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Record W2619733436 · doi:10.5539/ass.v13n6p113

Developing & Validating a Measure for PR Professionals’ Self-efficacy

2017· article· en· W2619733436 on OpenAlexvenueno aff
Ahmed Lawal Gusau, Zulhamri Abdullah, Ezhar Tamam, Nurul Ain Mohd Hasan

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationPsychologyExploratory factor analysisConfirmatory factor analysisNeglectScale (ratio)Discriminant validitySocial psychologySelf-efficacyMeasure (data warehouse)Construct validityExploratory researchApplied psychologyField (mathematics)Construct (python library)PsychometricsStructural equation modelingComputer scienceSociologyClinical psychologyStatisticsSocial scienceMathematics

Abstract

fetched live from OpenAlex

For the past three decades self-efficacy studies have been conducted in social science and other fields of academic endeavor. However, sufficient evidence has clearly shown that this research interest seems to neglect Public Relations (i.e. PR) discipline as there are hardly traceable works connected to this important field of study. This work therefore, represents an attempt to develop PR professionals’ self-efficacy scale to measure the ability of PR professionals in carrying out their duties. Exploratory factor analysis was conducted with PR experts and the result has shown a required factor loading for 23 out of total 24 items. Equally, the six operationalized dimensions were all consistent when confirmatory factor analysis was conducted. Similarly, discriminant and convergent validity tests which guarantees the instrument as valid for measuring Public Relations practitioners’ self-efficacy were also found to be fit.

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)0.029
metaresearch head score (Gemma)0.033
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 score0.029
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.343
Teacher spread0.284 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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