Structural Model of Tendency to Use Online Payment Gateway in Online Shopping and Customer Knowledge Management (Case Study: Pasargad Bank)
Bibliographic record
Abstract
Tendency to use online payment gateway in online is influenced by individual characteristics such as risk-taking, internet knowledge, online payment knowledge and risk of customer knowledge management tools used in payment gateway and in this study, these factors have been studied simultaneously in form of a model. In online payment, payment gateways are repeated contact point between service institutions such as banks and their customers that institutions can use this opportunity to attract new customers or increase royal customers. In addition to individual characteristics, customers are facing different tools of knowledge management such as shared databases, document repositories and work applications and these tools can have different effects on risk perception and their willingness to do online shopping using internet payment gateways. In this study, structural equation partial least squares statistical method was used. Also questionnaire was used to collect data in the given population, because customer’s members of the Pasargad bank were accustomed online shopping.In summary, risk-taking has negative and significant impact on the risk is perceived of knowledge management tools and internet knowledge was not known as a factor which have a significant negative impact on the perceived risk, but the impact of internet knowledge on preference to use online payment and online payment rather tendency to use internet payment gateway in online shopping was positive and significant. Also, the perceived risk of customer knowledge management tools has negative and significant correlation with tendency to buy using online payment in online shopping by users. On the other hand, the final conclusions of the research carried out in more detail and also practical suggestions for increasing the effectiveness of managers to use knowledge management tools in the payment gateway are presented.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".