Understanding Supplier Selection Criteria: Perspectives from Procurement Professionals in Diverse Industries
Bibliographic record
Abstract
This qualitative study explores supplier selection criteria from the perspectives of procurement professionals across diverse industries. Supplier selection is crucial for organizational efficiency and performance, influenced by factors beyond traditional cost considerations. The research aims to uncover nuanced criteria such as quality assurance, supplier reliability, innovation capability, sustainability practices, strategic alignment, relational dynamics, and risk management strategies. These criteria reflect a shift towards holistic evaluation frameworks that integrate economic, social, and environmental dimensions in procurement decision-making. Methodologically, the study employed semi-structured interviews with 30 procurement professionals, ensuring depth and diversity in perspectives. Thematic analysis was used to identify recurring themes and patterns, illuminating the complexities and strategic importance of supplier selection processes. Findings highlight the strategic role of suppliers in driving innovation, enhancing supply chain resilience, and supporting organizational goals. Moreover, the study underscores the significance of sustainable sourcing practices, ethical considerations, and effective supplier relationships in fostering long-term partnerships and mitigating operational risks. Practical implications suggest that organizations should adopt integrated approaches to supplier selection, leveraging data-driven insights and technological advancements to optimize decision-making. By prioritizing strategic alignment, fostering collaborative partnerships, and implementing robust risk management strategies, organizations can navigate uncertainties, capitalize on market opportunities, and sustain competitive advantage.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".