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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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