How Perceptions of Information Privacy and Security Impact Consumer Trust in Crypto-Payment: An Empirical Study
Bibliographic record
Abstract
The ever-increasing acceptance of cryptocurrencies has fueled applications beyond investment purposes. Crypto-payment is one such application that can bring radical changes to financial transactions in many industries, particularly e-commerce and online retail. However, characteristics of the technology such as transaction disintermediation, lack of central authority, and lack of adequate regulations may introduce new privacy and security concerns among the users. This coincides with another trend of rising individuals’ concerns pertaining to information privacy and security issues in online transactions. The current paper investigates how consumer trust in crypto-payment, a key determinant of consumer intentions and relational exchanges over the long-term, is formed based on their perceptions towards privacy and security aspects of the technology. Using data from 327 survey participants, the study found that perceived information privacy risk, perceived anonymity, and perceived traceability of transactions are significant determinants of consumer trust in crypto-payment; but their perceptions of information security fraud risk have no significant effect. It also provided support for the hypothesis that perceived trust contributes to consumers’ intention to adopt crypto-payment. The findings highlight the need to enhance consumer understanding and awareness of information privacy and potential security issues in crypto-payment as well as what needs to be done to address consumer concerns in this regard. The paper creates novel insights into the requirements of trust in crypto-payment services and the consequences of consumers’ perceptions of privacy and security in this domain.
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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 | 0.005 | 0.018 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".