Bibliographic record
Abstract
Purpose Increased requirements for competitiveness, innovation, quality, flexibility and information processing capability has led a number of small and medium‐sized enterprises (SMEs) to implement advanced manufacturing technologies (AMT). Seeks to explore this. Design/methodology/approach Using a contingency theory perspective, a survey study of 118 Canadian manufacturers was made to determine the performance outcomes of the “fit” or alignment between the critical success factors (CSFs) of operations management in SMEs and their level of proficiency in the use of AMT. Findings It was found that while increased CSF and AMT assimilation levels directly impact operational performance in terms of increased productivity, cost reductions, flexibility, quality, and integration, a mismatch between the two significantly reduces performance. From an information processing view of the firm, it was also found that increased uncertainty in the SMEs' environment leads to increased CSF levels but not to increased assimilation of AMT. Research limitations/implications Common to survey studies, the nature of the sample and perceptual nature of certain measures impose care in generalizing the results of the study. Originality/value Provides information showing that enterprises must increase their ability to manage both manufacturing and information technologies.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".