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Record W3173619021 · doi:10.5267/j.uscm.2021.4.002

The role of perceived usefulness in moderating the relationship between the DeLone and McLean model and user satisfaction

2021· article· en· W3173619021 on OpenAlexvenueno aff
Muhartini Salim, Lizar Alfansi, Anggarawati Sularsih, Fachri Eka Saputra, Chairil Afandy

Bibliographic record

VenueUncertain Supply Chain Management · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
FundersUniversitas Bengkulu
KeywordsService qualityInformation qualityQuality (philosophy)Context (archaeology)Information systemCustomer satisfactionKnowledge managementPsychologyUser satisfactionModerationComputer scienceService (business)Applied psychologyMarketingBusinessSocial psychologyEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

The aim of this paper is to examine one of the most crucial factors in the “Technology Acceptance Model” proposed by Davis (perceived usefulness) in moderating the “DeLone and McLean” success model in the context of educational portal in Higher Education. Questionnaires were distributed online to 200 respondents and deserved to be analyzed. The respondents were regular students at the University of Bengkulu. Data analysis used Smart-PLS version 3.2.9. The research findings indicated an influence of “system quality, information quality, and service quality partially on user satisfaction” of the educational portal information systems. The result shows that perceived usefulness can strengthen the relationship between system quality, information quality, and service quality to the satisfaction of customer. This research contributes to the development of perceived usefulness variable as a moderating variable affecting the quality of a system, quality of information, and quality of service partially on user satisfaction and finding strategies needed by the University of Bengkulu effective and efficient information system.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.547

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 studies0.0010.000
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 head0.081
GPT teacher head0.329
Teacher spread0.248 · 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

Citations32
Published2021
Admission routes1
Has abstractyes

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