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Record W3119220274 · doi:10.5267/j.msl.2020.12.002

The effect of soft skills, ethics, and value on the willingness of employers to continue recruiting UMT graduates

2021· article· en· W3119220274 on OpenAlexvenueno aff
Abdul Hafaz Ngah, Nurul Izni Kamalrulzaman, Fauzayani Ibrahim, Noor Azuan Abu Osman, Nur Asma Ariffin

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSoft skillsValue (mathematics)Structural equation modelingPsychologyBusinessMarketingMedical educationSocial psychologyMathematicsMedicineStatistics

Abstract

fetched live from OpenAlex

This study aims to investigate the effect of soft skills and ethics and value on the employers’ willingness to continue recruiting Universiti Malaysia Terengganu (UMT) graduates, together with the moderating effect of knowledge on the relationship between soft skills and the employers’ willingness to continue recruiting UMT graduates. The study’s respondents comprised of 208 employers in Malaysia who responded through an online survey using Google Forms. The survey data was then analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM), indicating that soft skills positively affected the employers’ willingness to continue recruiting UMT graduates. Nevertheless, ethics and value were found to be insignificant factors on the employers’ willingness to continue recruiting UMT graduates. It was also revealed that knowledge had the moderating effect on the relationship between soft skills and the employers’ willingness to continue recruiting UMT graduates. Therefore, universities were recommended to invest in soft skills and knowledge education to ensure that graduates met the employers’ professional recruitment standards in areas of expertise.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.336
Teacher spread0.309 · 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.

Study designQualitative
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
Published2021
Admission routes1
Has abstractyes

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