The interplay between location and strategy in a turbulent age
Bibliographic record
Abstract
Abstract Research Summary The spread of protectionist policies and the COVID‐19 pandemic force policymakers and managers to fundamentally rethink the relationship between location and strategy. We examine this location‐strategy interplay through a structure‐agency perspective by investigating how the economic landscape shapes and, simultaneously, is shaped by firm strategies. Increasing spatial disparity and diversity of innovation and wealth in clusters and city‐regions create both tremendous challenges and opportunities for multinational enterprises to strategically leverage knowledge over space. Locational choices and actions of multinationals, in turn, affect regional economic development paths and geographies of innovation. We argue for deep dialogue and collaboration between economic geography, international business and strategy to untie the knots in the intricate interplay between location and strategy and solve the grand challenges in our turbulent age. Managerial Summary The wide spread of protectionism and the COVID‐19 pandemic have disrupted global value chains unprecedently, forcing policymakers and firm managers to rethink the relationship between business strategies and locations. We suggest that this relationship can be understood in a bilateral way. The concentration of innovation and economic activities in city‐regions and clusters creates big challenges but also tremendous opportunities for multinational enterprises. Multinationals need to direct knowledge across space but also have to deal with local resistance and opposition. The choices and actions of these firms are shaped by and, simultaneously, influence spatial patterns of economic activities. We argue for deep collaboration between economic geographers and international business scholars to solve the grand challenges for business, community and society in our turbulent time.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".