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Record W4402843203 · doi:10.5267/j.dsl.2024.8.006

Comparative study of the impact of information literacy, digital literacy and media literacy on employability between Indonesia and Malaysia

2024· article· en· W4402843203 on OpenAlexvenueno aff
David Sukardi Kodrat, Damelina Basauli Tambunan, Wendra Hartono, Phuah Kit Tengb, Chow Poh Ling

Bibliographic record

VenueDecision Science Letters · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsEmployabilityLiteracyInformation literacyMedia literacyDigital literacyMathematics educationSociologyPedagogyPsychology

Abstract

fetched live from OpenAlex

The purpose of this research is to explore the relationships between information literacy, digital literacy, media literacy, Attitude toward use, and employability to examine the role of information and digital literacy in influencing employees' intentions to use technology in the workplace. The research sample for Indonesia was 250 respondents and for Malaysia there were 298 respondents. The data collection method uses Google Forms, distributed to respondents through purposive random sampling technique. The research results indicate that in Indonesia, Computer Literacy (CL) on Perceived Usefulness (PU), PU on Attitude towards Use (ATT), and ATT on Employability (EMP) have a big influence. On the other hand, Information Literacy (IL) and CL have a small influence on Employability. Likewise, Perceived Ease of Use (PEOU) has little influence on ATT. Malaysia, which has a big influence is Digital Literacy (DL) on Employability (EMP) and Perceived Ease of Use (PEOU). However, DL has a small influence on PU, as does IL on EMP, PEOU, and PU. Likewise, the influence of PEOU on PU is small.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0010.004
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 head0.031
GPT teacher head0.407
Teacher spread0.375 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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