Organizational scaling, scalability, and scale-up: Definitional harmonization and a research agenda
Bibliographic record
Abstract
The concepts of ‘scaling,’ ‘scalability,’ and ‘scale-up’ are increasingly used in business research and practice. However, the literature reveals a range of definitions for each, and often, their meanings are only implied. This diminishes the ability to build cumulative and meaningful insight - and conduct research - on each concept. In this editorial, we offer a systematic review that assesses and harmonizes prior definitions of these important concepts. This allows us to define and differentiate between (a) scaling as an organizational process, (b) scalability as an ordinary organizational capability, and (c) scale-up as a phase of organizational development. Complementing and extending existing scholarly work, we develop a rich agenda for scaling-related research in entrepreneurship.
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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 | 0.157 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.024 | 0.056 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".