Bibliographic record
Abstract
Purpose This paper aims to tie together insights from the body of research on knowledge management (KM) and management accounting control systems to propose a conceptual model in which performance measurement systems (PMS) can play a role in translating knowledge resources into enhanced performance. Design/methodology/approach The underlying assumption of the “fit-as-mediation” approach signifies that knowledge features can play a role in the determination of the structure and implementation of particular managerial processes and this, in turn, may support information processing and lead to desirable results within organizations. Findings Synthesizing theory from performance measurement and the knowledge-based view of the firm, the paper’s analysis and discussions elucidate how the implementation of an overarching PMS, i.e. diversity of measurement, could translate the knowledge-related factors, i.e. knowledge resources and knowledge process capabilities, into enhanced performance. In particular, the proposed model shows that a comprehensive PMS plays an intervening role between KM and organizational performance. Research limitations/implications The proposed model may inspire a new research agenda to show how knowledge initiatives are managed and measured in organizations and how they are properly aligned with specific managerial processes to deliver real value. Practical implications Drawing upon the conceptualized associations among KM, PMS and organizational performance, this paper recommends some practical guidelines by highlighting the importance of PMS whereby organizations may reap maximum benefit from their KM initiatives. Originality/value This paper sheds new light on the links between KM and organizational performance, and it appears to be the first study to propose an intervening effect of PMS between KM and organizational performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".