MULTIPLE CRITERIA ANALYSIS FOR EVALUATION OF INFORMATION SYSTEM RISK
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
Information technology (IT) involve a wide set of risks. Enterprise information systems are a major developing form of information technology involving their own set of risks, thus creating potential blind spots. This paper describes risk management issues involved in enterprise resource planning systems (ERP) which have high impact on organizations due to their high cost, and their pervasive impact on organizational operations. Alternative means of acquiring ERP systems, to include outsourcing to application service providers (ASPs) are available. But outsourcing ERP involves many risks that are often overlooked. After identification of typical risks involved with representative alternative forms of ERP, multiple criteria analysis is proposed as a useful tool for tradeoff analysis in this selection decision. SMART is compared with popular approaches such as DEA and PCA- based DEA. A demonstration of how multiple criteria analysis can be applied in the international ERP alternative selection decision is given by including outsourcing to China and South Korea.
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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 | 0.081 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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