Secrets and knowledge management strategy: the role of secrecy appropriation mechanisms in realizing value from firm innovations
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore in depth the mechanisms that organizations use to keep their innovations secret. This paper examines how, when and why secrecy appropriation mechanisms (SAMs) can enable innovators to appropriate value from their innovations. Design/methodology/approach Building from an extensive literature review of innovation and secrecy, the paper presents a number of implications for theory and research in the form of testable propositions. Findings This conceptualization proposes that SAMs can have both positive and negative effects on a number of organizational dynamics. SAMs involve tradeoffs, and the key to understanding whether they create value to organizations lies in understanding that these tradeoffs exist and the nature of these tradeoffs. Practical implications While most managers recognize the importance of secrecy in innovations, many struggle with the practical challenges of doing so. The paper presents guidance for managers to overcome these challenges. Originality/value This paper adds to previous research that has identified secrecy as an important appropriation mechanism for firms by digging deeper into the details of SAMs and exploring their sources, characteristics and effects.
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.
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 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".