Knowledge intermediation strategies: a dynamic capability perspective
Bibliographic record
Abstract
Abstract This study investigates (i) differences in knowledge intermediation strategies among knowledge and technology transfer organizations (KTTOs) and (ii) the factors that explain such differences. It uses data from 212 Canadian KTTOs. When knowledge delivery capacity (KDC) and knowledge integration capacity (KIC) dimensions of knowledge intermediation are simultaneously accounted for, four categories of KTTOs emerge, namely, (1) knowledge stores; (2) knowledge match providers; (3) knowledge integrators; and (4) knowledge brokers. This heterogeneity results in a differentiation in KTTOs' service delivery strategies. Factors that are conducive to custom-made solutions include (i) increased innovativeness; (ii) higher absorptive capacity; (iii) stronger information search and storage capabilities; (iv) effective customer knowledge management (CKM); and (v) increased networking capabilities. Larger knowledge intermediaries suffer from internal organizational stickiness that prevents them from delivering custom-made services. KTTOs with a low degree of formalization and centralization in decision-making are likely to adopt intermediation strategies aimed at reaching the largest possible number of users. Some managerial and public policy implications are drawn.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".