Absorption, combination and desorption: knowledge-oriented boundary spanning capacities
Bibliographic record
Abstract
Purpose The purpose of this paper is to theoretically develop and empirically explore knowledge absorption, combination and desorption within and between organizations. Design/methodology/approach On the basis of knowledge-based view and absorptive capacity, the authors have conducted a multiple-case study to develop a theoretically grounded and empirically supported model of intra- and inter-firm knowledge cycles. Findings Firms identify their knowledge gaps and stocks, both tacit and explicit, undertaking efforts to fill the latter and maximize the value of the former. The paper finds that knowledge exploration, integration and exploitation both within the firm and between firms relies on absorptive, combinative and desorptive capacities. Further, as such capacities are organizationally expensive to maintain, firms will often emphasize one capacity over the other and focus either internally or externally to meet organizational goals. Originality/value While there is extensive research into absorptive capacity and some into combinative capacity, there is little empirical investigation of desorptive capacity and none into the integration of the three concepts; this paper seeks to fill that gap. Moreover, the resulting novel integrative model allows managers and researchers to identify the various capacities in use and their applications within the firm and between firms.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| 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".