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Record W4297364514 · doi:10.1108/jaoc-05-2022-0078

Investigating the effects of innovation intensity and lenders’ monitoring on the relation between financial slack and performance

2022· article· en· W4297364514 on OpenAlex
Johnny Jermias, Fatih YİĞİT

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Accounting & Organizational Change · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBusinessFinanceAccountingRelation (database)EconomicsComputer science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.209
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it