Knowledge assets, innovation ambidexterity and firm performance in knowledge-intensive companies
Bibliographic record
Abstract
Purpose This study aims to build upon resource orchestration theory to theorize and empirically test a model that demonstrates how knowledge assets and innovation ambidexterity trigger a synergy in favor of firm performance. Design/methodology/approach Drawing on a survey of 158 Iranian knowledge-intensive companies, this study uses the partial least squares based on structural equation modeling to test the research hypotheses. Findings The results show that two elements of knowledge assets, namely, structural and relational capital, indirectly affect firm performance through the full mediation of innovation ambidexterity. The findings indicate that human capital has no relationship with both innovation ambidexterity and firm performance. Practical implications This study offers fresh insights into the issue of how organizations can create value from an effective orchestration of various strategic resources and capabilities, including knowledge assets and innovation ambidexterity. Originality/value This study applies resource orchestration theory to concurrently the areas of knowledge resources and organizational ambidexterity to show how innovation ambidexterity plays a role in translating three various knowledge assets into performance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".