Artificial intelligence and financial decisions: Empirical evidence from developing economies
Bibliographic record
Abstract
Recent technological advancements are endless and have had a profound influence on everyone in every part of life throughout the preceding decades. Artificial intelligence is one such invention that has the potential to change the world. Now, artificial intelligence is being used in almost all commercial operations. Hence, this research attempted to investigate the impact of artificial intelligence dimensions, including natural language processing, machine learning, expert systems, and computer vision on the financial decisions of pharmaceutical companies in Jordan. A cross-sectional approach was used through a comprehensive survey to collect research data from 148 accountants and financial managers in pharmaceutical companies listed on the Amman Stock Exchange with a response rate of 81.3%. The research hypotheses were examined using structural equation modeling of the collected quantitative data. The results indicated that the dimensions of artificial intelligence positively impact financial decisions. Accordingly, companies should spend on building strong artificial intelligence infrastructure and skills. Access to modern artificial intelligence technology, data analysis tools and cloud computing resources are also essential to rationalizing financial decision-making. Besides, Jordan's pharmaceutical sector can overcome these limitations and realize the full potential of artificial intelligence in financial decision-making by solving data privacy issues, encouraging ethical AI re-search, investing in artificial intelligence expertise, and enhancing collaboration.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".