Managing Erp System Risk in SMEs: A Multiple Case Study
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
ERP systems are increasingly accessible to small and medium-sized enterprises (SMEs). If the potential benefits of these systems are significant, the same applies to the risk associated with their implementation. A number of authors emphasize that IS risk management is most effective when it is initiated at the earliest possible moment in the system's lifecycle, that is, at the adoption phase. But how do SMEs actually manage the risk of ERP implementation during the ERP adoption process? The research objectives are (1) to identify and describe the influence of the SMEs’ context on their implementation risk exposure, and (2) to understand whether and how, within the adoption process, SMEs actually manage the risk of implementing an ERP system supplied by an ERP vendor, with open source software, or through in-house development. In order to do so, four case studies of SMEs having implemented an ERP system were undertaken. The study shows that to manage risk at the adoption stage, SMEs can proceed in a rather intuitive, informal and unstructured manner, that is explicitly based however upon an architecture of basic principles, policies and practices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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