Impact of Organizational Culture on Knowledge Management Process in Construction
Bibliographic record
Abstract
One of the key global pressures of knowledge management practice is knowledge acquisition, creation, sharing,storing and dissemination. The global business is reflecting a throng of culture, leadership and culturalupbringings which warrant bringing into line consistent alterations in management of knowledge because ofdiversity of workforce in construction organization. Theoretically, the study predicts the empirical role of culture(managerial learning and trust) with reference to knowledge management process. This paper presents aknowledge management (KM) model that comprises a set of KM hypothesis model and measurement models forunderstanding and applying these KM models to boost the application of KM in the construction organization.76 private construction organization was investigated with 323 questionnaire surveys. A hypothesized model ofKM process and culture was tested using structural equation modelling approach and a proposed model wastherefore developed. Likewise, all fit indices for KM process and factor loadings shows the significant impact ofculture on KM process, leading to a thrifty model achievement. The study shows that culture demonstrated 0.73significant influence on the knowledge management process. The analysis revealed that managerial learning andtrust were key factors that impact positively on KM process within the construction organization underinvestigation.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".