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Record W3213034770 · doi:10.5267/j.ijdns.2021.9.010

The role of e-purchasing in government procurement fraud reduction through expanding market access

2021· article· en· W3213034770 on OpenAlexvenueno aff
Femilia Zahra, Muhammad Ikbal Abdullah, Muhammad Din, Harifuddin Thahir, Harun Harun, Jati Kasuma Ali

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingProcurementBusinessIndonesianGovernment (linguistics)Government procurementService (business)MarketingStructural equation modelingComputer science

Abstract

fetched live from OpenAlex

This study aims to analyze the effect of e-purchasing implementation on the reduction of fraud in government procurements in Indonesia. This study also analyzes the role of market access in mediating the effect of e-purchasing implementation on government procurement fraud. The study was conducted in all Procurement Service Units (ULP) of cities and districts in Indonesia. The questionnaires were sent electronically to 520 ULPs, but only 120 respondents could be used in this study. In analyzing data, the Structural Equation Modelling (SEM) was used with the support of the program Partial Least Square (WarpPLS 7.0) to examine the relationship between variables studied. The results show that the implementation of e-purchasing directly reduces the level of fraud in government procurements in Indonesia. Other findings of the study also indicate that the implementation of e-purchasing expands market access. The rise of market access in implementing e-purchasing will affect the level of frauds relating to procurement practices in the Indonesian government.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.144

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.070
GPT teacher head0.325
Teacher spread0.255 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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