Bibliographic record
Abstract
Purpose The purpose of this paper is to propose the use of the tacit knowledge index (TKI) to assess the level of tacit knowledge within firms and its effect on firm performance. Design/methodology/approach A sample of 108 US and Canadian firms that are using knowledge management was surveyed to determine each firm's TKI. This measure includes both the degree of usage and the tacitness of the knowledge management method. Regression and correlation were used to statistically analyze the innovation and financial outcomes. Findings Significant relationships were found between a firm's level of TKI and the firm's innovation performance. Less clear is the relationship between a higher TKI and financial measures. Research limitations/implications This research gives managers a way to structure their use of knowledge management methodology and use of resources in a way that may maximize performance, either as stand alone systems or as part of the Balanced Scorecard. Practical implications The use of this research could greatly reduce the uncomfortable gut feeling that many managers have in funding so‐called soft tacit‐based knowledge management systems rather than invest in easier to assess hardware systems. Originality/value This pioneering research develops tacit knowledge as a measurable quantity and links this metric to firm 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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".