Bibliographic record
Abstract
Purpose – This paper aims to investigate two related questions on business research and development (R & D) simultaneously. First, does R & D create or resolve uncertainty? Second, does uncertainty encourage or discourage business R & D? Design/methodology/approach – This paper uses the three-stage least squares regression method and a system of simultaneous equations to examine the two research questions simultaneously. Instrumental variables overcome the econometric endogeneity problem. Findings – The results are consistent with the hypothesis that R & D creates rather than resolves uncertainty. Why then do risk-averse business managers undertake R & D? This paper argues that in creating uncertainty, R & D also creates “shadow options” for supplementary business investment not envisaged by business managers in the original objective for R & D. Rather, managers unexpectedly uncover shadow options in R & D’s inherent knowledge discovery process, which encourages business R & D in the first instance. Consistent with this real options interpretation, this paper reports evidence that volatility encourages R & D. Originality/value – This paper differs from the current literature in the sense that it investigates the two related R & D questions simultaneously rather than individually. The authors argue that the two related questions are inextricably interrelated, and investigating the two questions simultaneously would provide results that can possibly solve conflicting empirical results in the current literature. The results are also particularly useful for business managers who make decisions on whether to undertake R & D projects or not.
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 | 0.032 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".