The Impact of Knowledge Transfer on Investment in Knowledge Creation in Firms†‡
Bibliographic record
Abstract
ABSTRACT Knowledge is key to success in the modern business landscape. Firms invest billions of dollars every year in knowledge management systems, which commonly use artificial intelligence to allow within‐firm knowledge transfer to occur automatically. Despite this investment, these systems often fall short of producing expected results. Using psychology theory on goal dilution, we argue that a potential cause of the failure is that the prospect of knowledge transfer has a negative effect on knowledge creation. We further propose a mechanism to mitigate that effect. Specifically, we predict that the negative effect of knowledge transfer on knowledge creation will be mitigated when the linkages among firm‐ and unit‐level goals are communicated. We conduct an experiment and find that while, as predicted, the prospect of knowledge transfer has a negative effect on knowledge creation when the linkages among firm‐ and unit‐level goals are not communicated, it has the predicted positive effect when the linkages among firm‐ and unit‐level goals are communicated due to increased goal congruence. Additional analyses provide support for our underlying theories. Our results suggest that firms can adopt and communicate strategic performance measurement systems to improve the knowledge creation in a firm.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".