The effects of knowledge creation process on organizational performance using the BSC approach: the mediating role of intellectual capital
Bibliographic record
Abstract
Purpose Knowledge is a key success factor in achieving competitive advantage in the current fast-paced and uncertain economic environment. Several studies in the literature have analyzed the relationship between knowledge creation (KC) and organizational success; however, the mechanisms by which KC leads to accumulation of intellectual capital (IC) and thereby affects various dimensions of organizational performance are understudied. The purpose of this paper is to examine how KC and IC and their relationship influence key dimensions of organizational performance. Design/methodology/approach A research model was developed and tested based on the literature in the areas of KC, IC and organizational performance. This study uses a survey sent to companies in an intensive knowledge-based industry. The balanced scorecard (BSC) approach was used to measure the key dimensions of organizational performance. Findings The results from structural equation modeling (SEM) on 470 completed questionnaires received from the pharmaceutical companies in Iran reveal that KC activities lead to the accumulation of organizational IC and IC has a crucial and positive impact on the BSC. Furthermore, the results from the path analysis indicate that IC mediates the effects of KC on the BSC. Practical implications The findings of this study contribute to the extant literature on the relationship between knowledge and organizational performance by demonstrating that knowledge and KC lead to performance when organizations utilize KC activities and leverage them to accumulate IC. Once used effectively, IC will result in a better performance in the knowledge-intensive environments. Originality/value This is the first study that investigates how KC contributes to firm performance by incorporating the mediating impact of IC on the BSC. The proposed model and results will help organizations to identify the mechanisms through which KC initiatives improve organizational 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".