The nonlinear relationship between financial constraints and R&D investment: the mediating role of executive stock options
Bibliographic record
Abstract
Purpose The current study aims to investigate the mediating role of executive stock options in the nonlinear relationship between financial constraints and research and development (R&D) investment through two measures of financial constraints. Design/methodology/approach This study is based on a sample of 90 French firms for the period extending from 2008 to 2020. The authors employ a panel threshold method to analyze whether the impact of financial constraints on R&D investment depends on the level of financial constraints or not. Findings Using SA index (Hadlock and Pierce, 2010) and FCP index (Schauer et al., 2019) as measures of financial constraints, the authors demonstrate that the relationship between financial constraints and R&D investment is nonlinear. Moreover, the authors find that executive stock options mediate partially the relationship between financial constraints and R&D investment. More specifically, the authors show that stock options could play two roles depending on the level of the financial constraints; inconsistent mediation for firms with low/medium level of financial constraints and partial mediation for highly constrained firms. Originality/value This paper is the first to the best of the authors' knowledge to investigate the nonlinear relationship between financial constraints and R&D investment as well as the mediating role of executive stock option using dynamic panel threshold models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".