A Comprehensive Model of Perceived Risk of E-Commerce Transactions
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
Perceived risk is an important construct in e-commerce research, but it has not been approached in a manner sufficiently systematic, comprehensive, or detailed to be understood along multiple dimensions instructive for information systems designers. This paper fills the gap by proposing a model of perceived risk based on a well-established marketing theory of risk. It identifies events that expose consumers to harm in e-commerce transactions and measures the dimensions of perceived risk with rigorously developed formative indicators that incorporate the almost unlimited range of unwanted events of potential concern to consumers. This risk construct is placed in a nomological network and tested through an on-line field study of 411 participants aggregated with structural equation modeling. Test results show that the construct e-commerce transaction perceived risk is an aggregate factor with three dimensions: risk of functionality inefficiency, risk of information misuse, and risk of failure to gain product benefit.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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