Building knowledge: developing a knowledge‐based dynamic capabilities typology
Bibliographic record
Abstract
Purpose The purpose of this paper is to synthesize existing knowledge‐based dynamic capabilities research into a single typology for managerial and academic use. Design/methodology/approach Based on the resource‐based and knowledge‐based views, this study conducts a theoretically grounded typology development exercise based on an extensive review of the existing dynamic capabilities literature. Findings The paper identifies seven frameworks presented in the literature that showed some consistency in underlying concepts but conflict in nomenclature and application. Identifying over 80 uses of knowledge‐based dynamic capabilities in the literature review, three complementary dimensions that are common amongst the frameworks are identified and integrated into a consistent typology of eight knowledge‐based dynamic capabilities to encompass the extant literature. Originality/value Addressing fragmentation in the knowledge‐based dynamic capabilities discourse, the paper advances the concept of knowledge‐based dynamic capabilities by organizing the existing literature and frameworks into a comprehensive and consistent typology. Moreover, this integrative typology allows managers and researchers to identify those capabilities in use and the commonalities between them. Finally, the paper identifies a new knowledge‐based dynamic capability that has not yet been identified in any existing framework.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".