Influence of Organisational Culture on Supply Chain Resilience: A Power and Situational Strength Conceptual Perspective
Bibliographic record
Abstract
The purpose of this paper is to explore how organisational culture, represented by the competing values framework (CVF), and the relationship mechanisms of situational strength and power influence an organisation’s approach to supply chain resilience (SCRES). This is a conceptual paper which uses a multi-theoretical approach to create a framework outlining how organisations which possess different characteristics of culture within the CVF will work to achieve SCRES. Secondary analysis of four case examples as discussed in the supply chain and resilience literature are then used to support the development of propositions from this framework in more detail. The paper suggests that ‘flexibility focused’ cultures will create weaker situational strengths for supply chain partners when managing disruptions, while ‘stability focused’ cultures will create stronger situational strengths in the same scenarios. ‘Internally focused’ cultures may use coercive power with supply chain partners when managing disruptions, while ‘externally focused’ cultures will prefer non-coercive power in the same scenarios. The four case studies from the literature highlight that each type of culture within the CVF can enable an organisation to achieve SCRES. The practical implications of the findings are that managers should take into consideration how their organisation’s culture will influence their relationships with supply chain partners, depending on their application of power and situational strength. However, future research is required to empirically test the propositions.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".