Sharing is caring: Designing incentive rebate strategies for information‐sharing alliances
Bibliographic record
Abstract
Abstract Information security plays a crucial role in organizational governance and management, and information‐sharing alliances (ISAs) have emerged as effective platforms for the secure and controlled sharing of information security knowledge. Despite their potential, many ISAs face financial and operational challenges, including inadequate pricing policies and insufficient incentives for information sharing. This study addresses these challenges by proposing a modeling framework for the fee rebate strategies that ISAs can deploy to motivate effective information sharing. Taking into account the economic implications of both information sharing and information security technology investment, we propose two ISA‐based pricing rebate strategies for information sharing: the split‐return rebate strategy and the swap‐return rebate strategy. Analytical and numerical analyses are conducted to demonstrate the dynamics in different ISA settings under these pricing rebate strategies. The results suggest that in addition to firm size, the price for participating in sharing should be adjusted based on each participating firm's technology investment level, its information‐sharing level, and the marginal cost of information sharing. Also, by focusing on various information‐sharing environments, the study identifies specific conditions under which unfair sharing practices are likely to occur.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".