Developing a Model of Knowledge Transactions: A Critical Review of Background Theories
Bibliographic record
Abstract
Interactions between companies, crucial for economic success and knowledge advancement, involve significant exchanges of information and knowledge beyond mere economic transactions. Understanding how businesses can leverage these knowledge transactions (KTs) with trading partners is vital for both Knowledge Management (KM) research and practice. This entails identifying the knowledge essential for fruitful trading relationships, determining how to derive value from these exchanges, deciding what knowledge should be protected or shared, and developing value-adding strategies for knowledge exchange. To address these questions, this paper critically examines possible theoretical foundations for a KT model. It reviews nine notable KM models to assess the insights they provide (or do not provide) into knowledge exchange mechanisms both within organizations and between trading partners. These models provide some fundamental insights, but also have limitations, especially in addressing the "economic value" of knowledge exchanges.The study highlights the need for a comprehensive and effective KT model that positions knowledge exchanges in trading as a core, value-adding component of economic activities and business strategies. After this preliminary review of existing KM models, it suggests a new KT model, and indicates the need for further development towards a more encompassing approach.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".