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Record W3126376519 · doi:10.4018/ijegr.2021010103

Investigating the Nature of Expectations and Its Influence on Attitudes Towards Malaysian Government E-Services

2021· article· en· W3126376519 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Electronic Government Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGovernment (linguistics)Situational ethicsStakeholderContext (archaeology)BusinessPublic relationsResource (disambiguation)Focus groupRelation (database)Knowledge managementConceptual modelArticulation (sociology)MarketingPsychologyPolitical scienceSocial psychologyComputer sciencePolitics

Abstract

fetched live from OpenAlex

This paper investigates the nature of expectations and its influence on attitudes towards government electronic services (e-services) in Malaysia. Based on a discussion of findings from in-depth focus group studies with government providers and users of e-services in Malaysia, a conceptual model is devised which explores both the extrinsic and intrinsic forces (in the form of e-government stimuli) influencing the articulation and actualization of stakeholder expectations, which can sway attitudes toward e-services. Key contributing factors (e.g., technological issues, managerial/institutional challenges, resource constraints, user needs), which have inhibited the extent of benefits realization when using e-services are explored. The model also introduces the concept of situational context—the importance of considering e-services in relation to its specific setting or circumstances at play.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.390
Teacher spread0.362 · 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