Recognition and Measurement Obstacles of the Conceptual Framework of Financial Accounting Underlying E-commerce Business
Bibliographic record
Abstract
The remarkable growth in electronic commerce constitutes another new challenge for the accounting profession in its effort to meet the rapid and continuing revolution of information changes. Therefore, the objective of this study is to investigate the important obstacles facing corporations working in the business of E-commerce. This study also aims to investigate the production of accounting information as related to level three (recognition and measurements) of the conceptual framework underlying financial accounting. Therefore, to achieve the primary objectives of this study the researcher has developed a questionnaire that has been distributed to Jordanian external auditors. A total of 77 questionnaires were distributed; however, only 71 questionnaires were suitable for the analysis. A sample t- test was used to test the hypotheses of the study. The main results of the study revealed high arithmetic mean related to the obstacles of the accounting concepts (principles, assumptions, and constraints) at level three of the conceptual framework that underlies financial accounting. This requires attention in the preparation of the financial reports of a corporation operating in E-commerce Business. Moreover, the research concludes that the obstacles are connected, interdependent and interrelated with each other. Therefore, the accounting principle obstacles have implications over the application of accounting assumptions and constraints. Consequently, the researcher recommends the need to make changes in the concepts of recognition and measurements in the conceptual framework that underlies financial accounting. This is to ensure the qualitative characteristics of accounting information for E-commerce business corporations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".