1 Do Companies ’ Online Privacy Policy Disclosures Match Customer Needs?
Bibliographic record
Abstract
Along with companies ’ increased interest in the use of personal information and the growing concerns of customers, privacy emerges as a critical issue in an e-commerce environment. Although several researchers have examined companies ’ privacy practices using posted privacy policy disclosures, few studies have investigated companies ’ privacy policies against Fair Information Practices (FIP). This study investigates companies ’ privacy policy statements and important privacy policies that individuals want to know against FIP. We examine the privacy policy statements of 136 companies from U.S. and Canada and relate them to the results of a Web-based user survey of 210 respondents. Our findings reveal a difference in companies ’ privacy policies between U.S. and Canada. The Security Safeguards and Use Limitation principles were the two most important companies ’ privacy policies that individuals want to know. The Security Safeguards and Purpose Specification principles were the two most frequently addressed OECD principles in more information-sensitive industries while the Purpose Specification and Openness principles were the two most frequently addressed principles in less information-sensitive industries.Thus, there is a gap between what privacy policies individuals value and what companies in less information-sensitive industries disclose in their privacy policy statements. However, companies in more information-sensitive industries frequently disclose an important privacy policy (i.e., Security Safeguards) that individuals want to know.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".