A profile of ERP adoption in manufacturing SMEs
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
Purpose This paper seeks to build and validate a typological profile of manufacturing small to medium‐sized enterprises (SMEs) in regard to their eventual adoption of an enterprise resource planning (ERP) system, based on the predisposition of their environmental, organizational, and technological context. Design/methodology/approach Provides cluster analysis of secondary questionnaire data obtained from a benchmarking database of 356 Canadian manufacturing SMEs. Findings Three types of SMEs were obtained: 140 “internally predisposed” SMEs, 60 “externally predisposed” SMEs, and 156 “unfavourably disposed” SMEs. Originality/value Provides a valid framework for analysis that can serve ERP vendors and consultants, as well as SME owner‐managers, the first to better target their offer of products/services, and the second to better position their firm before contemplating the implementation of an ERP system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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