Investment Models for Enterprise Architecture (EA) and IT Architecture Projects within the Open Innovation Concept
Bibliographic record
Abstract
Information technologies (IT) architecture and infrastructure is a significant cost item, especially for enterprises with complex production infrastructure and equipment that require automated and digital devices to collect and process primary data on technological and production processes. Most investment models for enterprise-wide development projects usually do not take into account the automation’s costs, including the design and implementation of information systems. The Enterprise Architecture (EA) paradigm has been proposed to bridge the gap between the business and the IT sector. The study aims to develop investment models for projects for the implementation and development of EA solutions, including IT architectures that eliminate the shortcomings of existing approaches. The research methodology is based on the analysis of published approaches to investment models for projects creating and developing EA, IT architectures with the identification of their advantages and limitations, and on the analysis of IT investment assessment practices in Russian infrastructure-intensive companies. As a result, investment and appraisal models are proposed that have advantages associated with the ability to calculate the effect of an integrated approach to the implementation of IT solutions, a more accurate calculation of an investment project cost by taking into account the IT system’s cost, a reduction in the investment cycle of development and implementation of architectural solutions, including physical and IT component.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".