Knowledge management, problem solving and performance in top Italian firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically test the link between knowledge management practices, problem-solving processes and organizational performance. Design/methodology/approach This study uses survey data from 112 leading Italian companies. To test the structural relations of the research model, we used the partial least squares method. Findings Results show a strong relationship between knowledge management practices and intermediate activities of creative problem solving and problem-solving speed. In addition, creative problem solving has a direct impact on both organizational and financial performances, whereas problem-solving speed has a direct effect only on financial performance. Research limitations/implications The focus on top Italian firms limits the generalizability of results. Practical implications This study provides empirical evidence of the importance of knowledge management practices for problem-solving activities and firm performance. Originality/value The present paper fills an important gap in the extant literature by conceptualizing and empirically testing the relationship between knowledge management, problem-solving processes (creative problem solving and problem-solving speed) and firm performance. This study is the first ever to study these relationships within the Italian context.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".