Geopolitical disruptions and supply chain structural ambidexterity
Bibliographic record
Abstract
Purpose This paper seeks insights into how multinational enterprises restructure their global supply chains to manage the uncertainty caused by geopolitical disruptions. To answer this question, we investigate three significant geopolitical disruptions: Brexit, the US-China trade war and the coronavirus disease 2019 (Covid-19) pandemic. Design/methodology/approach The study uses an inductive theory-elaboration approach to build on Organisational Learning Theory and Dunning’s eclectic paradigm of international production. Twenty-nine expert interviews were conducted with senior supply chain executives across 14 multinational manufacturing firms. The analysis is validated by triangulating secondary data sources, including standard operating procedures, annual reports and organisational protocols. Findings We find that, when faced with significant geopolitical disruptions, companies develop and deploy supply chain structural ambidexterity in different ways. Specifically, during Covid-19, the US-China trade war and Brexit, companies developed and deployed three distinct types of supply chain structural ambidexterity through (1) partitioning internal subunits, (2) reconfiguring supplier networks and (3) creating parallel supply chains. Originality/value The findings contribute to Dunning’s eclectic paradigm by explaining how organisational ambidexterity is extended beyond firm boundaries and embedded in supply chains to mitigate uncertainty and gain exploration and exploitation benefits. During significant geopolitical disruptions, we find that managers make decisions in tight timeframes. Therefore, based on the transition time available, we propose three types of supply chain structural ambidexterity. We conclude with a managerial framework to assist firms in developing supply chain structural ambidexterity in response to geopolitical disruptions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".