MétaCan
Menu
Back to cohort
Record W2946002266 · doi:10.5430/ijfr.v10n3p153

Examining Enterprise Resource Planning Post Implementation and Employees’ Performance in Small and Medium Enterprises Using DeLone and McLean’s Information System Success Model

2019· article· en· W2946002266 on OpenAlexvenueno aff
Erlane K Ghani, Siti Aimi Mohamad Yasin, Mazurina Mohd Ali

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEnterprise resource planningBusinessKnowledge managementInformation qualityQuality (philosophy)Information systemService qualitySmall and medium-sized enterprisesResource (disambiguation)Process managementService (business)MarketingComputer scienceEngineering

Abstract

fetched live from OpenAlex

This study examines the effect of Enterprise Resource Planning (ERP) post implementation on employees’ performance in Small Medium Enterprises (SME) in Malaysia. Specifically, this study relies on the DeLone and McLean’s information system success model that proposes three quality dimensions namely, system quality, information quality and service quality in examining the effect of ERP post implementation on employees’ performance in SMEs. Using a questionnaire survey on 117 respondents that have experience in ERP, this study shows that out of the three qualities of ERP, system quality and service quality have a significant positive effect on employees’ performance in the SMEs. This study shows that information quality does not have a significant effect on employees’ performance in Malaysia. This study contributes to the information system literature and provides information regarding the quality dimensions which could help managers or ERP providers to assess the success of their ERP implementation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.364
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Financial ResearchSame topicERP Systems Implementation and ImpactFrench-language works237,207