Should I Keep a Secret? The Effects of Trade Secret Protection Procedures on Employees' Obligations to Protect Trade Secrets
Bibliographic record
Abstract
Organizations' trade secrets (which can be chemical formulae, recipes, customer files, machinery designs, or many other types of information) are often valuable, enduring sources of competitive advantage. In this study, the influence of organizations' formal efforts to protect trade secrets on employees' beliefs about their obligations to protect those secrets was investigated. Quantitative and qualitative data were gathered by means of survey interviews with 111 employees of two high-tech organizations. Employees' obligations were influenced by their levels of familiarity with, and their perceptions of the enforcement of, two types of trade secret protection procedures (TSPPs): trade secret access restriction procedures (ARs) and trade secret handling procedures (HPs). Employees' levels of familiarity with ARs were negatively related to their felt obligations to protect trade secrets, but the opposite was true for HPs: Employees' levels of familiarity with HPs were positively related to the obligations they felt to protect trade secrets. For both types of TSPPs, the relationship between familiarity and felt obligations was moderated by employees' perceptions of the degree to which the TSPPs were enforced.
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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 | 0.023 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".