Locational strategy: Understanding location in economic geography and corporate strategy
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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