Knowledge assets, capabilities and performance measurement systems: a resource orchestration theory approach
Bibliographic record
Abstract
Purpose The pivotal role of knowledge management (KM) and its extensive implications have been debated in the academic literature with insufficient focus on its link to particular organizational control mechanisms such as performance measurement systems (PMS). To bridge this gap and building on resource orchestration theory, this paper aims to investigate the relationships between KM factors, PMS and corporate performance. Design/methodology/approach Based on a survey data set of 92 listed companies in Iran, the framework and hypotheses were tested using structural equation modeling (SEM) based on partial least squares (PLS). Findings The SEM-PLS results indicate that knowledge assets are significantly associated with both PMS and corporate performance while knowledge process capabilities (KPC) are not significantly associated with PMS and corporate performance. This study also shows that PMS mediates the relationship between knowledge assets and corporate performance. Practical implications The results suggest that the use of appropriate management control systems plays an effective role in synchronizing, aligning and orchestrating a company’s various knowledge resources, which, in turn, can lead to superior overall performance. Originality/value Building on a unique synthesis of resource orchestration theory and the knowledge-based view of the firm, the results of this study provide the first empirical evidence on how PMS intervenes in the relationship between knowledge resources (knowledge assets and KPC) and corporate 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".