Bibliographic record
Abstract
With the advent of e-commerce, the potential of new Internet technologies to mislead or deceive consumers has increased considerably. This paper extends prior classifications of deception and presents a typology of product-related deceptive information practices that illustrates the various ways in which online merchants can deceive consumers via e-commerce product websites. The typology can be readily used as educational material to promote consumer awareness of deception in e-commerce and as input to establish benchmarks for good business practices for online companies. In addition, the paper develops an integrative model and a set of theory-based propositions addressing why consumers are deceived by the various types of deceptive information practices and what factors contribute to consumer success (or failure) in detecting such deceptions. The model not only enhances our conceptual understanding of the phenomenon of product-based deception and its outcomes in e-commerce but also serves as a foundation for further theoretical and empirical investigations. Moreover, a better understanding of the factors contributing to or inhibiting deception detection can also help government agencies and consumer organizations design more effective solutions to fight online deception.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".