Determining the impact of quality management practices and purchasing‐related information systems on purchasing performance
Bibliographic record
Abstract
Purpose Many studies claim that the implementation of quality management practices and specific information systems can help organizations to improve performance. The objective of this article is to provide insights into current quality management and information systems theory and practice in the purchasing function and their impact on purchasing performance. Design/methodology/approach Hypotheses derived from the key features of quality management practices in purchasing (QMPP) and related information systems (IS) practices presented by previous authors are tested using Structural Equation Modelling through field research on a sample of 306 manufacturing companies in Spain. Findings Findings from this study indicate that there is significant evidence to support the hypothesized model in which QMPP has a direct impact on related IS practices and purchasing performance, as well as an indirect impact on purchasing performance mediated through IS. Research limitations/implications Use of a single key informant is a possible limitation as opposed to information directly obtained from actual suppliers and internal customers. Also a more stringent test of the relationship between QMPP, IS and purchasing performance requires a more protracted time‐span rather than a singlular point in time. Finally, future research could include SRM, ERP, MRP, etc. in the purchasing department Practical implications A survey of QMPP and IS practices in manufacturing suggests how firms and other organisations should focus their investments to improve purchasing performance. Originality/value While many researchers have studied information systems and total quality management operations strategies individually, the relationship between the adoption of quality management practices in purchasing and purchasing‐related information systems and QMPP's effect on purchasing performance has not yet been analyzed.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.010 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".