Effect of IT and quality management on performance
Bibliographic record
Abstract
Purpose The present study aims to draw on operations management and information technology literature to examine the effect of three information technology resources (electronic data interchange (EDI), computer‐aided design and manufacturing (CAD/CAM), and enterprise resource planning (ERP) systems) and three related quality management capabilities (customer and supplier relations, product and process management, and quality data and workforce management) and their effect on a firm's quality performance. Design/methodology/approach Hypotheses derived from the key features of quality management and information technology presented by previous authors are tested using structural equation modeling through field research on a sample of 229 manufacturing companies in Spain. Findings Findings from this study indicate that there is significant evidence to support the hypothesized model in which information technology resources (EDI, ERP systems, and CAD/CAM systems) have a direct impact on related quality management capabilities (customer and supplier relations, product and process management, and quality data and workforce management) as well as an indirect impact on quality performance mediated through quality management capabilities. Originality/value The discrepant findings in the literature suggest the need to identify contingencies that may govern the IT‐performance relationship. This study focuses on the interplay between information technology, quality management, and quality performance.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".