From clicks to carts: Examining the role of perceived flow and customer satisfaction as mediators in social media e-commerce
Bibliographic record
Abstract
Customer satisfaction is the bedrock of e-commerce. This study identifies the factors and consequences of the perceived flow of consumers with shopping websites. The research scrutinizes the association linking purchase intentions arising from customer satisfaction, website quality and perceived flow by incorporating the model of Stimulus Organism Response. The data were collected from online shoppers using purposive sampling targeted through social media. The data from 270 respondents have been analyzed using SmartPLS incorporating the Partial Least Square-Structural Equation Modeling. The assessment showed an important beneficial impact of website usability, their security, functionality, and privacy features on the perceived flow of customers. Furthermore, perceived flow has been found to affect customers’ purchase intentions as well as satisfaction, and lastly, purchase intention is induced by how satisfied customers are with their shopping experience. The findings identify how important perceived flow functions in enhancing purchase intentions and customer satisfaction online. The findings can be noteworthy for online businesses and banks to improve online shopping experience and transactions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".