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Record W4283653217 · doi:10.3390/jrfm15070283

Driving SMEs’ Performance in South Africa: Investigating the Role of Performance Appraisal Practices and Managerial Competencies

2022· article· en· W4283653217 on OpenAlexvenueno aff
Nhamo Mashavira, Sevias Guvuriro, Crispen Chipunza

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsPerformance appraisalBusinessStructural equation modelingContext (archaeology)Small and medium-sized enterprisesConceptual modelConceptual frameworkKnowledge managementMarketingManagementEconomicsFinance

Abstract

fetched live from OpenAlex

Managerial competencies and performance appraisal practices are often ignored when considering the performance of Small-to-Medium Enterprises (SMEs). Yet, these competencies and practices are fundamental for the survival of the SMEs. SMEs are critical for economic growth and job creation in many economies. The current study sought to establish whether managerial competencies and performance appraisal practices correlate with SMEs’ performance in South Africa. Firm performance was measured using two variables: innovation and return on investment (ROI). The study adopted a structural equation modelling analytical approach. Interpersonal competencies were found to be a significant factor of managerial competencies, but conceptual and political competencies were not. The study also found that managerial competencies, and not performance appraisal practices, significantly correlated with both innovation and ROI. The study recommends that performance appraisal practices be tailored to suit SMEs in the developing countries context.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.479

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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