Performance measurement of AMT: a cross‐regional study
Bibliographic record
Abstract
Purpose To examine the relationship of performance in advanced manufacturing technologies (AMT) to the levels of AMT investments and planning and implementation activities in two regions of America: Anglo‐Saxon (USA and Canada) and Hispanic (Mexico and Costa Rica). Design/methodology/approach Survey methodology was employed to collect data. The instrument was translated into Spanish for administration in the Hispanic region. Exploratory factor analysis was used to establish discriminant validity of the constructs under investigation. Through multiple regression analysis, predictors for two types of performance (organizational and operational) were examined. Findings Both types of performance are reasonably predicted by the AMT investment and planning and implementation factors. Performance predictors are different between the two regions. Research limitations/implications There are limitations common to survey research (e.g. subjective perceptions and respondent bias). Also, results depart from the literature in terms of the predictors for operational and organizational performance. This can be due to other complex relationships among the variables not identifiable by regression analysis. Future work should address this by using more sophisticated statistical tools such as structural equation modeling. Originality/value The study can help managers understand the factors leading to a successful AMT implementation. This is one of the first studies on AMT in developing countries; and as such, it should encourage more research in these countries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".