Control Mechanism of Identity Theft and Its Integrative Impact on Consumers' Purchase Intention in E-Commerce
Bibliographic record
Abstract
There are many collection and application sources of identity theft. The Internet is one of the vulnerable medias for identity theft and is used, especially, as an application source of identity theft. This current chapter has twofold objectives. As the first objective, it develops a conceptual framework to prevent/control identity theft of E-Commerce (EC) in conjunction with different sources if identity theft. From this framework and shedding light on the recent literature of sources of identity theft, the authors identify global laws, controls placed on organizations, publications to develop awareness, technical management, managerial policy, risk management tools, data management, and control over employees are the potential measuring items to prevent identity theft in EC. All EC organizations are struggling to control identity theft. This chapter argues that control mechanism of identity theft has both positive and negative impact on EC. This chapter sets its second objective to explore the integrative effect of overall identity theft control mechanism on consumer trust, the cost of products/services, and operational performance, all of which in turn contribute to a purchase intention using E-Commerce (EC). A case study in banking sector through a qualitative approach was conducted to verify the proposed relations, constructs, and measuring items.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".