Firm Strategy and Outcome Uncertainty in R&D Firms
Bibliographic record
Abstract
We evaluate the impact of firm strategy on the variability of future performance for R&D firms and how firm strategy mediates the relation between R&D expenditures and firm outcome uncertainty of sales revenue, earnings, and operating cash flows. Following prior literature, we run exploratory factor analysis using resource allocations in the past towards intended strategy to measure the realized strategy pursued by firms. We find that, in R&D firms, differentiation strategy leads to lower variability of future sales, earnings, and operating cash flows. In contrast, cost leadership strategy leads to higher variability of future sales and operating cash flows, and lower variability of future earnings. Our study is the first, to our knowledge, to empirically document the impact of firm strategy of R&D firms on the variability of various future performance measures. Using mediation analysis, we further document that differentiation strategy negatively mediates the association between R&D expenditures and variability of sales revenue, earnings, and operating cash flows. While cost leadership strategy negatively mediates the association between R&D expenditures and variability of sales and variability cash flows, it positively mediates the association between R&D expenditures and variability of earnings.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".