Antecedents and performance outcomes of advanced manufacturing systems sophistication in SMEs
Bibliographic record
Abstract
Abstract Purpose – In order to deepen one's knowledge and further build theory on the implementation and use of advanced manufacturing systems (AMS) in small and medium‐sized enterprises (SMEs), the present research seeks to explore the following questions: What is the present level of AMS sophistication in SMEs? What characteristics of the SMEs' strategic, organizational and entrepreneurial context are associated with higher levels of AMS sophistication? And what are the operational and business performance impacts of this sophistication for small and medium‐sized manufacturers? Design/methodology/approach – A survey of 248 Canadian manufacturers was used to collect data that were analyzed by structured equation modeling. Findings – AMS sophistication significantly impacts both the operational performance and the business performance of SMEs. Antecedents of this sophistication include the education and experience of the owner‐manager, the strategic orientation of the firm, the type of production, and the commercial dependency of small manufacturers. Research limitations/implications – The nature of the sample and perceptual nature of certain measures impose care in generalizing the results of the study. Future research should examine environmental factors (e.g. environmental uncertainty) and structural factors (e.g. structural complexity) in particular for added explanatory power of AMS sophistication. Practical implications – Small business managers, wanting to increase their firm's manufacturing flexibility, reduce costs, improve quality, and eventually increase profitability, should look at the present level of AMS sophistication in conjunction with their strategic intent. Originality/value – Given the dearth of empirical knowledge in this regard, the present study has contributed to a better understanding of the nature and state of AMS sophistication in small manufacturing firms, and of the antecedents and outcomes of this sophistication.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".