MétaCan
Menu
Back to cohort
Record W3201240449 · doi:10.1093/icc/dtab056

Corporate governance and R&D investment: the role of debt financing

2021· article· en· W3201240449 on OpenAlexaff
Hussain Muhammad, Stefania Migliori, Sana Mohsni

Bibliographic record

VenueIndustrial and Corporate Change · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsCorporate governanceDebtInvestment (military)FinanceBusinessCorporate financeInternal financingDebt financingDebt ratioExternal financingFinancial systemMonetary economicsEconomics

Abstract

fetched live from OpenAlex

Abstract This paper examines the role of debt financing in the relationship between corporate governance and research and development (R&D) investment using a sample of publicly traded U.S. pharmaceutical firms from 2009 to 2018. The results show a positive and significant association between corporate governance mechanisms (such as board size, board independence, board gender diversity, and ownership concentration) and R&D investment and a negative and significant association between debt financing and R&D investment. In addition, we show that debt financing plays a moderating role and a partial mediating role in the relationship between corporate governance mechanisms and R&D investment. Specifically, debt financing attenuates the negative effect of board size on R&D investment and accentuates the positive effect of ownership concentration on R&D investment. Our study helps to shed light on a close and complex relationship existing between the firm’s choices of corporate governance, debt financing, and R&D investments, which the previous literature has so far examined in a partial and fragmented way. To ensure effective R&D investment, firms need to consider the effect of debt financing on corporate governance decisions.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.136
GPT teacher head0.216
Teacher spread0.080 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial and Corporate ChangeSame topicCorporate Finance and GovernanceFrench-language works237,207