel Consumption and Satisfaction between China and Ghana
Bibliographic record
Abstract
The study explores and compares the influence of perceived online shopping benefits namely convenience, pricing, and wider selection towards online satisfaction between China and Ghana. It also seeks to explore the factors that motivate individuals to shop online. Further, the problem(s) faced by both countries in shopping online is examined. Descriptive analysis, correlation, Anova and regression analysis were used in assessing and comparing consumers’ online experience. It was found that there is a high prevalent rate (97.5%) of online apparel shopping among Chinese and Ghanaian respondents where the prevalent rate of patronizing online apparel was relatively higher among Chinese youth than the Ghanaian. Convenience, internet usage proficiency and easy access to internet were the main factors that facilitates online apparel shopping among the respondents. Level of income makes the difference in rate online apparel patronization between Chinese and the Ghanaian. On the contrary, level of income, Trust, and Privacy and confidentiality of personal information were found as challenges discourages Ghanaians online apparel consumers likewise Chinese consumers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".