Bibliographic record
Abstract
This paper addresses the application of financial innovations from the corporate finance perspective. The objective is to identify and prioritize the main types of barriers to the implementation of financial innovations by nonfinancial firms. The motivation behind the study lies in the importance of financial innovations for the firms’ ability to create value. As proven by the extensive literature review, comprehensive studies on financial innovation applications by nonfinancial firms are relatively rare. To cover this cognitive gap, the theoretical argumentation followed by the discussion of results of the empirical research are presented in this paper. The paper provides the results of two-stage survey research, aiming to find opinions of financial managers (end-users) and experts (creators of innovation) on the main barriers to financial innovations in Poland. According to managers, the most important are exogenous barriers, including: (1) Unclear tax and accounting regulations, (2) complex construction of financial innovations, and (3) transaction costs related to their application. On the other side, the experts from financial institutions recognized the greater importance of endogenous factors such as: (1) Lack of sufficient knowledge about financial innovations and (2) the reluctance to change observable in many firms. This study contributes to the ongoing debate on financial innovations by adding the perspective of corporate financial strategy. It also offers insights into the potential actions (at the institutional and individual level) aiming to reduce the barriers and support the implementation of financial innovations by nonfinancial firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".