The effect of privacy concerns, risk, control, and trust on individuals’ decisions to share personal information: A game theory-based approach
Bibliographic record
Abstract
Abstract The rapid developments and innovations in technology have created unlimited opportunities for private and public organizations to collect, store and analyze the large and complex information about users and their online activities. Data mining, data publishing, and sharing sensitive data with third parties help organizations improve the quality of their products and services and raise significant individuals’ privacy concerns. Privacy of personal information remains subject to considerable controversy. The problem is that big data analytics methods allow user’s data to be unlawfully generated, stored, and processed by leaving users with little to no control over their personal information. This quantitative correlational study measures the effect of privacy concerns, risk, control, and trust on individuals’ decisions to share personal information in the context of big data analysis. The key research question aimed to examine the relationship among the variables of perceived privacy concerns, perceived privacy risk, perceived privacy control, and trust. Drawing on Game Theory, the study explores all the game players’ actions, strategies, and payoffs. Correlation analysis was used to test these variables based on the research model with 418 internet users of e-services in the United States. The overall correlation analysis showed that the variables were significantly related. Recommendations for future studies are to explore e-commerce, e-government, and social networking separately, and data should be collected in different regions where many factors can affect the privacy concerns of the individuals.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".