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Record W4281250958 · doi:10.3390/jrfm15050227

Linking Supply Chain Disruption Orientation to Supply Chain Resilience and Market Performance with the Stimulus–Organism–Response Model

2022· article· en· W4281250958 on OpenAlexvenueno aff
Aaron Rae Stephens, Minhyo Kang, Charles Arthur Robb

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainDynamismMarket orientationBusinessExtant taxonStimulus (psychology)Resilience (materials science)Industrial organizationSupply chain managementMarketingPsychologyCognitive psychologyBiology

Abstract

fetched live from OpenAlex

Since 2020, supply chain disruptions have emerged as an ever-present challenge. This research provides a glimpse into the organizational structures that develop supply chain resilience and market performance amid continuous supply chain disruptions. Utilizing psychosomatic variables and empirical modeling, a model was constructed through a review of extant literature and tested with PLS-SEM analysis. Uniquely, this research model is framed with the stimulus–organism–response model; thus, placing a firm within the context of a tumultuous environment where stimuli elicit responses from an organization that behaves as an organism. Results demonstrate that organizational culture plays a critical role in developing supply chain resilience amid supply chain dynamism. Market performance was also developed but only through supply chain resilience; supply chain disruption orientation alone did not improve market performance. Mediation effects highlight the importance of supply chain disruption orientation, a strategic orientation that cements an organization’s ability to develop supply chain resilience.

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)0.002
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 score0.542
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.203
Teacher spread0.198 · 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

Citations23
Published2022
Admission routes1
Has abstractyes

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