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.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".