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Record W2584910546 · doi:10.5539/ijef.v9n3p11

Impact of the Implementation of E-Accounting in Mexico

2017· article· en· W2584910546 on OpenAlexvenueno aff
Roberto Rodríguez Venegas, Rafael Espinoza Mosqueda

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
FundersUniversidad de Guanajuato
KeywordsAccountingRevenueFund accountingAccounting information systemBusinessProcess (computing)Management accountingVariable (mathematics)Balance sheetAccounting standardPositive accountingVariablesBalance (ability)Financial accountingComputer science

Abstract

fetched live from OpenAlex

In Mexico in 2015, a new fiscal requirement emerged called electronic accounting (e-Accounting), which consists of sending specified documents to the tax authority that form part of corporate accounting, this new requirement puts the country at the forefront of electronic accounting world-wide since the purpose is to increase tax revenue. The purpose of this study was to describe and analyze the adoption and implementation of electronic accounting by companies in Mexico and their contribution is to identify the factors that contribute to the success of the process. We applied a questionnaire to 94 companies selected at random, which correspond to individuals and corporations that had to comply with e-Accounting requirements in 2015 and 2016, where we analyzed some variables that were considered fundamental: training and specialization of the CEO, training of accounting staff, the state of computer use and technical conditions for being able to keep electronic accounting. We performed a regression analysis with 12 independent variables where the results show that the integration of balance sheet items is relevant for the process of adoption and implementation of electronic accounting (dependent variable).

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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