The effect of supplier development initiatives on purchasing performance: a structural model
Bibliographic record
Abstract
Purpose Supply chain management is an increasingly important organizational concern, and proper management of supplier relationships constitutes one essential element of supply chain success. However, there is little empirical research that has tested the effect of supplier development on performance. The main objective is to analyze the effect of supplier development practices with different levels of implementation complexity on the firm's purchasing performance. Design/methodology/approach Three supplier development constructs were defined: basic supplier development, moderate supplier development, and advanced supplier development. Three structural models were hypothesized and tested using structural equation modeling through field research on a sample of 306 manufacturing companies in Spain. Findings Identified important interrelationships among the various supplier development practices, basic, moderate, and advanced. Also indicated that the implementation of supplier development practices significantly contributes to the prediction of purchasing performance. Research limitations/implications The use of a single key informant could be seen as a potential limitation of the study. The study was a cross‐sectional and descriptive sample of the manufacturing industry at a given point in time. A more stringent test of the relationships between the different levels of supplier development and performance requires a longitudinal study, or field experiment. Practical implications This study focused on supplier development practices and revealed how involving suppliers in supplier development activities is important and may help buyers to increase their purchasing performance. The findings from the structural analysis should provide practicing managers with insights on how these practices and their benefits are related in terms of purchasing performance, thus affecting their ability to make better sourcing decisions. Originality/value Fills an important gap in the purchasing literature with respect to the area of supplier development. While there is much written about supplier development based on conceptual and case study research, this study is unique in that it is the first attempt to empirically model the relationships between different levels of supplier development and their impact on purchasing performance using a comprehensive set of practices.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".