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Record W2617402756 · doi:10.4236/ti.2017.82012

Religious Belief and Firm R&D Investment

2017· article· en· W2617402756 on OpenAlexvenueno aff
Hailipitimu Aibibula, Gege Wang, Chengcheng Zhang

Bibliographic record

VenueTechnology and Investment · 2017
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)Risk aversion (psychology)EconomicsOrder (exchange)MicroeconomicsMechanism (biology)Investment decisionsFinancial economicsFinanceLawExpected utility hypothesisBehavioral economicsPhilosophyEpistemologyPolitical science

Abstract

fetched live from OpenAlex

In order to explore the influencing mechanism of entrepreneur’s religious belief on firm R&D investment, this paper constructs a model on the basis of a game between an entrepreneur and researchers over the control right of R&D projects. We take researchers’ concern on free inquiry as an intermediate variable. We find that the researcher’s attention of free inquiry has a critical impact on the relationship between entrepreneur’s religious belief and firm R&D investment. Specifically, our results show that no matter how much the entrepreneur hates risk, the firm will increase R&D investment only when the researchers pay enough attention to free inquiry. On the contrary, if researchers do not attach any importance to the academic spirit which is “free inquiry”, corporate R&D investment is decreased with the enhancing of the degree of entrepreneur’s risk aversion, namely the entrepreneurs who are religious, risk-aversive may try to reduce the R&D investment.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2017
Admission routes1
Has abstractyes

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