The confluence of knowledge management and management control systems: A conceptual framework
Bibliographic record
Abstract
Purpose This study seeks to synthesize theories from the knowledge‐based view (KBV) of the firm and management control systems (MCS) to explore how organizations leverage MCS in order to support their knowledge management (KM) agenda. Design/methodology/approach The core premise of the “fit‐as‐mediation” view holds that knowledge factors may trigger positive changes in the design and usage of certain organizational mechanisms. This, in turn, can expedite information processing and thereby deliver more value to organizations. Findings Drawing upon the KBV of the firm and Simons' levers of control framework, the conceptual model of the study shows that the usage of particular organizational control systems, that is, the balanced use of MSC, can play a role in translating KM into improved performance. More precisely, the model indicates how KM is indirectly related to organizational performance through the mediating influence of the balanced use of MCS. Practical implications The paper may provoke practical courses of action by highlighting the importance of using a balanced and comprehensive MCS in order to support KM strategies and initiatives. Originality/value Based on a unique synthesis of the KBV and the fit‐as‐mediation notion of contingency view, this study provides new insights into the association between KM and organizational performance. This is the first study that introduces a mediating effect of the balanced use of MCS between KM and organizational 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".