Customer Satisfaction Towards Online Shopping by Empirical Validation of Self-Determination Theory
Bibliographic record
Abstract
The chapter aims at understanding the predictors of customer satisfaction with online shopping in India by using self-determination theory. This research validates perceived enjoyment, social influence, social media interactions, reverse logistics, and pay-on-delivery (POD) mode of payment as new predictors of customer satisfaction in online shopping. 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 modelling (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 findings of the chapter revealed that social influence, reverse logistics, and POD mode of payment had a significant positive impact on customer satisfaction. Perceived enjoyment emerged as the strongest predictor of online shopping satisfaction. In contrast, social media interactions emerged as non-significant.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| 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; both teacher heads 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".