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Record W2805550879 · doi:10.5430/bmr.v7n2p49

Corporate Strategy, Governance Structure and Organization Performance: A Research Agenda

2018· article· en· W2805550879 on OpenAlexvenueno aff
Fatuma B. Omar, James M. Kilika

Bibliographic record

VenueBusiness and Management Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsBlueprintCorporate governanceUnderpinningRelation (database)Extant taxonKnowledge managementMultidisciplinary approachEmpirical researchBusinessProcess managementManagement scienceSociologyComputer scienceManagementEconomicsEpistemologySocial scienceEngineering

Abstract

fetched live from OpenAlex

Corporate strategy plays a critical role in the proper functioning of an organization as it provides the blueprint that guides the corporate direction of an organization while governance structure presents an organization with a framework for the distribution of responsibilities and resources to achieve organization performance. While the constructs have been sufficiently studied and documented in various studies separately in relation to organization performance, few studies have been undertaken to study the two constructs together to understand how they jointly explain organization performance. This paper presents a review of the extant theoretical and empirical literature on the two constructs in relation to organization performance. Relevant underpinning theories are reviewed, constructs described and their operational indicators identified and compared with empirical work and emergent knowledge gaps identified. The paper finally proposes a multidisciplinary based theoretical model suitable to address the gaps identified to advance knowledge in the area and calls upon future research to empirically test the propositions of the study.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0020.006
Scholarly communication0.0110.013
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.125
GPT teacher head0.345
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 designTheoretical or conceptual
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

Citations6
Published2018
Admission routes1
Has abstractyes

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