Integrating knowledge management with intellectual capital to drive strategy: a focus on Italian SMEs
Bibliographic record
Abstract
Purpose This study aims to provide empirical evidence on the linkage between knowledge management (KM), intellectual capital (IC), planning effectiveness (PE) and innovation performance in Italian small and medium-sized enterprises (SMEs). Design/methodology/approach Survey data from 172 Italian SMEs was collected through an online questionnaire and analyzed using structural equation modeling (partial least square). Findings Results show that KM practices have a positive direct impact on each IC component which influences PE. Finally, structural capital and PE have a positive direct impact a firm’s ability to innovate. Research limitations/implications For researchers, this paper fills an important gap in the academic literature by conceptualizing and empirically testing the link between IC and PE. The main practical implication of this study is that developing intangible resources is of particular importance for strategic decision-making in SMEs. The focus on Italian SMEs limits the generalizability of the results. Originality/value This study provides empirical evidence on how KM and IC interact and mutually drive PE. Second, results shed light on the importance of IC to enhance a firm’s ability to reach its goals. Finally, the focus on SMEs enriches the extant literature in the field confirming the vital role of KM and IC in managerial decision-making.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".