The resurgence of nationalism and its implications for supply chain risk management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper examines nationalism as a driver of political risk and how it can lead to supply chain disruptions for foreign multinational enterprises (MNEs). Design/methodology/approach Conceptual research based on a review of the literature on nationalism and supply chain risk management. Findings This research unveils how economic nationalism could engender supply chain disruptions via discriminatory practices toward all foreign MNEs and how national animosity may generate additional risks for the MNEs of nations in conflict with one another. These discriminatory practices include an array of host government and grassroots actions targeting foreign MNEs. While economic nationalism and national animosity emanate from within a host country, they may stimulate geopolitical crises outside the host country and thereby affect the international supply chains of foreign MNEs. Research limitations/implications This research lays the foundation for analytical and empirical researchers to integrate key elements of nationalism into their studies and recommends propositions and datasets to study these notions. Practical implications This study shows the implications that nationalist drivers of supply chain disruptions have for foreign MNEs and thus can help managers to proactively mitigate such disruptions. Originality/value This study reveals the importance of integrating notions of national identity and national history in supply chain research, since they play a key role in the emergence of policies and events responsible for supply chain disruptions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it