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Record W2151996496 · doi:10.5539/ass.v10n3p253

Managerial Factors and Management Conflict in Venture Capital Financing in Malaysia

2014· article· en· W2151996496 on OpenAlexvenueno aff
Hisham Bin Mohammad, Mohd Sobri Minai, Esuh Ossai-Igwe Lucky

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsVenture capitalSocial venture capitalBusinessKuala lumpurConflict managementCapital (architecture)QuestionnaireInvestment (military)StructuringFinanceMarketing

Abstract

fetched live from OpenAlex

The warm venture cooperation built between venture capitalists and entrepreneurs may still be interrupted by the management's conflicts occurred due to various managerial factors. As a result, this study investigates the management conflict in venture capital investments. A cross-sectional study of questionnaire survey research design was conducted in this respect. Questionnaire data was generated from 35 Malaysian venture capital companies located in Kuala Lumpur and Selangor. The questionnaires were distributed through the mailing procedure. Overall, the findings indicate that the managerial factors significantly influence the management conflict. Further results show that managerial factors which consist of Deal Origination and Screening (DOS), Evaluating Venture Proposal (EVP), Contracting and Deal Structuring (CDS), Monitoring and Post Investment Activities (MPI) and Risk Management (RM) significantly influence the formation of management conflict in venture cooperation. Based on the findings, it is inferred that managerial factors does influence the occurrence of management conflict in venture cooperation. Thus, the study recommends that Malaysian venture capitalists give consideration to the managerial factors in reducing or curbing the possibility of conflict to occur.

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)0.001
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 score0.866
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.229
Teacher spread0.217 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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