Financial and Administrative Management Models for Digital Ventures: A Literature Review
Bibliographic record
Abstract
Financial and administrative management models are crucial to the success of digital ventures, providing practices that optimize resource management and support strategic decision-making in dynamic digital environments. This study presents an original systematic literature review (SLR) following the PRISMA guidelines, analyzing 354 articles extracted from Scopus and Web of Science databases. Bibliometric techniques, including VOSViewer 1.6.19 version and R-Bibliometrix software 4.3.3 version, were used to identify key research themes, emerging trends, and future directions in the field. A notable 114.29% increase in academic output from 2019 to 2024 underscores the growing importance of these management models. The analysis reveals a focus on financial management tools (e.g., Valuation, Discounted Cash Flow models) and administrative models (e.g., RocaSalvatella, INCIPY), while also exploring the challenges and opportunities present in digital environments. The interaction between external variables (resource management, operational efficiency, adaptability, financial planning, technological innovation) and internal variables (market conditions, government regulations, economic trends) is discussed. This study highlights the integration of agile methodologies, such as Lean Startup, and the growing emphasis on digital resilience, organizational agility, and the impact of digital transformation on business models. The theoretical contribution of this study lies in offering a comprehensive framework that synthesizes existing models, highlights key research gaps, and emphasizes the need for future studies on the dynamic interaction between financial planning, technological innovation, and organizational agility. From a practical perspective, the findings provide digital entrepreneurs and managers with valuable insights into implementing financial tools and administrative frameworks that enhance decision-making, while also underscoring the importance of agility, operational efficiency, and market adaptability to navigate digital disruptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".