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Record W3040141282 · doi:10.3390/jrfm13070147

Influence of Organisational Culture on Supply Chain Resilience: A Power and Situational Strength Conceptual Perspective

2020· article· en· W3040141282 on OpenAlexvenueno aff
J. B. Whiteside, Samir Dani

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsSituational ethicsFlexibility (engineering)Supply chainPerspective (graphical)BusinessConceptual frameworkResilience (materials science)Process managementKnowledge managementPower (physics)Organizational cultureConceptual modelPsychologyMarketingComputer scienceSociologyPublic relationsSocial psychologyManagementPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.209
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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