MAPPING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MANAGEMENT AND COMMERCE
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of Artificial Intelligence (AI) in commerce and management, focusing on scholarly publications indexed in the Web of Science (WOS) database between 2019 and 2023. AI has emerged as a pivotal research area; however, few studies have addressed its bibliometric dimensions. This study aims to bridge this gap by evaluating the academic landscape and comparing AI research to assess its impact on decision-making in business management. Data for the analysis was collected from WOS, and performance analysis and science mapping were conducted using R-Studio and MS Excel. The findings highlight AI’s growing role in enhancing business functions, including decision-making, process optimization, and innovation. Key themes include "performance" (9%), "innovation" (6%), and "integration" (3%), reflecting AI’s potential in improving organizational efficiency and competitiveness. The research reveals an annual growth rate of 9.05%, with a peak in 2022, and underscores the significant influence of international collaboration, particularly in smaller countries such as Canada and Australia. The study identifies the USA and China as leading contributors and highlights the concentrated influence of a few prolific scholars and journals. The analysis concludes that AI is an indispensable tool for enhancing agility and adaptability in business management, positioning it as a strategic asset in a rapidly evolving global marketplace.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.056 | 0.117 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".