E-Commerce and Privacy: Exploring What We Know and Opportunities for Future Discovery
Bibliographic record
Abstract
ABSTRACT Electronic commerce (e-commerce) has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction and the risk of negative consequences from providing such information. This requirement and the increased sophistication of companies' personal information gathering have made e-commerce privacy a critical issue and have spawned a broad research literature that is reviewed in this paper. Key research issues and findings are organized, using a framework defined by four key stakeholder groups—companies, customers, privacy solution providers (PSPs), and governments—as well as the interactions among them. The review indicates that the published research on e-commerce privacy peaked in the early 2000s; thus, it has not addressed many of the technological advances and other relevant developments of the past decade. Potential research opportunities for researchers in Management Information Systems (MIS) and Accounting Information Systems (AIS) include: company privacy strategies, operations, disclosures, and compliance practices; customer privacy concerns arising from company practices such as Internet activity tracking, physical location tracking, personal information gathering by social networks, and information exchanges in cloud computing environments; privacy-enhancing technologies, controls, and assurance practices developed by PSPs; and privacy regulations relating to various industries, countries, and cultures. More use of experimental and archival research is encouraged.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.017 | 0.044 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".