The Influence of Organizational Culture on Procurement Practices: A Multi-industry Perspective
Bibliographic record
Abstract
This qualitative study investigates the influence of organizational culture on procurement practices across multiple industries, aiming to enrich understanding of how cultural dynamics shape decision-making, supplier relationships, and strategic outcomes within procurement departments. Through semi-structured interviews with procurement professionals, organizational leaders, and industry experts, data were gathered to explore the impact of leadership styles, ethical frameworks, industry dynamics, and external influences on procurement strategies. Findings reveal that transformational leadership fosters innovation, strategic alignment, and long-term partnerships in procurement, whereas transactional and autocratic styles may prioritize cost efficiency over broader strategic goals. Ethical cultures within organizations significantly influence supplier selection criteria and procurement decisions, highlighting the importance of integrating ethical standards consistently to mitigate risks and enhance organizational reputation. Industry-specific dynamics necessitate adaptive procurement strategies tailored to technological advancements, regulatory requirements, and market conditions. Effective supplier relationship management practices, emphasizing trust, transparency, and mutual value creation, enhance procurement effectiveness and resilience. Organizational structures, including centralized, decentralized, and hybrid models, play crucial roles in optimizing procurement operations. Learning cultures promoting continuous improvement and knowledge sharing foster innovation and resilience in procurement practices. External factors such as market volatility, regulatory changes, and geopolitical risks underscore the need for adaptive procurement strategies and robust risk management practices. This study contributes to theoretical insights and practical implications for aligning organizational culture with strategic procurement objectives to optimize effectiveness, mitigate risks, and drive sustainable value creation across supply chains.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".