Understanding Trust, Privacy and Financial Fears in Online Payment
Bibliographic record
Abstract
In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.
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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 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".