Do intellectual capital and environmental uncertainty affect firm performance? A mediating role of value chain
Bibliographic record
Abstract
A value chain plays a crucial role in increasing production efficiency due to business uncertainty and high competition between business entities. Of these, the business must carry out the activities effectively and efficiently to achieve maximum profit. However, the study that focuses on the moderating role of the value chain in its relationship to firm performance is still limited. Thus, the present study aims to examine the effect of intellectual capital and environmental uncertainty toward firm performance and the mediating role of the value chain in the relationship between intellectual capital and environmental uncertainty to firm performance. This study was designed using a quantitative approach through an online survey. A total of 207 staff from non-financial state-owned enterprises consists of 10 clusters and 76 companies. The data were analyzed using the Structural Equation Modeling with Partial Least Square method (SEM-PLS) and assisted by statistical software, namely SmartPLS 3.3.3. The result indicated that intellectual capital and environmental uncertainty have a significant effect on firm performance. Also, this study found that a value chain moderates the relationship of intellectual capital and environmental uncertainty toward firm performance. In conclusion, this study has successfully examined the effect of intellectual capital and environmental uncertainty on firm performance—also, the role of a value chain in the relationship of studied variables. In addition, the findings of this study showed that a value chain is an important tool for companies to improve their business 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 | 0.002 | 0.015 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".