Advancements in Management Accounting and Digital Technologies: A Systematic Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This systematic literature review applied bibliometric analysis and science mapping to analyse studies on management accounting and digital technologies. The objective was to gain insights from multidisciplinary perspectives, including topics such as big data, business intelligence and analytics, artificial intelligence, and blockchain. The analysis of 140 articles from Scopus and Web of Science indicated that almost 75% were published in the last five years, due to technological innovations, demand for efficiency, cost control, sustainability, and social responsibility practices. The findings indicated that the conceptual structure of this sample can be categorised into four clusters: artificial intelligence and blockchain, information technology and cloud computing, big data, and business intelligence. Future research should empirically examine AI and blockchain integration in government institutions and small and medium-sized enterprises, and their roles in enhancing social and environmental sustainability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it