Emerging Trends in Digital Transformation and Information Systems by Bibliometric Analysis in the United States
Bibliographic record
Abstract
This bibliometric analysis examines the evolving trends of digital transformation (DT) and information systems (IS) in U.S. businesses. It explores how technology—focused on strategy, efficiency, innovation, and customer engagement—is reshaping organizations and workplaces. Using a PRISMA-based systematic review and data from Scopus (2016–2026), the study applies the Bibliometrix R package to assess publication patterns. Results show significant growth, with 2,692 documents reflecting a 43.58% annual increase and 18.39% involving international collaboration. Key themes include AI/ML integration in business processes, digital sustainability, and IS as a strategic driver for business model evolution. U.S. businesses are increasingly aligning digital transformation with sustainability goals. This study addresses a key research gap by offering detailed insights into DT and IS impacts on operations and sustainability practices. It underscores the need for integrated socio-technical strategies, responsible data governance, and global collaboration to foster innovation and bridge digital divides.
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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.157 | 0.263 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".