MétaCan
Menu
Back to cohort
Record W3113518138 · doi:10.5430/ijhe.v10n3p46

The Relationship between Strategic Capabilities and Academic Performance: An Empirical Evidence from Sudan

2020· article· en· W3113518138 on OpenAlexvenueno aff
Nahla El Sheikh Hagoug, Yousif Abdelbagi Abdalla

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Empirical researchStrategic planningCluster samplingCluster (spacecraft)Body of knowledgeField (mathematics)Knowledge managementBusinessEmpirical evidenceRegression analysisPsychologyMarketingSociologyPopulationComputer scienceGeographyStatistics

Abstract

fetched live from OpenAlex

The objective of this study is to examine the role of strategic capabilities in achieving academic performance in Sudanese private universities. Based on a literature search, an accurate questionnaire was used to collect the needed data.198 questionnaires were collected from Sudanese private universities using the two-stage cluster sampling. For data analysis, multiple regression model was conducted. The findings indicated that the constructs of strategic capabilities including human resources and physical resources are significantly and positively associated with performance. The research concluded that strategic capabilities are the factors of achieving academic performance in private universities in Sudanese context. Moreover, there are a few studies in such field, and there are only few empirical studies that have examined resources’ characteristics. This research will expand the body of knowledge of both scholars and practitioners in the area of strategic responses among private universities establishments, as well as identify areas that could be further studied.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.159
GPT teacher head0.362
Teacher spread0.202 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicIntellectual Capital and Performance AnalysisFrench-language works237,207