Locational strategy: Understanding location in economic geography and corporate strategy
Bibliographic record
Abstract
Research Summary Drawing on key concepts from management theory, corporate strategy, and economic geography, we argue that the time has come for “Locational Strategy.” Locational strategy is a framework for understanding how the locational decisions of organizations fit into broader corporate strategy. Locational strategy is particularly relevant given rise of knowledge and talent as key factors of productions and the fact that these inputs are so clustered in space. We lay out several principles to guide further work in this area, and briefly anticipate the role for locational strategy in the post‐pandemic economy. Such an approach is well suited to the study of the sprawling modern firm, the footloose geography of talent, and the hyper‐competitive field of regional economic policy. Managerial Summary Management needs to consider locational strategy as a key element of broader corporate strategy. This is because location and firm location decisions are ever more central to firm strategy. We review key ideas from the academic literature that bear on how managers can get the best access to talent, knowledge, and customers. Access to talent and embeddedness in complex knowledge systems is a defining feature of Locational strategy over and above simple input cost concerns. Furthermore, firms need to consider the actions and reactions of jurisdictions as they decide how to locate and deploy resources across in places across the world. Management training typically does not feature the geographic considerations of location strategy. The authors have refined their approach while teaching students in their course on The City and Business in the MBA program at the University of Toronto's Rotman School.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".