Managing Erp System Risk in SMEs: A Multiple Case Study
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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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".