Bibliographic record
Abstract
The main purpose of the current study is to determine the influence of convenience risk, product risk, and perceived risk on online shopping with the moderating effect of attitude in Pakistani context.In these days, online shopping is rapidly increasing all over the world and it gives confidence to scholars to determine what factor at the time of shopping online consumers see.The research model of this study is developed on the basis of theoretical background to investigate the influence of convenience risk, product risk, and perceived risk on online shopping with the moderating effect of attitude.The data was collected from students who are mostly master degree holders.The data was collected through questionnaire technique by applying convenient sampling technique, and one hundred questionnaires were distributed to students of Gujranwala and Islamabad.Confirmatory factor analysis (CFA) and structural equation modeling (SEM) techniques have been used for statistical analysis.Findings revealed that convenience risk and perceived risk are significantly and negatively associated with online shopping.Moreover, attitude is significantly and positively associated with online shopping.In contrast, product risk is insignificantly associated with online shopping.Furthermore, findings elucidated that attitude significantly moderates the relationship between convenience risk, product risk, and online shopping.In contrast, findings revealed that attitude does not significantly moderate the relationship between perceived risk and online shopping.Limitations of the current study and direction for future studies are delineated at the end of paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.946 | 0.927 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".