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Record W4385356280 · doi:10.34010/agregasi.v10i1.5670

KESUKSESAN APLIKASI PELAPORAN KEUANGAN DAN KINERJA ORGANISASI OPD KOTA/KABUPATEN DI PROVINSI RIAU

2022· article· en· W4385356280 on OpenAlexaff
Ursula Gunasanti, Ruhul Fitrios, Al Azhar L, Ismon Zakya

Bibliographic record

VenueJurnal Agregasi Aksi Reformasi Government dalam Demokrasi · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy and Behavior
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsOrganizational commitmentStratified samplingSample (material)Organizational performanceOrganizational cultureBusinessPopulationOrganizational behavior and human resourcesOrganizational learningOrganizational safetyKnowledge managementBusiness administrationOperations managementPsychologyMarketingManagementComputer scienceOrganizational engineeringStatisticsSocial psychologyEngineeringSociologyMathematicsEconomics

Abstract

fetched live from OpenAlex

This study aims to examine the effect of organizational culture and organizational commitment on the success of financial reporting/accounting information systems (AIS) applications and their effect on organizational performance. The population of research is the City/Regency Regional Apparatus Organization (OPD) in the North Coastal Region of Riau Province. The sample selection used proportional stratified random sampling with a total sample of 104 OPD. The data analysis used is Smart PLS. The results show that organizational culture affects AIS applications success and organizational performance, while organizational commitment does not affect AIS applications success and organizational performance, and AIS applications success affects organizational performance. This study indicated the importance of developing a strong organizational culture to increase the success of AIS applications and improve organizational performance. Furthermore, the use of AIS applications can improve organizational performance.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.004
Open science0.0020.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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