Privacy in E‐Commerce: Development of Reporting Standards, Disclosure, and Assurance Services in an Unregulated Market
Bibliographic record
Abstract
Abstract Government regulation of financial reporting by publicly listed firms, coupled with a punitive regime for violation of generally accepted accounting principles (GAAP), has been in place in the United States for seven decades. Whether this regime is effective or useful is an open question, especially in the absence of data on the behavior of unregulated economies. Privacy disclosure in e‐commerce is essentially an unregulated environment with some parallels to financial disclosure. A study of privacy standards, disclosures practices, and demand for audits can help accountants and security regulators project the consequences of a competitive regime sans regulation for accounting standards, disclosure and audit practices. In this article we set up a framework for such a study, gather data from the field, and analyze privacy standards, privacy disclosure practices, and the effectiveness of opt‐out practices of 100 high‐traffic e‐commerce Web sites. We observe four diverse sets of privacy standards (TRUSTe, BBB Online, WebTrust, and PWC Privacy) competing in this market, attracting clienteles of their own as reflected in privacy policies and the disclosure of such policies. With a few exceptions, actual disclosure and opt‐out practices correspond reasonably well to stated policies in e‐commerce. There is little evidence that the prevailing competitive regime induces a race to the bottom with respect to privacy standards and disclosures. We explore the implications of these results for the consequences of a competitive regime for regulation of financial reporting.
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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 | 0.030 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".