Risk-based efficiency assessment of information systems
Bibliographic record
Abstract
The implementation of information systems is aimed at improving the financial performance of a company, creating a transparent reporting system and improving many other competitive factors. However, the acquisition of these benefits does not negate the complexity of making a decision whether or not to implement a particular IT project. The total cost of ownership of the information system throughout the life cycle is usually not considered in comparison with the expected benefits from the use of the system, due to the uncertainty of such benefits. Comparative certainty of approaches and methods is present only in terms of costs, both for a priori (planned) and a posteriori (actual) assessment. It is possible to determine both capital and operating costs accurately enough. Indirect definition of the positive influence of an information system on the activity of the organization also seems possible. However, there are currently no generally recognized methods for analyzing the expected positive effect of an IT project. At the same time, large companies, in accordance with the requirements of the respective regulators and / or due to internal management considerations, build a risk management system to determine the level of capabilities, losses and to prevent adverse events. This study considers the feasibility of an approach to analyze the effectiveness of the implementation of the information system on the basis of the company’s risk reduction, leading to a decrease in economic benefits. It takes into account the internal risks of the information system that occur during the installation of the system, its operation and the termination of work with the system.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".