Navigating Financial Risk in the Digital Age: The Mediating Role of Performance and Indebtedness
Bibliographic record
Abstract
In the context of an increasingly digital economy, firms are rapidly adopting technological innovations to bolster financial resilience and competitiveness. However, the quantitative impact of digital transformation on key financial outcomes—specifically performance, indebtedness, and risk—remains underexplored. This study investigates the extent and pathways through which digital transformation influences financial structures and stability. Employing Structural Equation Modeling (SEM) on firm-level survey data, the analysis reveals that digital transformation significantly enhances financial performance (β = 0.538, p < 0.01). Improved performance, in turn, leads to substantial reductions in firm indebtedness (β = −0.591, p < 0.01) and financial risk (β = −0.124, p = 0.021). While digital transformation does not directly affect indebtedness, it mitigates financial risk indirectly through two mediating variables: financial performance and firm indebtedness (mediated effects: β = −0.221 and β = −0.318, respectively; both p < 0.01). These findings underscore the financial value of digital initiatives, highlighting their role in enhancing performance and reducing financial vulnerabilities. The study offers strategic insights for managers and policymakers aiming to leverage digital transformation for financial optimization.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".