ERP implementation at SMEs: analysis of five Canadian cases
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the critical success factors (CSFs) of enterprise resource planning (ERP) system implementation in small and medium‐sized enterprises (SMEs). Design/methodology/approach Five case studies of Canadian SMEs were conducted. They included interviewing individuals from five roles at each organization and gathering project documents. Following an evaluation of each project's success (within‐case analysis), cross‐case analysis was conducted to elicit influential and distinctive factors. Findings Factors were identified that appeared to explain variation between successful and unsuccessful implementations at SMEs, besides factors that appeared to be innovative or counter‐intuitive in light of the established literature. Research limitations/implications The study reinforces the need for more research that is focused on SMEs. All cases were of Canadian SMEs with either a manufacturing or distribution focus, potentially limiting the generalizability of findings to other industries or countries. Practical implications By identifying relevant CSFs for SMEs, managers can better prioritize implementation efforts and resources to maximize success of ERP implementations. Originality/value The paper appears to be one of the first studies to focus on the CSFs of ERP implementation at SMEs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".