The role of affective leadership in improving firm performance through the integrated internal system and external integration FMCG Industry
Bibliographic record
Abstract
The manufacturing industry always tries to improve performance amid the trade globalization. Demand and supply uncertainty, and increasingly the intense competition, prosecute the presence of an affective leadership to integrate the company's internal and external resources in improving company performance. This research investigates the role of affective leadership in firm performance through an internally integrated system and external integration in FMCG companies. Data collection used questionnaires, designed with a five-point Liker scale, were distributed to 55 fast-moving consumer goods (FMCG) manufacturing companies. The data analysis used the PLS technique utilizing smart PLS software to assess the validity and reliability of the outer model and to examine the hypotheses developed. The results of the hypothesis testing found that affective leadership can improve internal system integration, external integration, and firm performance. Internal system integration has an impact on external integration but is not strong enough to have a direct effect on firm performance. The internally integrated system has an influence on firm performance through external integration. The company's ability to share information with external partners can improve firm performance through demand fulfillment. The study provides a managerial implication on how to enhance firm performance in the context of internal and external system integration. The finding of this research enriches the current studies in supply chain management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".