The impact of blockchain technology on financial transparency: A study of SMEs in emerging economies
Bibliographic record
Abstract
With the rapid advancement of blockchain technology, SMEs face an opportunity to leverage decentralized ledger systems to address longstanding challenges related to financial transparency. This study aims to assess the implications of blockchain adoption for SMEs operating in the Jordanian context, focusing on its potential to improve accountability, trust, and efficiency in financial operations. Drawing on quantitative research methods, this paper examines the current state of financial transparency using a structural equation modeling approach of 215 surveys. The findings indicated that there is a positive impact of block-chain technology on enhancing financial transparency. The findings of this research contribute to both academic understanding and practical implications for policymakers, regulators, and SMEs in Jordan seeking to enhance financial transparency through blockchain technology. By shedding light on the positive impact of blockchain on financial transparency in the Jordanian SME sector, this paper aims to inform strategic decision-making and stimulate further research in this emerging field. Ultimately, it underscores the transformative potential of block-chain technology in promoting accountability, trust, and eco-nomic development among SMEs in Jordan and beyond.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".