Adoption and risk of ERP systems in manufacturing SMEs: a positivist case study
Bibliographic record
Abstract
Purpose In order to deepen the knowledge and further advance theory on enterprise resource planning (ERP) implementation in small‐ to medium‐sized enterprises (SMEs), this paper seeks to explore the following question: what can be done to minimize the risk of ERP system implementation, from the adoption stage onwards, in a small manufacturing firm? Design/methodology/approach The research method is based on a positivist holistic single‐case design in order to perform an initial test of a process model of ERP system adoption by SMEs. The unit of analysis selected by purposeful sampling is a small manufacturing business. Findings In attempting to minimize the risk of ERP implementation, the small manufacturing firm applied three principles, eight policies and ten specific practices in adopting ERP. Research limitations/implications The research design, based upon a single‐case study, imposes care in generalizing the results of the study. This design, however, allowed the identification and understanding of the risk of ERP from a managerial/practical standpoint, as opposed to a research/theoretical standpoint. Practical implications In managerial terms, the results show that highly formalized management is not necessary to minimize ERP implementation risk in the context of SMEs. Originality/value Few studies have focused on the adoption process within the ERP implementation cycle. The proposed model, as validated empirically in this study, adds to the understanding of this process in small‐manufacturing firms, especially as regards the minimization of implementation risk from the adoption stage onwards.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".