Trends in IT Strategy Implementation: A Systematic Review Across Education and Industry (2000–2022)
Bibliographic record
Abstract
The rapid and global development of digital technology or digital transformation has encouraged various business sectors to adapt to the field of information technology.The education and industrial sectors are interrelated and need to adapt to developments in digital technology where the eduacton sector is the supplieer of human resources and the industrial sector is one of the purposes of the education sector.The balancing prepareness of thos both sector on IT starategy whoulld be conducted for the optimum of busnis process otuput.One of the indicators of the digital transformation adaptation process is the implementation of the information technology (IT) strategy plan.In this paper, a bibliometric analysis is carried out from database Scopus on IT strategy implementation in the industry and education sectors published within the last two decades to get an overview of the responses of these two sectors to the dynamics of digital transformation.Bibliometric analysis" refers to the quantitative assessment of scholarly publications and research activities within a specific field or discipline.The analysis is based on the papers' growth trends and the thematic maps' evolution over each decade.The compilation comprises a total of 41 journals and 23 proceedings.To enhance our comprehension of thematic evolution, the two articles have been categorized into distinct decade periods: 2000-2010 and 2010-2022.The results show that the industrial sector has more publications with an earlier productivity peak than the education sector.The peak productivity of paper in the two sectors occurred before the COVID-19 pandemic.The productivity rate of papers during and after the COVID-19 pandemic was at a reasonably low value, which can be interpreted as an indicator of readiness for the pandemic events that occurred and their effects.The distribution of the thematic maps of the two sectors is different, with the industrial sector having more variables than the education sector.Industrial thematic objects are scattered in all quadrants, while the education sector has been concentrated in quadrants 2 and 3 in the last decade.The thematic objective's distribution indicates the dynamics of the challenges in implementing the information technology strategy for both sectors over the next two decades.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.042 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".