Knowledge management and organizational performance: an exploratory analysis
Bibliographic record
Abstract
Purpose The purpose of this paper is to report the results of an exploratory investigation of the organizational impact of knowledge management (KM). Design/methodology/approach A search of the literature revealed 12 KM practices whose performance impact was assessed via a survey of business organizations. Findings KM practices were found to be directly related to organizational performance which, in turn, was directly related to financial performance. There was no direct relationship found between KM practices and financial performance. A different set of KM practices was associated with each value discipline (i.e. customer intimacy, product development and operational excellence). A gap exists between the KM practices that firms believe to be important and those that were directly related to organizational performance. Research limitations/implications The majority of the research constructs were formative, thus improving the measurement of KM practices will prove vital for validating and extending these findings. The findings were based solely on organizations from North America and Australia and may not reflect KM practices in other geographic, economic or cultural settings. Practical implications This study encourages practitioners to focus their KM initiatives on specific intermediate performance outcomes. Originality/value The paper examines the relationship between KM practices and performance outcomes. It was expected that a direct relationship between KM practices and organizational performance would be observed. It was also expected that organizational performance would mediate the relationship between KM practices and financial performance. These expectations were supported. KM practices showed a direct relationship with intermediate measures of organizational performance, and organizational performance showed a significant and direct relationship to financial performance. There was no significant relationship found between KM practices and financial 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".