Investigating the effects of innovation intensity and lenders’ monitoring on the relation between financial slack and performance
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the moderating roles of innovation intensity and lenders’ monitoring on the relation between financial slack and performance. Design/methodology/approach This study adopts an empirical method using data from firms listed in both the Compustat S&P500 and Boardex for the period 2010 to 2019 to analyze the effects of innovation intensity and lenders’ monitoring on the relation between financial slack and performance. Findings The authors find that financial slack is positively related to performance, and this relation is stronger as innovation intensity increases. Furthermore, we demonstrate that lenders’ monitoring strengthens the positive relationship between financial slack and performance. Research limitations/implications First, this study focuses on the effects of financial slack, research and development (R&D) intensity and lenders’ monitoring on financial performance. Future research might extend this study by investigating the effects of these variables on non-financial performance. Second, the data and results do not provide insights into the reasons for firms to accumulate financial slack. Future research might conduct a longitudinal field study to understand why firms build financial slack. Finally, this study only uses R&D intensity and lenders’ monitoring as the moderating variables. Future studies might incorporate other contingency variables such as firms’ budgeting and budget-based compensation systems to provide useful insights into the relationship between financial slack and performance. Practical implications This study provides important insights into the value of financial slack for firms that invest heavily in R&D activities. This study also provides useful insight into the benefits of lenders’ monitoring to mitigate managers’ unethical behavior. Social implications This study provides useful insights for companies that invest heavily in innovation activities by showing that financial slack is beneficial for this company and lenders’ monitoring is needed to discipline managers in using the slack resources. Originality/value This study is the first to investigate the moderating effects of innovation intensity and lenders’ monitoring on the relation between financial slack and performance. Previous studies focus their investigations on the direct effect of financial slack and performance.
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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 | 0.032 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".