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Record W1996550076 · doi:10.4236/ti.2015.61007

Small and Medium Sized Entities Management’s Perspective on Principles-Based Accounting Standards on Lease Accounting

2015· article· en· W1996550076 on OpenAlexvenueno aff
Jierong Cheng

Bibliographic record

VenueTechnology and Investment · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingBusinessManagement accountingFinancial accountingAccounting information systemAccounting standardLeaseMark-to-market accountingFinance

Abstract

fetched live from OpenAlex

Lease accounting is viewed as one of the top priorities for the International Financial Reporting Standards (IFRS) convergence. Small and medium sized entities are an important part of the economy, and this research investigates the management’s perspective on the adoption of principles-based IFRS about lease accounting. This researcher interviewed four managers from three different small and medium sized entities, and found the management to be more concerned about their long-term business success than the change of accounting standards. Only when the entities have a loan with the bank, then the management focuses on the lease classification. The interview also suggests that the managers and business owners in the small and medium sized entities have limited knowledge and skills in accounting reporting standards. These firms outsource their accounting needs to local accountants rather than having their own in-house departments. The other aspect of focus for management of these firms is tax consequence of IFRS adoption. The research suggests other regulatory agencies, i.e., Internal Revenue Service, should also be involved in enhancing financial statement transparency and usefulness after the adoption of accounting standards.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.022
GPT teacher head0.234
Teacher spread0.213 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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