Growing Thanks to Whom? The Impact of Staff on Demand on Organizational Growth Dynamics: Evidence from Sweden
Bibliographic record
Abstract
Young fast-growing companies operating in the digital economy represent a tendency which has, so far, been little explored by academic literature that has, until now, not been able to form a systematic approach to this topic. Among the many factors that can help to explain their rapid evolutionary dynamics, some researchers (Ismail, 2013; Burke, 2015) underline the so-called use of Staff on Demand, that is to say freelance workers. In other words, freelance personnel. One usually thinks that not having stable relationships with the companies for which they work, contractors can help to streamline organizational processes and therefore favour faster growth. By means of a multiple case study, this paper will show that, contrary to belief, Staff on Demand represents an important but not crucial presence for fast-growing companies. The case study was carried out on a number of recently formed Swedish companies. The collection of data and the interviews with their founders, CEOs and HR Officers, clearly show that the most important role is still played by full time employees and that the presence of Staff on Demand does not affect in any substantial way the growth of a company or of Human Resource management.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".