Examining the Importance of AI-Based Criteria in the Development of the Digital Economy: A Multi-Criteria Decision-Making Approach
Bibliographic record
Abstract
As one of the main pillars of global transformation in the contemporary world, the digital economy helps create new economic and business opportunities through new technologies. In addition to improving efficiency and reducing costs, this transformation plays a vital role in the economic growth and development of various countries. Artificial intelligence, as one of the key technologies in the development of the digital economy, has a profound impact on optimizing processes, increasing productivity, and enhancing customer experience. By processing big data and providing advanced analytics, this technology makes economic decisions faster and more accurately and affects various sectors of the digital economy. In this regard, 20 key AI-based criteria in the development of the digital economy were extracted from a review of previous studies and were placed in four general categories. The four general categories include structural, organizational, technological and economic. Hesitant Fuzzy Best Worst Method (HF-BWM) was used to rank the AI-based criteria in the development of the digital economy. “Investing in innovation (C16)”, “Potent processing capabilities (C1)”, “Process automation and intelligence (C11)”, “Identifying growth opportunities (C6)” and “Adapting business models to changes (C7)” ranked one to five, respectively. Managers in the digital economy should pay attention to investing in innovation and strengthening processing infrastructure to exploit new technologies and make more accurate decisions. Process intelligence, identifying new areas of growth and adapting the business model to market changes also help improve efficiency, reduce costs, exploit new opportunities and make organizations stable in the face of rapid changes and increasing competition.
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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".