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.
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".