Measuring Enterprise Resource Planning (ERP) Software Risk Management for Digital SMEs
Bibliographic record
Abstract
The research aims to analyze risk management in the adoption of Enterprise Resource Planning (ERP).ERP currently widely used, however limited research focus on risk management adoption regarding technological and information system.Digital transformation encourages SMEs to adopt information technology to streamline business processes, especially ERP.Through a comprehensive investigation involving 85 SME owners, the study focuses on ERP risk management.The analysis is conducted by identifying, mapping, and assessing severity.Additionally, data is analyzed using a neural network to explain the satisfaction level of ERP adoption.The research identifies several key risk factors, including vendor stability, data security, system downtime, and inadequate support.Based on research result, risk management underscores the need for robust encryption protocols, access controls, and regular security audits to mitigate the risks of data breaches, unauthorized access, and the compromise of critical business data.The research is reliable in illustrating ERP adoption risks, with a 98.3% correct prediction rate.The novelty of this research lies in its identification and mapping of risk factors in ERP adoption for SMEs, serving as an alternative reference for determining information technology improvements.The research suggests prioritizing thorough due diligence when selecting an ERP vendor and establishing clear and comprehensive Service Level Agreements.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".