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Record W2884806180 · doi:10.17722/ijrbt.v10i3.506

Information System and Service Quality: An Empirical Study of Their Impact on End-Users Satisfaction ERP Systems

2018· article· en· W2884806180 on OpenAlexvenueno aff
Andreas Andreas, Enni Savitri

Bibliographic record

VenueInternational Journal of Research in Business and Technology · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsService qualityInformation systemInformation qualityBusinessQuality (philosophy)Empirical researchUser satisfactionComputer scienceService (business)End userProcess managementKnowledge managementMarketingWorld Wide WebHuman–computer interactionEngineeringStatistics

Abstract

fetched live from OpenAlex

ERP systems are integrated information systems that can be applied in both business and non-business organizations. For business organizations it covers the entire functional enterprise that includes accounting and finance, production, sales, purchasing, personnel and other functions. These functions are separated by software modules and interconnected with the integrated data center. Implementation of ERP systems does not always provide satisfaction for end-users. This paper examines the quality of information systems and service that impact on end-user satisfaction, specifically banking companies located in Pekanbaru, Indonesia. Data analysis results reveal that the information systems and service quality partially affect end-users’ satisfaction with ERP systems and thus these findings, remind the designers of ERP systems to improve the quality  information systems and the availability of user friendly service.

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)0.001
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 score0.042
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.214
GPT teacher head0.519
Teacher spread0.305 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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