POD Mode of Payment, Return Policies and Virtual-Try-on Technology as Predictors of Trust: An Emerging Economy Case
Bibliographic record
Abstract
This study examines virtual-try-on technology (VTO), return policy, and pay-on-delivery (POD) mode of payment as predictors of trust in an online context. The study further investigates trust as an influencer of repurchase intentions while positing habit as a moderator of the relationship between trust and repurchase intention. Data was collected through a self-administered and structured questionnaire targeting online shoppers in North Indian states. A sample of 424 online shoppers was considered in this research. Structural Equation Modeling (SEM) was used to evaluate the constructs. CFA was applied to calculate validity and composite reliability. To examine the hypothesized relationships, path analysis was carried out. The results confirmed that both return policies and POD are significant predictors of trust. Interestingly, VTO has no significant effect on trust, while habit strengthens the positive relationship between trust and repurchase intentions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".