Sharing is caring: Designing incentive rebate strategies for information‐sharing alliances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it