How Perceptions of Information Privacy and Security Impact Consumer Trust in Crypto-Payment: An Empirical Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it