A Comparison of Revenue Growth at Recent-IPO and Established Firms: The Influence of SG&A, R&D and COGS
Bibliographic record
Abstract
A dynamic view of the resource based theory (RBT) examines how a firm builds its resources over time, considering variations in resources' growth rates while the firm attempts to grow. Accordingly, we consider the elasticity of accumulated resources to assess conditions where these resources might serve as substitutes for rather than complements to COGS during periods of growth. We specify a production function that links aggregate resource allocation among SG&A, R&D and COGS expenses to a firm's revenue. This function yields a set of hypotheses on the elasticity of SG&A and R&D, and the productivity of COGS, while controlling for the revenue growth rate. We test these hypotheses on a dataset of 64 randomly selected firms that recently underwent an IPO, and a comparable set of 64 established public firms from four high-technology sectors. Results show that the accumulated stocks of resources can serve as substitutes for rather than complements to COGS, and the manner in which recent-IPO firms allocate and use resources differs from their established counterparts. We discuss the implications of associated elasticity and productivity results.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".