Tinjauan Manajemen Modal Kerja: Analisis Bibliometrik dan Prospek Masa Depan
Bibliographic record
Abstract
Main Purpose - The aim of the research is to analyze trends, patterns, and future directions of working capital management using a bibliometric approach. Method - This study employed a bibliometric analysis method utilizing publications obtained from the Scopus database. The search process was conducted based on keywords, field of study, document type, language, and open access, resulting in 88 final publications for analysis. Main Findings - Research on working capital management continues to grow, focusing on business and management, economics, econometrics, and finance. Developing countries like Indonesia and China are leading research contributions, while developed countries like France, Canada, and others are making new contributions. Theory and Practical Implications - Theoretically, this research enriches academic understanding of the evolution of working capital management studies through a bibliometric approach. Practically, it provides direction for academics, practitioners, and policymakers to develop evidence-based, collaborative, and contextualized working capital management strategies addressing global change. Novelty - The novelty of this research lies in the use of bibliometric analysis to comprehensively map the trends, patterns, and directions of global development of working capital management research in the 2009–2025 time period, which has not previously been widely done in financial studies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".