Navigating the paradox of global scaling
Bibliographic record
Abstract
Abstract Research summary Much global strategy research explores the management of competing strategic demands. Although these demands vary by a firm's context, the focus has been largely on established long‐lived multinational enterprises (MNEs) that are not based on digital technologies. There is thus a need to extend theory to take into account the co‐existence of rapid growth and digitization, a condition which is increasingly prominent. This study of globally scaling digital firms shows that they navigate the paradoxical demands of replication, to achieve frictionless rapid growth, and entrepreneurship, to innovate and remain competitive. We provide a theoretical model, which shows how MNEs navigate this global scaling paradox through a virtuous cycle of identifying innovations that can be replicated. Surprisingly, given the ease of modifying digital products and services, navigating the global scaling paradox involves minimizing local responsiveness, which is regarded as antithetical to replication. This research also builds insights on the global strategies of digital firms. Managerial summary Many digital firms strive to scale globally to achieve market dominance in competitive, fast‐paced industries, but only a few succeed. Studying software‐as‐a‐service firms that have successfully scaled globally, we illustrate that the core demands of global scaling are replication and entrepreneurship. Although contradictory, both demands need to be satisfied in tandem. Leaders of globally scaling firms can achieve this through a strategy that sustains three interrelated mechanisms: top‐down replication, bottom‐up entrepreneurial orientation, and replicable innovation generation to engender and screen replicable ideas. These mechanisms represent a virtuous cycle through which globally scaling digital firms can revise their global business model in a replicable way in order to sustain competitiveness.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".