Strategy for privacy assurance in offshoring arrangements
Bibliographic record
Abstract
Purpose Offshoring is a common practice to operationalize global business strategies. Data protection and privacy assurance are major concerns in such international arrangements. This paper aims to examine the strategy adopted to ensure privacy assurance in offshoring arrangements. Design/methodology/approach This is a literature review to understand privacy assurance strategies adopted in offshoring arrangements and an exploratory case study of captive offshoring arrangement with onshore location in Canada and offshoring locations in India and Philippines. A comparative analysis of the privacy laws and privacy principles of Canada, Philippines and India has been done. Findings It was found that at the time of migration of process or work to the offshore location, organizations follow a conformist privacy strategy; however, once in business as usual mode, they follow entrepreneur privacy strategy. Privacy impact assessment (PIA) was found to be an important element in resolving the “administrative problem” of an offshoring organization’s privacy assurance strategy. Research limitations/implications The core privacy principles are outlined in the PIA templates; however, the current templates are designed to meet the conformist strategy and may need to be revised to include the cultural aspects, training, audit and information security requirements to plan and deliver on the entrepreneur strategy. Practical implications Offshoring organizations can benefit by planning for entrepreneur privacy assurance strategy at the inception stage. Enhancements to PIA templates to facilitate the same have been suggested. Originality/value Privacy assurance strategy followed by organizations while offshoring has been examined. This paper suggests extending the PIA process so that it covers privacy assurance requirements in offshoring arrangements. The learnings can be used in managing privacy assurance requirements in similar multi-country offshore arrangements.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".